├── Array_Thinning_With_Natural_Selection_Genetic_Algorithm.m ├── HFSS_pattern_import.m ├── Initial_ULA.m ├── LICENSE ├── PSO_on_Linear_Array_For_Null.m ├── PSO_on_Linear_Array_For_SLL.m ├── Pattern_Visualization.m ├── Plot_Circular_Array_Response.m ├── Plot_Circular_Array_With_Element_Response.m ├── Plot_Linear_Array_Response.m ├── Plot_Linear_Array_With_Element_Response.m ├── Plot_Planar_Array_Response.m ├── Plot_Planar_Array_With_Element_Response.m ├── README.md ├── SLC_on_Linear_Array_For_Null.m ├── Test_Array ├── Far_Field_Data │ └── 5ELx2 FarField Tx Only.mat ├── Near_Field_Data │ └── 5ELx2 1meter NearField Tx Only.mat ├── PSO_Cancellation.m ├── Power_Point │ └── 2x5 Element Array NF Datas.pptx ├── ReadTouchstone.m └── S_Parameters │ ├── 5ELx2 NearField 1 to 6G.s10p │ ├── 5ELx2 NearField 20 to 500M.s10p │ └── 5ELx2 NearField 500M to 1G.s10p ├── Uniform_Circular_Array.m ├── Uniform_Linear_Array.m ├── Uniform_Planar_Array.m ├── generate_HFSS_field_vectors.m └── generate_phi_theta.m /Array_Thinning_With_Natural_Selection_Genetic_Algorithm.m: -------------------------------------------------------------------------------- 1 | clear all 2 | clc 3 | 4 | freq_range = [24e9 28e9]; % [start_frequency stop_frequency] [Hz] 5 | freq_points = 101; % No. of frequency points 6 | d = 0.005353; % Element spacing [m] 7 | N_elements_per_side = 10; 8 | weights = ones(N_elements_per_side, N_elements_per_side); % Amplitude weights for N elements from 0 to 1 9 | 10 | freq_step = (freq_range(2) - freq_range(1)) / (freq_points - 1); 11 | frequency_vector = freq_range(1):freq_step:freq_range(2); 12 | theta_vector_degrees = -90:1:90; 13 | theta_vector_rads = theta_vector_degrees * (pi / 180); 14 | 15 | phi_val = 0; 16 | for n = 1:1:length(frequency_vector) 17 | frequency = frequency_vector(n); 18 | 19 | % create omnidirectional characteristic 20 | iPattern = zeros(1, length(theta_vector_degrees)); 21 | 22 | % Calculate Array Factor 23 | for nn = 1:1:length(theta_vector_rads) 24 | [AF, AF_dB, AV] = Uniform_Planar_Array(phi_val*(pi / 180), theta_vector_rads(nn), frequency, d, weights); 25 | weight_vs_angle{n}{nn} = {theta_vector_rads(nn), AV}; 26 | %Combine for full characteristics 27 | F(n, nn) = AF_dB + iPattern(nn); 28 | end 29 | end 30 | 31 | figure(2); 32 | plot(theta_vector_degrees, F(end,:)); 33 | hold on 34 | 35 | pel = findpeaks(F(end, :), theta_vector_degrees, 'NPeaks', 2, 'SortStr', 'descend'); 36 | sllopt = pel(1) - pel(2); 37 | 38 | % Array Thining using the Natural Selection Genetic Algorithm 39 | % References 40 | % Randy L. Haupt, Thinned Arrays Using Genetic Algorithms, 41 | % IEEE Transactions on Antennas and Propagation, Vol 42, No 7, 1994 42 | % Randy L. Haupt, An Introduction to Genetic Algorithms for Electromagnetics, 43 | % IEEE antennas and Propagation Magazine, Vol 37, No 2, 1995 44 | % Harry L. Van Trees, Optimum Array Processing, Wiley-Interscience, 2002 45 | 46 | 47 | % Set random number generator for repeatibility 48 | rng_state = rng(2013, 'twister'); 49 | 50 | % Set active / inactive weight matrix for only 1/4 of the array since it is 51 | % symmetric about both axes using uniform distribution 52 | rows = N_elements_per_side / 2; 53 | columns = N_elements_per_side / 2; 54 | candidates_per_generation = 200; 55 | w0 = double(rand(rows, columns, candidates_per_generation, 'double') > 0.5); 56 | % Set the inner N_elements_per_side / 4 elements to active and the outer 57 | % ones to random activity, this is so that we have a decent generation to 58 | % start with 59 | w0(1:idivide(rows, int32(2)), 1:idivide(columns , int32(2)), :) = 1; 60 | 61 | %Single Iteration Example 62 | % Pick one random candidate, plot the beam pattern, and compute the sidelobe 63 | % level 64 | wtemp = w0(:, :, randi(candidates_per_generation)); 65 | weights = [fliplr(flipud(wtemp)) flipud(wtemp); fliplr(wtemp) wtemp]; 66 | 67 | for n = 1:1:length(frequency_vector) 68 | frequency = frequency_vector(n); 69 | 70 | % create omnidirectional characteristic 71 | iPattern = zeros(1, length(theta_vector_degrees)); 72 | 73 | % Calculate Array Factor 74 | for nn = 1:1:length(theta_vector_rads) 75 | [AF, AF_dB, AV] = Uniform_Planar_Array(phi_val*(pi / 180), theta_vector_rads(nn), frequency, d, weights); 76 | weight_vs_angle{n}{nn} = {theta_vector_rads(nn), AV}; 77 | %Combine for full characteristics 78 | F(n, nn) = AF_dB + iPattern(nn); 79 | end 80 | end 81 | 82 | figure(2); 83 | plot(theta_vector_degrees, F(end,:)); 84 | 85 | pel = findpeaks(F(end, :), theta_vector_degrees, 'NPeaks', 2, 'SortStr', 'descend'); 86 | sllopt = pel(1) - pel(2); 87 | 88 | fillrate = (sum(weights(:)) / power(N_elements_per_side, 2)) * 100; %fill percent 89 | 90 | %Actual implementation use 91 | clear sllopt; 92 | generations = 2; 93 | for iteration = 1:1:generations 94 | %Calculate the sidelobe level for each candidate from the current 95 | %generation 96 | for candidate = 1:1:candidates_per_generation 97 | % Pick one random candidate, plot the beam pattern, and compute the sidelobe 98 | % level 99 | wtemp = w0(:, :, candidate); 100 | weights = [fliplr(flipud(wtemp)) flipud(wtemp); fliplr(wtemp) wtemp]; 101 | 102 | for n = 1:1:length(frequency_vector) 103 | frequency = frequency_vector(n); 104 | 105 | % create omnidirectional characteristic 106 | iPattern = zeros(1, length(theta_vector_degrees)); 107 | 108 | % Calculate Array Factor 109 | for nn = 1:1:length(theta_vector_rads) 110 | [AF, AF_dB, AV] = Uniform_Planar_Array(phi_val*(pi / 180), theta_vector_rads(nn), frequency, d, weights); 111 | weight_vs_angle{n}{nn} = {theta_vector_rads(nn), AV}; 112 | %Combine for full characteristics 113 | F(n, nn) = AF_dB + iPattern(nn); 114 | end 115 | end 116 | 117 | pel = findpeaks(F(end, :), theta_vector_degrees, 'NPeaks', 2, 'SortStr', 'descend'); 118 | sllopt(candidate) = pel(1) - pel(2); 119 | end 120 | 121 | %Now sort the performance of all candidates 122 | [~, idx] = sort(sllopt, 'descend'); 123 | 124 | %Now remove the half of the current generation that has the worse 125 | %performance. 126 | w0 = w0(:, :, idx(1:1:(candidates_per_generation / 2))); 127 | 128 | %Finally perform random mutation of candidates that performed best and 129 | %add create new generation. All this does is take the best performes 130 | %and perform a random mutation and add those to the new generation. 131 | %First it takes all weights from the row mutation start idx onward and does a 132 | %flipud therby mutating the rows and then takes all rows from the column 133 | %mutation start idx and does a fliplr therby mutating the columns 134 | row_mutation_start_idx = randi(rows); 135 | column_mutation_start_idx = randi(columns); 136 | w0(row_mutation_start_idx:1:end , :, ((candidates_per_generation / 2) + 1):1:candidates_per_generation) ... 137 | = flipud(w0(row_mutation_start_idx:1:end , :, 1:1:(candidates_per_generation / 2))); 138 | w0(column_mutation_start_idx:1:end , :, ((candidates_per_generation / 2) + 1):1:candidates_per_generation) ... 139 | = fliplr(w0(column_mutation_start_idx:1:end , :, 1:1:(candidates_per_generation / 2))); 140 | end 141 | 142 | wtemp = w0(:, :, 1); 143 | optimum_weights = [fliplr(flipud(wtemp)) flipud(wtemp); fliplr(wtemp) wtemp]; 144 | fillrate = (sum(optimum_weights(:)) / power(N_elements_per_side, 2)) * 100; %fill percent 145 | 146 | rng(rng_state); 147 | 148 | for n = 1:1:length(frequency_vector) 149 | frequency = frequency_vector(n); 150 | 151 | % create omnidirectional characteristic 152 | iPattern = zeros(1, length(theta_vector_degrees)); 153 | 154 | % Calculate Array Factor 155 | for nn = 1:1:length(theta_vector_rads) 156 | [AF, AF_dB, AV] = Uniform_Planar_Array(phi_val*(pi / 180), theta_vector_rads(nn), frequency, d, optimum_weights); 157 | weight_vs_angle{n}{nn} = {theta_vector_rads(nn), AV}; 158 | %Combine for full characteristics 159 | F(n, nn) = AF_dB + iPattern(nn); 160 | end 161 | end 162 | 163 | figure(2); 164 | plot(theta_vector_degrees, F(end,:)); 165 | 166 | pel = findpeaks(F(end, :), theta_vector_degrees, 'NPeaks', 2, 'SortStr', 'descend'); 167 | sllopt = sllopt(idx(1)); 168 | 169 | fillrate = (sum(optimum_weights(:)) / power(N_elements_per_side, 2)) * 100; %fill percent -------------------------------------------------------------------------------- /HFSS_pattern_import.m: -------------------------------------------------------------------------------- 1 | function [pattern_phitheta, phi, theta] = HFSS_pattern_import(relative_filename) 2 | 3 | patternData = csvread(relative_filename); % import csv 4 | 5 | % Extract phi/theta values from custom pattern 6 | 7 | chktheta = (patternData(:,2) == patternData(1,2)); 8 | blockLen = length(chktheta(chktheta~=0)); 9 | nCols = size(patternData,1)/blockLen; 10 | thetaBlocks = reshape(patternData(:,2),blockLen,nCols); 11 | phiBlocks = reshape(patternData(:,1),blockLen,nCols); 12 | 13 | theta = thetaBlocks(1,:); 14 | phi = phiBlocks(:,1).'; 15 | 16 | pattern_phitheta = reshape(patternData(:,3),blockLen,nCols).'; 17 | -------------------------------------------------------------------------------- /Initial_ULA.m: -------------------------------------------------------------------------------- 1 | clear all 2 | clc 3 | 4 | relative_filename = './Radiation_Patterns/DF1_3GHz_Gain_3D_pt.csv'; 5 | 6 | %Import the custom pattern (can add phase too if you want eve though it isn't in there now) 7 | %Input columns need to be phi, theta, pattern_dB pattern_phase 8 | [pattern_phitheta, phi, theta] = HFSS_pattern_import(relative_filename); 9 | 10 | %converts the pattern from phi - theta coordinates to az-el coordinates. 11 | [pattern_azel,az,el] = phitheta2azelpat(pattern_phitheta, phi, theta); 12 | 13 | 14 | freqVector = [2.75 3.25].*1e9; % Frequency range for element pattern 15 | antenna = phased.CustomAntennaElement('FrequencyVector', freqVector, ... 16 | 'AzimuthAngles', az, ... 17 | 'ElevationAngles', el, ... 18 | 'MagnitudePattern', pattern_azel, ... 19 | 'PhasePattern', zeros(size(pattern_azel))); 20 | 21 | fmax = freqVector(end); 22 | pattern(antenna,fmax,'Type','powerdb') 23 | view(90,0) 24 | 25 | 26 | c = 299792458; 27 | lambda = c / fmax; 28 | array = phased.UCA('Element',antenna,'NumElements',4,'Radius',lambda) 29 | 30 | pattern(array,fmax,'PropagationSpeed',c,'Type','powerdb',... 31 | 'CoordinateSystem','UV'); 32 | 33 | pattern(array,fmax,-1:0.01:1,0,'PropagationSpeed',c,'Type','powerdb', ... 34 | 'CoordinateSystem','UV') 35 | axis([-1 1 -50 0]); 36 | 37 | 38 | 39 | f0 = 3e9; 40 | scanPhi = -30:30; 41 | 42 | steeringvec = phased.SteeringVector('SensorArray',array,... 43 | 'PropagationSpeed',c); 44 | 45 | arrayresp = phased.ArrayResponse('WeightsInputPort',true,... 46 | 'SensorArray',array); 47 | 48 | scanTheta = zeros(1,numel(scanPhi)); 49 | scanAngles = [scanPhi;scanTheta]; 50 | weights = steeringvec(f0,scanAngles); % Calculate Weights for steering 51 | 52 | az = -180:180; 53 | el = zeros(1,numel(az)); 54 | ang_pairs = [az;el]; 55 | 56 | arrayresp2d = ArrayResponseDemo2DPolarScope; % Initialize scope 57 | 58 | for t = 1:length(scanTheta) % Calculate response 59 | w = weights(:,t); 60 | temp_out = abs(arrayresp(f0,ang_pairs,w)); 61 | temp_out = temp_out/max(temp_out); 62 | arrayresp2d(temp_out); % Display on scope 63 | end 64 | 65 | 66 | % Derive the elevation pattern. 67 | el_ang = -90:90; 68 | arrayresp = phased.ArrayResponse('SensorArray',array, ... 69 | 'PropagationSpeed',c); 70 | el_pat = abs(arrayresp(fmax,el_ang)); % elevation pattern 71 | 72 | 73 | % Plot the radar vertical diagram. 74 | freespace_rng = 100; % in km 75 | ant_height = 20; % in m 76 | 77 | radarvcd(fmax,freespace_rng,ant_height,... 78 | 'HeightUnit','m','RangeUnit','km',... 79 | 'AntennaPattern',el_pat/max(el_pat),'PatternAngles',el_ang.'); 80 | 81 | 82 | 83 | subplot(211) 84 | pattern(array,fc,-180:180,0,'CoordinateSystem','rectangular', ... 85 | 'PropagationSpeed',c,'Type','powerdb') 86 | title('Without steering') 87 | subplot(212) 88 | pattern(array,fc,-180:180,0,'CoordinateSystem','rectangular', ... 89 | 'PropagationSpeed',c,'Type','powerdb','Weights',sv) 90 | title('With steering') -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /PSO_on_Linear_Array_For_Null.m: -------------------------------------------------------------------------------- 1 | clear all 2 | clc 3 | 4 | beam_theta = 0; 5 | null_thetas = [35 65]; 6 | Goal_Beam_Power = 24; 7 | Goal_SINR_Value = 70; 8 | 9 | freq_range = [24e9 28e9]; % [start_frequency stop_frequency] [Hz] 10 | freq_points = 101; % No. of frequency points 11 | d = 0.005; % Element spacing [m] 12 | weights = ones(1, 16); % Amplitude weights for N elements from 0 to 1 13 | 14 | freq_step = (freq_range(2) - freq_range(1)) / (freq_points - 1); 15 | frequency_vector = freq_range(1):freq_step:freq_range(2); 16 | theta_vector_degrees = -90:1:90; 17 | theta_vector_rads = theta_vector_degrees * (pi / 180); 18 | 19 | for n = 1:1:length(frequency_vector) 20 | frequency = frequency_vector(n); 21 | 22 | % create omnidirectional characteristic 23 | iPattern = zeros(1, length(theta_vector_degrees)); 24 | 25 | % Calculate Array Factor 26 | for nn = 1:1:length(theta_vector_rads) 27 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nn), frequency, d, weights); 28 | weight_vs_angle{n}{nn} = {theta_vector_rads(nn), AV}; 29 | %Combine for full characteristics 30 | F(n, nn) = AF_dB + iPattern(nn); 31 | end 32 | end 33 | 34 | figure(2); 35 | plot(theta_vector_degrees, F(end,:)); 36 | 37 | 38 | %Now do PSO algorithm 39 | particles_per_value = 64; 40 | 41 | % PSO paramters 42 | Max_iteration = 60; 43 | values = size(weights, 1) * size(weights, 2); 44 | magnitude_max = 1; 45 | velocity_max = 0.4; 46 | inertia_max = 0.9; 47 | inertia_min = 0.2; 48 | c1 = 2; c2 = 2; 49 | cg_curve = zeros(1,Max_iteration, values); 50 | 51 | particle_grid_steps = sqrt(particles_per_value); 52 | x = linspace(-1/sqrt(2), 1/sqrt(2), particle_grid_steps); 53 | 54 | % Initializations 55 | for value = 1:1:values 56 | for n = 1:1:particle_grid_steps 57 | for nn = 1:1:particle_grid_steps 58 | particles_value(n, nn, value) = x(n) + j * x(nn); 59 | end 60 | end 61 | end 62 | 63 | particles_velocities = velocity_max * (rand(particle_grid_steps, particle_grid_steps, values) ... 64 | + (j * rand(particle_grid_steps, particle_grid_steps, values))); 65 | particles_value_personal_best = rand(particle_grid_steps, particle_grid_steps, values) ... 66 | + (j * rand(particle_grid_steps, particle_grid_steps, values)); 67 | particle_personal_best_objective_function = zeros(particle_grid_steps, particle_grid_steps, values); 68 | 69 | swarm_global_best_value = ones(1, 1, values); 70 | swarm_global_best_objective_function = zeros(1, 1, values); 71 | 72 | for itteration = 1:1:Max_iteration % main loop 73 | 74 | %Calculate objective function for each particle 75 | for value = 1:1:values 76 | for n = 1:1:particle_grid_steps 77 | for nn = 1:1:particle_grid_steps 78 | 79 | %update active weights 80 | weights = permute(swarm_global_best_value, [3 1 2]).'; 81 | weights(1, value) = particles_value(n, nn, value); 82 | 83 | %Compute objective function START 84 | frequency = frequency_vector(end); 85 | 86 | % create omnidirectional characteristic 87 | iPattern = zeros(1, length(theta_vector_degrees)); 88 | 89 | % Calculate Array Factor 90 | for nnn = 1:1:length(theta_vector_rads) 91 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nnn), frequency, d, weights); 92 | %Combine for full characteristics 93 | F(end, nnn) = AF_dB + iPattern(nnn); 94 | end 95 | 96 | 97 | SINR = sum(F(end, find(theta_vector_degrees == beam_theta)) - F(end, find(ismember(theta_vector_degrees, null_thetas)))) / length(null_thetas); 98 | Beam_Power = F(end, find(theta_vector_degrees == beam_theta)); 99 | %Compute objective function END 100 | 101 | objective_function_value = (SINR/Goal_SINR_Value) + (Beam_Power / Goal_Beam_Power); 102 | if objective_function_value > particle_personal_best_objective_function(n, nn, value) 103 | particle_personal_best_objective_function(n, nn, value) = objective_function_value; 104 | particles_value_personal_best(n, nn, value) = particles_value(n, nn, value); 105 | end 106 | if(objective_function_value > swarm_global_best_objective_function(1, 1, value)) 107 | swarm_global_best_objective_function(1, 1, value) = objective_function_value; 108 | swarm_global_best_value(1, 1, value) = particles_value(n, nn, value); 109 | end 110 | end 111 | end 112 | end 113 | 114 | %Update the inertia weight 115 | inertial_weight = inertia_max - (itteration * ( (inertia_max - inertia_min) / Max_iteration)); 116 | 117 | %Update the Velocity and Position of particles 118 | particles_velocities = inertial_weight * particles_velocities + ... % inertia 119 | c1 * rand(particle_grid_steps, particle_grid_steps, values) .* (particles_value_personal_best - particles_value) + ... % congnitive 120 | c2 * rand(particle_grid_steps, particle_grid_steps, values) .* (swarm_global_best_value - particles_value); % social 121 | 122 | index = find(particles_velocities > velocity_max); 123 | particles_velocities(index) = velocity_max * rand; 124 | 125 | index = find(particles_velocities < -velocity_max); 126 | particles_velocities(index) = -velocity_max * rand; 127 | 128 | particles_value = particles_value + particles_velocities; 129 | 130 | magnitude = sqrt(particles_value .* conj(particles_value)); 131 | index = find(magnitude > magnitude_max); 132 | particles_value(index) = particles_value(index) ./ magnitude(index); 133 | 134 | cg_curve(itteration, 1:1:values) = swarm_global_best_objective_function; 135 | swarm_global_best_objective_function 136 | end 137 | figure(1) 138 | hold on 139 | for value = 1:1:values 140 | plot(cg_curve(:,value)) 141 | end 142 | xlabel('Iteration') 143 | 144 | 145 | weights = permute(swarm_global_best_value, [3 1 2]).'; 146 | 147 | for n = 1:1:length(frequency_vector) 148 | frequency = frequency_vector(n); 149 | 150 | % create omnidirectional characteristic 151 | iPattern = zeros(1, length(theta_vector_degrees)); 152 | 153 | % Calculate Array Factor 154 | for nn = 1:1:length(theta_vector_rads) 155 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nn), frequency, d, weights); 156 | %Combine for full characteristics 157 | F(n, nn) = AF_dB + iPattern(nn); 158 | end 159 | end 160 | 161 | figure(2); 162 | hold on 163 | plot(theta_vector_degrees, F(end,:)); -------------------------------------------------------------------------------- /PSO_on_Linear_Array_For_SLL.m: -------------------------------------------------------------------------------- 1 | clear all 2 | clc 3 | 4 | beam_theta = 0; 5 | Goal_Beam_Power = 24; 6 | Goal_SLL_Value = -30; 7 | beam_width_in_Degrees = 20; %Must be even 8 | 9 | freq_range = [24e9 28e9]; % [start_frequency stop_frequency] [Hz] 10 | freq_points = 101; % No. of frequency points 11 | d = 0.005; % Element spacing [m] 12 | weights = ones(1, 16); % Amplitude weights for N elements from 0 to 1 13 | 14 | freq_step = (freq_range(2) - freq_range(1)) / (freq_points - 1); 15 | frequency_vector = freq_range(1):freq_step:freq_range(2); 16 | theta_vector_degrees = -90:1:90; 17 | theta_vector_rads = theta_vector_degrees * (pi / 180); 18 | 19 | for n = 1:1:length(frequency_vector) 20 | frequency = frequency_vector(n); 21 | 22 | % create omnidirectional characteristic 23 | iPattern = zeros(1, length(theta_vector_degrees)); 24 | 25 | % Calculate Array Factor 26 | for nn = 1:1:length(theta_vector_rads) 27 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nn), frequency, d, weights); 28 | weight_vs_angle{n}{nn} = {theta_vector_rads(nn), AV}; 29 | %Combine for full characteristics 30 | F(n, nn) = AF_dB + iPattern(nn); 31 | end 32 | end 33 | 34 | mask = [(max(F(end,:)) + Goal_SLL_Value)*ones(1, (181 - (beam_width_in_Degrees + 1)) / 2) max(F(end,:))*ones(1, beam_width_in_Degrees + 1) (max(F(end,:)) + Goal_SLL_Value)*ones(1, (181 - (beam_width_in_Degrees + 1)) / 2)]; 35 | slmask = ~(mask == max(F(end,:))); 36 | 37 | figure(2); 38 | plot(theta_vector_degrees, F(end,:)); 39 | hold on 40 | plot(theta_vector_degrees, mask,'k'); 41 | 42 | %Now do PSO algorithm 43 | particles_per_value = 64; 44 | 45 | % PSO paramters 46 | Max_iteration = 60; 47 | values = size(weights, 1) * size(weights, 2); 48 | magnitude_max = 1; 49 | velocity_max = 0.4; 50 | inertia_max = 0.9; 51 | inertia_min = 0.2; 52 | c1 = 2; c2 = 2; 53 | cg_curve = zeros(1,Max_iteration, values); 54 | 55 | particle_grid_steps = sqrt(particles_per_value); 56 | x = linspace(-1/sqrt(2), 1/sqrt(2), particle_grid_steps); 57 | 58 | % Initializations 59 | for value = 1:1:values 60 | for n = 1:1:particle_grid_steps 61 | for nn = 1:1:particle_grid_steps 62 | particles_value(n, nn, value) = x(n) + j * x(nn); 63 | end 64 | end 65 | end 66 | 67 | particles_velocities = velocity_max * (rand(particle_grid_steps, particle_grid_steps, values) ... 68 | + (j * rand(particle_grid_steps, particle_grid_steps, values))); 69 | particles_value_personal_best = rand(particle_grid_steps, particle_grid_steps, values) ... 70 | + (j * rand(particle_grid_steps, particle_grid_steps, values)); 71 | particle_personal_best_objective_function = 1e3*ones(particle_grid_steps, particle_grid_steps, values); 72 | 73 | swarm_global_best_value = ones(1, 1, values); 74 | swarm_global_best_objective_function = 1e3*ones(1, 1, values); 75 | 76 | for itteration = 1:1:Max_iteration % main loop 77 | 78 | %Calculate objective function for each particle 79 | for value = 1:1:values 80 | for n = 1:1:particle_grid_steps 81 | for nn = 1:1:particle_grid_steps 82 | 83 | %update active weights 84 | weights = permute(swarm_global_best_value, [3 1 2]).'; 85 | weights(1, value) = particles_value(n, nn, value); 86 | 87 | %Compute objective function START 88 | frequency = frequency_vector(end); 89 | 90 | % create omnidirectional characteristic 91 | iPattern = zeros(1, length(theta_vector_degrees)); 92 | 93 | % Calculate Array Factor 94 | for nnn = 1:1:length(theta_vector_rads) 95 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nnn), frequency, d, weights); 96 | %Combine for full characteristics 97 | F(end, nnn) = AF_dB + iPattern(nnn); 98 | end 99 | 100 | %Remove -Inf values 101 | temp = F(end, find(F(end, :).*slmask ~= 0)); ind = find(temp == -Inf); temp(ind) = -100; 102 | SLL = sum(temp - Goal_SLL_Value) / sum(slmask); 103 | temp = F(end, find(F(end, :).*(~slmask) ~= 0)); ind = find(temp == -Inf); temp(ind) = -100; 104 | Beam_Power = sum(Goal_Beam_Power - temp) / sum(~slmask); 105 | %Compute objective function END 106 | 107 | objective_function_value = SLL + Beam_Power; 108 | if objective_function_value < particle_personal_best_objective_function(n, nn, value) 109 | particle_personal_best_objective_function(n, nn, value) = objective_function_value; 110 | particles_value_personal_best(n, nn, value) = particles_value(n, nn, value); 111 | end 112 | if(objective_function_value < swarm_global_best_objective_function(1, 1, value)) 113 | swarm_global_best_objective_function(1, 1, value) = objective_function_value; 114 | swarm_global_best_value(1, 1, value) = particles_value(n, nn, value); 115 | end 116 | end 117 | end 118 | end 119 | 120 | %Update the inertia weight 121 | inertial_weight = inertia_max - (itteration * ( (inertia_max - inertia_min) / Max_iteration)); 122 | 123 | %Update the Velocity and Position of particles 124 | particles_velocities = inertial_weight * particles_velocities + ... % inertia 125 | c1 * rand(particle_grid_steps, particle_grid_steps, values) .* (particles_value_personal_best - particles_value) + ... % congnitive 126 | c2 * rand(particle_grid_steps, particle_grid_steps, values) .* (swarm_global_best_value - particles_value); % social 127 | 128 | index = find(particles_velocities > velocity_max); 129 | particles_velocities(index) = velocity_max * rand; 130 | 131 | index = find(particles_velocities < -velocity_max); 132 | particles_velocities(index) = -velocity_max * rand; 133 | 134 | particles_value = particles_value + particles_velocities; 135 | 136 | magnitude = sqrt(particles_value .* conj(particles_value)); 137 | index = find(magnitude > magnitude_max); 138 | particles_value(index) = particles_value(index) ./ magnitude(index); 139 | 140 | cg_curve(itteration, 1:1:values) = swarm_global_best_objective_function; 141 | swarm_global_best_objective_function 142 | end 143 | figure(1) 144 | hold on 145 | for value = 1:1:values 146 | plot(cg_curve(:,value)) 147 | end 148 | xlabel('Iteration') 149 | 150 | 151 | weights = permute(swarm_global_best_value, [3 1 2]).'; 152 | 153 | for n = 1:1:length(frequency_vector) 154 | frequency = frequency_vector(n); 155 | 156 | % create omnidirectional characteristic 157 | iPattern = zeros(1, length(theta_vector_degrees)); 158 | 159 | % Calculate Array Factor 160 | for nn = 1:1:length(theta_vector_rads) 161 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nn), frequency, d, weights); 162 | %Combine for full characteristics 163 | F(n, nn) = AF_dB + iPattern(nn); 164 | end 165 | end 166 | 167 | figure(2); 168 | hold on 169 | plot(theta_vector_degrees, F(end,:)); -------------------------------------------------------------------------------- /Pattern_Visualization.m: -------------------------------------------------------------------------------- 1 | clear all 2 | clc 3 | 4 | frequency = 28e9; 5 | d_a = 0.005; 6 | weights = ones(1, 10); 7 | array_type = 'circular'; 8 | 9 | [phi_theta, phi_theta_dB, phi, theta] = generate_phi_theta(frequency, d_a, weights, array_type); 10 | 11 | patternCustom(phi_theta_dB', theta, phi); 12 | patternCustom(phi_theta_dB', theta, phi,'CoordinateSystem','rectangular'); 13 | patternCustom(phi_theta_dB', theta, phi,'CoordinateSystem','polar','Slice', ... 14 | 'phi','SliceValue',[45 90 180 359]); 15 | patternCustom(phi_theta_dB', theta, phi,'CoordinateSystem','rectangular', ... 16 | 'Slice','phi','SliceValue',[45 90 180 359]); 17 | 18 | %converts the pattern from phi - theta coordinates to az-el coordinates. 19 | [pattern_azel,az,el] = phitheta2azelpat(phi_theta_dB, phi, theta); 20 | 21 | freqVector = [24 28].*1e9; % Frequency range for element pattern 22 | antenna = phased.CustomAntennaElement('FrequencyVector', freqVector, ... 23 | 'AzimuthAngles', az, ... 24 | 'ElevationAngles', el, ... 25 | 'MagnitudePattern', pattern_azel, ... 26 | 'PhasePattern', zeros(size(pattern_azel))); 27 | 28 | fmax = freqVector(end); 29 | pattern(antenna,fmax,'Type','powerdb') 30 | view(90,0) 31 | 32 | %more here 33 | %https://www.mathworks.com/help/antenna/examples/visualize-custom-radiation-patterns.html 34 | %https://www.mathworks.com/help/phased/examples/antenna-array-analysis-with-custom-radiation-pattern.html -------------------------------------------------------------------------------- /Plot_Circular_Array_Response.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Plot_Circular_Array_Response.m -------------------------------------------------------------------------------- /Plot_Circular_Array_With_Element_Response.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Plot_Circular_Array_With_Element_Response.m -------------------------------------------------------------------------------- /Plot_Linear_Array_Response.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Plot_Linear_Array_Response.m -------------------------------------------------------------------------------- /Plot_Linear_Array_With_Element_Response.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Plot_Linear_Array_With_Element_Response.m -------------------------------------------------------------------------------- /Plot_Planar_Array_Response.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Plot_Planar_Array_Response.m -------------------------------------------------------------------------------- /Plot_Planar_Array_With_Element_Response.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Plot_Planar_Array_With_Element_Response.m -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Phased_Array 2 | Phased array and beamforming library 3 | -------------------------------------------------------------------------------- /SLC_on_Linear_Array_For_Null.m: -------------------------------------------------------------------------------- 1 | clear all 2 | clc 3 | 4 | beam_thetas = [0 50 70]; 5 | null_thetas = [35 60 80 90]; 6 | 7 | freq_range = [24e9 28e9]; % [start_frequency stop_frequency] [Hz] 8 | freq_points = 101; % No. of frequency points 9 | d = 0.005; % Element spacing [m] 10 | weights = ones(1, 16); % Amplitude weights for N elements from 0 to 1 11 | 12 | freq_step = (freq_range(2) - freq_range(1)) / (freq_points - 1); 13 | frequency_vector = freq_range(1):freq_step:freq_range(2); 14 | theta_vector_degrees = -90:1:90; 15 | theta_vector_rads = theta_vector_degrees * (pi / 180); 16 | 17 | for n = 1:1:length(frequency_vector) 18 | frequency = frequency_vector(n); 19 | 20 | % create omnidirectional characteristic 21 | iPattern = zeros(1, length(theta_vector_degrees)); 22 | 23 | % Calculate Array Factor 24 | for nn = 1:1:length(theta_vector_rads) 25 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nn), frequency, d, weights); 26 | weight_vs_angle{n}{nn} = {theta_vector_rads(nn), AV}; 27 | %Combine for full characteristics 28 | F(n, nn) = AF_dB + iPattern(nn); 29 | end 30 | end 31 | 32 | figure(2); 33 | plot(theta_vector_degrees, F(end,:)); 34 | 35 | %Calculate weights for sidelobe canceller 36 | for n = 1:1:length(beam_thetas) 37 | weights_desired_direction(:, :, n) = weight_vs_angle{end}{find(theta_vector_degrees == beam_thetas(n))}{2}'; 38 | end 39 | %Can just sum desired direction vectors and normalize for total beam response 40 | weights_desired_direction = sum(weights_desired_direction, 3); 41 | weights_desired_direction = weights_desired_direction ./ abs(weights_desired_direction); 42 | 43 | %Need to itteratively subtract out null vectors, cannot do cumulatively 44 | %like desired vectors, however, you can only effectively place one deep null 45 | %with this method which will be the last one that you subtract. The other 46 | %nulls may or may not be there but if they are there they will not be there 47 | %not like that final one you place 48 | 49 | %weights = desired_weights - null_weights * ((null_weights' * desired_weights) / (null_weights' * null_weights)); 50 | %What is happening here is from a paper about optimization but it can be 51 | %thought of as the following 52 | %(null_weights' * desired_weights) is the difference vector between null_weights and desired_weights 53 | % i.e. is the angle between the two vectors and also difference in 54 | % magnitude 55 | %(null_weights' * null_weights) is the magnitude of the null_weights 56 | % so null_weights * ((null_weights' * desired_weights) / (null_weights' * null_weights)); 57 | % is doing (null_weights / magnitude of null_weights) * difference in 58 | % magnitude and angle between null_weights and desired_weights 59 | % again this is actually derives as the LMS value in the paper 60 | % 5.0 SIDELOBE CANCELLATION 61 | % but this is one way to think of how I have applied it here vs how they 62 | % apply it / derive its application in the paper 63 | weights = weights_desired_direction; 64 | for n = 1:1:length(null_thetas) 65 | null_weights = weight_vs_angle{end}{find(theta_vector_degrees == null_thetas(n))}{2}'; 66 | weights = weights - null_weights * ((null_weights' * weights) / (null_weights' * null_weights)); 67 | weights_null_direction(:, :, n) = null_weights; 68 | end 69 | 70 | %Apply 71 | for n = 1:1:length(frequency_vector) 72 | frequency = frequency_vector(n); 73 | 74 | % create omnidirectional characteristic 75 | iPattern = zeros(1, length(theta_vector_degrees)); 76 | 77 | % Calculate Array Factor 78 | for nn = 1:1:length(theta_vector_rads) 79 | [AF, AF_dB, AV] = Uniform_Linear_Array(theta_vector_rads(nn), frequency, d, weights); 80 | %Combine for full characteristics 81 | F(n, nn) = AF_dB + iPattern(nn); 82 | end 83 | end 84 | 85 | figure(2); 86 | hold on 87 | plot(theta_vector_degrees, F(end,:)); -------------------------------------------------------------------------------- /Test_Array/Far_Field_Data/5ELx2 FarField Tx Only.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Test_Array/Far_Field_Data/5ELx2 FarField Tx Only.mat -------------------------------------------------------------------------------- /Test_Array/Near_Field_Data/5ELx2 1meter NearField Tx Only.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Test_Array/Near_Field_Data/5ELx2 1meter NearField Tx Only.mat -------------------------------------------------------------------------------- /Test_Array/PSO_Cancellation.m: -------------------------------------------------------------------------------- 1 | clear all 2 | clc 3 | 4 | %In this case we want all tx to rx values to be 0 5 | %Since the tx ports are 1-5 and the rx ports are 6-10 6 | %we want to make sure that S(6,7,8,9,10)1, S(6,7,8,9,10)2, etc.... 7 | %are all 0. Since the matrix has input ports vs columns and output ports 8 | %vs rows as below 9 | 10 | %s11 s12 s13 s14 s15 s16 s17 s18 s19 s110 11 | %s21 s22 s23 s24 s25 s26 s27 s28 s29 s210 12 | %s31 s32 s33 s34 s35 s36 s37 s38 s39 s310 13 | %s41 s42 s43 s44 s45 s46 s47 s48 s49 s410 14 | %s51 s52 s53 s54 s55 s56 s57 s58 s59 s510 15 | %s61 s62 s63 s64 s65 s66 s67 s68 s69 s610 16 | %s71 s72 s73 s74 s75 s76 s77 s78 s79 s710 17 | %s81 s82 s83 s84 s85 s86 s87 s88 s89 s810 18 | %s91 s92 s93 s94 s95 s96 s97 s98 s99 s910 19 | %s101 s102 s103 s104 s105 s106 s107 s108 s109 s1010 20 | 21 | %The inputs I have are to the weights on the upper left diagonal only 22 | %So woud defactor set x6-x10 = 0 23 | % x = [x1 x2 x3 x4 x5 0 0 0 0 0].'; 24 | %The output vector y results from s * x and since x is tx only that also 25 | %further masks off our s matrix effectively to the following 26 | 27 | %s11 s12 s13 s14 s15 0 0 0 0 0 28 | %s21 s22 s23 s24 s25 0 0 0 0 0 29 | %s31 s32 s33 s34 s35 0 0 0 0 0 30 | %s41 s42 s43 s44 s45 0 0 0 0 0 31 | %s51 s52 s53 s54 s55 0 0 0 0 0 32 | %s61 s62 s63 s64 s65 0 0 0 0 0 33 | %s71 s72 s73 s74 s75 0 0 0 0 0 34 | %s81 s82 s83 s84 s85 0 0 0 0 0 35 | %s91 s92 s93 s94 s95 0 0 0 0 0 36 | %s101 s102 s103 s104 s105 0 0 0 0 0 37 | 38 | %So effectively all we are reallly solving for is the left half of the 39 | %sparameter matrix so we can truncate the square matrix to a rectangular as 40 | %such 41 | 42 | %| s11 s12 s13 s14 s15 | * | x1 | = | y1 | 43 | %| s21 s22 s23 s24 s25 | | x2 | | y2 | 44 | %| s31 s32 s33 s34 s35 | | x3 | | y3 | 45 | %| s41 s42 s43 s44 s45 | | x4 | | y4 | 46 | %| s51 s52 s53 s54 s55 | | x5 | | y5 | 47 | %| s61 s62 s63 s64 s65 | | y6 | 48 | %| s71 s72 s73 s74 s75 | | y7 | 49 | %| s81 s82 s83 s84 s85 | | y8 | 50 | %| s91 s92 s93 s94 s95 | | y9 | 51 | %| s101 s102 s103 s104 s105 | | y10 | 52 | 53 | %The goal is to minize the y6 - y10 terms and we don't care about the 54 | %y1 - y5 terms so again we can effectively reduce the matrix to the 55 | %following 56 | 57 | %| s61 s62 s63 s64 s65 | * | x1 | = | y6 | 58 | %| s71 s72 s73 s74 s75 | | x2 | | y7 | 59 | %| s81 s82 s83 s84 s85 | | x3 | | y8 | 60 | %| s91 s92 s93 s94 s95 | | x4 | | y9 | 61 | %| s101 s102 s103 s104 s105 | | x5 | | y10 | 62 | 63 | %And lastly we can say that we want to just minimize all values equally so 64 | %we will say that min( y*y' ) is the fitness function that we want to 65 | %evaluate/minimize which is just to total power from the tramsmit array 66 | %into the receive array 67 | 68 | %Also note that the manifold file has x different cell for each frequency 69 | %Once in a manifold you have a single element per column and rows are vs 70 | %phi 71 | 72 | [FileName, sp, freq_GHz] = ReadTouchstone(); 73 | sparameter_freq_vector = freq_GHz * 1e9; 74 | 75 | Near_Field_Struct = load('./Near_Field_Data/5ELx2 1meter NearField Tx Only.mat'); 76 | Far_Field_Struct = load('./Far_Field_Data/5ELx2 FarField Tx Only.mat'); 77 | 78 | beam_theta = 90; 79 | null_phis = [0]; 80 | transmit_phis = [-180:5:15 15:5:175]; 81 | Max_SINR = 70; 82 | 83 | freq_range = [5e8 6e9]; % [start_frequency stop_frequency] [Hz] 84 | freq_step = 50e6; % Frequency Step size [Hz] 85 | weights = ones(1, 5); % Amplitude weights for N elements from 0 to 1 86 | 87 | Starting_Element_Number = 6; 88 | Ending_Element_Number = 10; 89 | S = sp(Starting_Element_Number:1:Ending_Element_Number, 1:1:length(weights), :); 90 | 91 | freq_points = ((freq_range(2) - freq_range(1)) / freq_step) + 1; 92 | frequency_vector = freq_range(1):freq_step:freq_range(2); 93 | phi_vector_degrees = -180:1:179; 94 | phi_vector_rads = phi_vector_degrees * (pi / 180); 95 | 96 | N = zeros(length(frequency_vector), length(phi_vector_degrees)); 97 | F = zeros(length(frequency_vector), length(phi_vector_degrees)); 98 | for n = 1:1:length(frequency_vector) 99 | frequency = frequency_vector(n); 100 | 101 | near_field_findex = find(Near_Field_Struct.flist_MHz == (frequency/1e6)); 102 | near_field_vs_phi = Near_Field_Struct.manifold{near_field_findex}; 103 | temp = near_field_vs_phi(ismember(Near_Field_Struct.phi, phi_vector_degrees), :).*weights; 104 | AF = sum(temp, 2); 105 | N(n, :) = 10*log10(AF.*conj(AF)); 106 | 107 | far_field_findex = find(Far_Field_Struct.flist_MHz == (frequency/1e6)); 108 | far_field_vs_phi = Far_Field_Struct.manifold{far_field_findex}; 109 | temp = far_field_vs_phi(ismember(Far_Field_Struct.phi, phi_vector_degrees), :).*weights; 110 | AF = sum(temp, 2); 111 | F(n, :) = 10*log10(AF.*conj(AF)); 112 | end 113 | 114 | analysis_and_plot_frequency_index = find(frequency_vector == 1e9); 115 | analysis_and_plot_frequency = frequency_vector(analysis_and_plot_frequency_index); 116 | 117 | figure(2); 118 | plot(phi_vector_degrees, N(analysis_and_plot_frequency_index,:), phi_vector_degrees, F(analysis_and_plot_frequency_index,:)); 119 | figure(3) 120 | polarplot(phi_vector_degrees*pi/180, N(analysis_and_plot_frequency_index,:)); 121 | figure(4) 122 | polarplot(phi_vector_degrees*pi/180, F(analysis_and_plot_frequency_index,:)); 123 | 124 | FF_Max = max(F(analysis_and_plot_frequency_index,:)); 125 | 126 | 127 | %Now do PSO algorithm 128 | particles_per_value = 64; 129 | 130 | % PSO paramters 131 | Max_iteration = 200; 132 | values = size(weights, 1) * size(weights, 2); 133 | magnitude_max = 1; 134 | velocity_max = 0.4; 135 | inertia_max = 0.9; 136 | inertia_min = 0.2; 137 | c1 = 2; c2 = 2; 138 | cg_curve = zeros(1,Max_iteration, values); 139 | 140 | particle_grid_steps = sqrt(particles_per_value); 141 | x = linspace(-1/sqrt(2), 1/sqrt(2), particle_grid_steps); 142 | 143 | % Initializations 144 | for value = 1:1:values 145 | for n = 1:1:particle_grid_steps 146 | for nn = 1:1:particle_grid_steps 147 | particles_value(n, nn, value) = x(n) + j * x(nn); 148 | end 149 | end 150 | end 151 | 152 | particles_velocities = velocity_max * (rand(particle_grid_steps, particle_grid_steps, values) ... 153 | + (j * rand(particle_grid_steps, particle_grid_steps, values))); 154 | particles_value_personal_best = rand(particle_grid_steps, particle_grid_steps, values) ... 155 | + (j * rand(particle_grid_steps, particle_grid_steps, values)); 156 | particle_personal_best_objective_function = 1e3*ones(particle_grid_steps, particle_grid_steps, values); 157 | 158 | swarm_global_best_value = ones(1, 1, values); 159 | swarm_global_best_objective_function = 1e3*ones(1, 1, values); 160 | 161 | for itteration = 1:1:Max_iteration % main loop 162 | 163 | %Calculate objective function for each particle 164 | for value = 1:1:values 165 | for n = 1:1:particle_grid_steps 166 | for nn = 1:1:particle_grid_steps 167 | 168 | %update active weights 169 | weights = permute(swarm_global_best_value, [3 1 2]).'; 170 | weights(1, value) = particles_value(n, nn, value); 171 | 172 | %Compute objective function START 173 | frequency = analysis_and_plot_frequency; 174 | 175 | near_field_findex = find(Near_Field_Struct.flist_MHz == (frequency/1e6)); 176 | near_field_vs_phi = Near_Field_Struct.manifold{near_field_findex}; 177 | temp = near_field_vs_phi(ismember(Near_Field_Struct.phi, null_phis), :).*weights; 178 | AF = sum(temp, 2); 179 | NF = 10*log10(sum(AF.*conj(AF))/length(null_phis)); 180 | 181 | far_field_findex = find(Far_Field_Struct.flist_MHz == (frequency/1e6)); 182 | far_field_vs_phi = Far_Field_Struct.manifold{far_field_findex}; 183 | temp = far_field_vs_phi(ismember(Far_Field_Struct.phi, transmit_phis), :).*weights; 184 | AF = sum(temp, 2); 185 | FF = 10*log10(sum(AF.*conj(AF))/length(transmit_phis)); 186 | 187 | sparameter_frequency_index = find(sparameter_freq_vector == frequency); 188 | 189 | temp = S(:, :, sparameter_frequency_index) * weights.'; 190 | MSP = 10*log10((temp'*temp) / length(temp)); 191 | 192 | Power_Loss = 10*log10(length(weights) / (weights*weights')); 193 | 194 | % Use Near Fields (Modified for SINR) 195 | % Just need to limit the near field null effect so it doesn't 196 | % dominate the cost function 197 | MNF = ((FF - Max_SINR) * (NF < (FF - Max_SINR))) + (NF * ~(NF < (FF - Max_SINR))); 198 | MPL = (Power_Loss * (3 > Power_Loss)) + (Power_Loss*10 * ~(3 > Power_Loss)); 199 | % Dont use Sparameter results, they skew the cost function and 200 | % have no bearing on nulling performance. They do reflect good 201 | % nulling once weights are computed but do not do well to adapt 202 | % for good weights 203 | %objective_function_value = MSP + MNF + MPL + (FF_Max - FF); 204 | objective_function_value = MNF + MPL + (FF_Max - FF); 205 | % Use S parameters and Near Fields 206 | %objective_function_value = MSP + NF + Signal_Power + (FF_Max - FF); 207 | % Use Near Fields only 208 | %objective_function_value = NF + Signal_Power; 209 | % Use Far Fields only (Creats null in far filed but it shifts 210 | % in the near filed and fills in so this will not work) 211 | %objective_function_value = Signal_Power + FF; 212 | % Use S parameters only (Does not create null like we would 213 | % hope so we can ignore these in optimization algorithm) 214 | %objective_function_value = MSP + Signal_Power; 215 | 216 | 217 | if objective_function_value < particle_personal_best_objective_function(n, nn, value) 218 | particle_personal_best_objective_function(n, nn, value) = objective_function_value; 219 | particles_value_personal_best(n, nn, value) = particles_value(n, nn, value); 220 | end 221 | if(objective_function_value < swarm_global_best_objective_function(1, 1, value)) 222 | swarm_global_best_objective_function(1, 1, value) = objective_function_value; 223 | swarm_global_best_value(1, 1, value) = particles_value(n, nn, value); 224 | end 225 | end 226 | end 227 | end 228 | 229 | %Update the inertia weight 230 | inertial_weight = inertia_max - (itteration * ( (inertia_max - inertia_min) / Max_iteration)); 231 | 232 | %Update the Velocity and Position of particles 233 | particles_velocities = inertial_weight * particles_velocities + ... % inertia 234 | c1 * rand(particle_grid_steps, particle_grid_steps, values) .* (particles_value_personal_best - particles_value) + ... % congnitive 235 | c2 * rand(particle_grid_steps, particle_grid_steps, values) .* (swarm_global_best_value - particles_value); % social 236 | 237 | index = find(particles_velocities > velocity_max); 238 | particles_velocities(index) = velocity_max * rand; 239 | 240 | index = find(particles_velocities < -velocity_max); 241 | particles_velocities(index) = -velocity_max * rand; 242 | 243 | particles_value = particles_value + particles_velocities; 244 | 245 | magnitude = sqrt(particles_value .* conj(particles_value)); 246 | index = find(magnitude > magnitude_max); 247 | particles_value(index) = particles_value(index) ./ magnitude(index); 248 | 249 | cg_curve(itteration, 1:1:values) = swarm_global_best_objective_function; 250 | swarm_global_best_objective_function 251 | end 252 | figure(1) 253 | hold on 254 | for value = 1:1:values 255 | plot(cg_curve(:,value)) 256 | end 257 | xlabel('Iteration') 258 | 259 | weights = permute(swarm_global_best_value, [3 1 2]).'; 260 | 261 | weights.*weights'.' %Magnitudes 262 | angle(weights)*180/pi %Phases 263 | y = S(:, :, sparameter_frequency_index) * weights.'; 264 | 10*log10(y.*y'.') %S parameters 265 | 10*log10(weights*weights' / length(weights)) %power loss 266 | 267 | N = zeros(length(frequency_vector), length(phi_vector_degrees)); 268 | F = zeros(length(frequency_vector), length(phi_vector_degrees)); 269 | for n = 1:1:length(frequency_vector) 270 | frequency = frequency_vector(n); 271 | 272 | near_field_findex = find(Near_Field_Struct.flist_MHz == (frequency/1e6)); 273 | near_field_vs_phi = Near_Field_Struct.manifold{near_field_findex}; 274 | temp = near_field_vs_phi(ismember(Near_Field_Struct.phi, phi_vector_degrees), :).*weights; 275 | AF = sum(temp, 2); 276 | N(n, :) = 10*log10(AF.*conj(AF)); 277 | 278 | far_field_findex = find(Far_Field_Struct.flist_MHz == (frequency/1e6)); 279 | far_field_vs_phi = Far_Field_Struct.manifold{far_field_findex}; 280 | temp = far_field_vs_phi(ismember(Far_Field_Struct.phi, phi_vector_degrees), :).*weights; 281 | AF = sum(temp, 2); 282 | F(n, :) = 10*log10(AF.*conj(AF)); 283 | end 284 | 285 | figure(2); 286 | hold on 287 | plot(phi_vector_degrees, N(analysis_and_plot_frequency_index,:), phi_vector_degrees, F(analysis_and_plot_frequency_index,:)); 288 | figure(3) 289 | hold on 290 | polarplot(phi_vector_degrees*pi/180, N(analysis_and_plot_frequency_index,:)); 291 | ax = gca; 292 | ax.RLim = [0 FF_Max+2]; 293 | figure(4) 294 | hold on 295 | polarplot(phi_vector_degrees*pi/180, F(analysis_and_plot_frequency_index,:)); 296 | polarplot(phi_vector_degrees*pi/180, N(analysis_and_plot_frequency_index,:)); 297 | ax = gca; 298 | ax.RLim = [0 FF_Max+2]; -------------------------------------------------------------------------------- /Test_Array/Power_Point/2x5 Element Array NF Datas.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jsochacki/Phased_Array/6b815612739053dee38c9a8993f47ea73b206f38/Test_Array/Power_Point/2x5 Element Array NF Datas.pptx -------------------------------------------------------------------------------- /Test_Array/ReadTouchstone.m: -------------------------------------------------------------------------------- 1 | function [FileName, sp, freq_GHz] = ReadTouchstone() 2 | % function [sp,freq]=ReadTouchsone() 3 | % Reads the touchstone .snp file 4 | % Returns S parameters in complex 3D matrix in sp(n,m,freq) 5 | % Returns frequency in freq in [GHz] 6 | % Value of n in .snp must be the number of ports 7 | % Normalization factor is not returned in this script (the number after 8 | % R in the header), adding z0 in the return value should give this back 9 | 10 | % Touchstone format is based on the version 1.1 11 | % http://www.eda.org/pub/ibis/connector/touchstone_spec11.pdf 12 | 13 | % base on the script found on the edaboard.com and also N.K. 14 | % http://www.edaboard.com/ftopic328289.html 15 | % Ryosuke Ito on 12/02/2009 16 | 17 | 18 | 19 | verbose=1 ; % flag for verbose mode, if other than 0, shows the progress 20 | 21 | [FileName,PathName]=uigetfile('*.s*p','Select valid touchstone file'); 22 | % if cancel is pressed, returns zeros 23 | if FileName==0 24 | sp=0 ; freq_GHz=0 ; 25 | return 26 | end 27 | 28 | [t,r]=strtok(FileName,'.'); % separating before and after the first '.' 29 | [t,r]=strtok(r,'.'); % extracting the first token after the first '.' 30 | s1=sscanf(t,'%c%d%c'); % extracting the integer in between the characters from extension 31 | port_num=s1(2); % getting the n in 'snp' 32 | 33 | fname=[PathName, FileName]; 34 | fid=fopen(fname,'r'); 35 | 36 | % line_entries is the number of complex data per line 37 | if(port_num>4) 38 | line_entries=4; 39 | positions_per_last_line=rem(port_num,4); 40 | elseif(port_num==2) 41 | line_entries=4; 42 | else 43 | line_entries=port_num; 44 | end 45 | 46 | %%%%%%%%%%%%%%%%%%% 47 | mode=0; % data format mode 'RI' or 'MA' 48 | unit=1; % frequency unit, 1 for GHz, 1e-3 for MHz 49 | j=sqrt(-1); % defining j 50 | 51 | 52 | %% process the preamble. 53 | buffer='NaN' ; 54 | 55 | while( isnan(str2double(strtok(buffer))) ) % checking if the first token is not a number 56 | 57 | % read one line from the file, change to upper case, and save to buffer 58 | buffer=upper(fgetl(fid)); 59 | 60 | % detecting blank line and discard 61 | if(strcmp(strtok(buffer),'')) 62 | buffer=fgetl(fid); 63 | continue; 64 | end; 65 | 66 | % if the first non space character is ! 67 | if(strncmpi(strjust(buffer,'left'),'!',1) ) 68 | continue; 69 | end; 70 | 71 | % if the first non space character is # 72 | if(strncmpi(strjust(buffer,'left'),'#',1) ) 73 | [token, buffer]=strtok(buffer); % dropping the # and forward the buffer 74 | [token, buffer]=strtok(buffer); % reading the next token 75 | % 76 | if (strcmp(token, 'HZ')) 77 | unit=1.0E-9; 78 | elseif (strcmp(token, 'MHZ')) 79 | unit=1.0E-3; 80 | elseif (strcmp(token, 'GHZ')) 81 | unit=1.0; 82 | end; 83 | % 84 | [token, buffer]=strtok(buffer); % dropping 'S' 85 | [token, buffer]=strtok(buffer); 86 | % 87 | if (strcmp(token, 'RI')) 88 | mode=1; 89 | elseif (strcmp(token, 'MA')) 90 | mode=0; 91 | 92 | end; 93 | [token, buffer]=strtok(buffer); % dropping 'R' 94 | [token, buffer]=strtok(buffer); 95 | z0=str2num(token) ; % normalization value saved into z0 96 | end 97 | 98 | end 99 | 100 | % for verbose mode 101 | switch verbose 102 | case 0 103 | otherwise 104 | switch mode 105 | case 1 106 | disp('Reading touchstone file in RI mode.') ; 107 | case 0 108 | disp('Reading touchstone file in MA mode.') ; 109 | end 110 | 111 | switch unit 112 | case 1 113 | disp('Frequency unit is GHz.') ; 114 | case 1e-3 115 | disp('Frequency unit is MHz.') ; 116 | case 1e-9 117 | disp('Frequency unit is Hz.') ; 118 | end 119 | 120 | fprintf('Z0 = %f\n\n',z0) ; 121 | end 122 | 123 | 124 | %% Reading the data. 125 | n=1; 126 | while(strcmp(strtok(buffer),'')) 127 | buffer=fgetl(fid); 128 | end 129 | while(~feof(fid)) 130 | [data,buffer]=strtok(buffer) ; 131 | [data_x, count]=sscanf(data, '%f', 1); 132 | freq_GHz(n,1)=data_x*unit; 133 | 134 | l=1; 135 | while (l f = c / Lambda and c = 1/sqrt(mu_r*mu_0*epsi_r*epsi_0) 21 | % k = 2*pi*(c / lambda)*sqrt(mu_r*mu_0*epsi_r*epsi_0) => k = (2*pi)/lambda 22 | 23 | % Physical constants 24 | c = 299792458; 25 | 26 | % Derived values 27 | N = length(weights); 28 | lambda = c / f; 29 | k = (2 * pi) / lambda; 30 | delta_phi = (2 * pi) / N; 31 | 32 | n = 0:1:N-1; 33 | PHI = k * a * sin(theta); 34 | AV = weights .* exp(j * PHI * cos(phi - (delta_phi * n))); 35 | AF = sum(AV); 36 | 37 | AF_dB = 10*log10(AF*AF'); 38 | 39 | end -------------------------------------------------------------------------------- /Uniform_Linear_Array.m: -------------------------------------------------------------------------------- 1 | function [AF, AF_dB, AV] = Uniform_Linear_Array(theta, f, d, weights) 2 | %Uniform_Linear_Array Calculate array factor of linear antenna array for a given 3 | % theta [rad] based on an array defined by: 4 | % frequency f [Hz], 5 | % element spacing d [m], 6 | % and element complex weight 7 | 8 | % Here a zero value for theta corresponds to the line that the array makes 9 | % i.e. a vector with theta zero points directly in line with the array 10 | % i.e. parallel to the length of the array and 11 | % a theta of 90 degrees if perpendicular to the length of the array 12 | 13 | % Note that this linear array has a reference point that is at the very end 14 | % of the array and not in the center 15 | 16 | % Since k^2 = (2*pi*f)^2*mu*epsi then k = 2*pi*f*sqrt(mu_r*mu_0*epsi_r*epsi_0) 17 | % and c = lambda * f => f = c / Lambda and c = 1/sqrt(mu_r*mu_0*epsi_r*epsi_0) 18 | % k = 2*pi*(c / lambda)*sqrt(mu_r*mu_0*epsi_r*epsi_0) => k = (2*pi)/lambda 19 | 20 | if (size(weights, 2) < size(weights, 1)), weights = weights.';, end; 21 | 22 | % Physical constants 23 | c = 299792458; 24 | 25 | % Derived values 26 | N = length(weights); 27 | lambda = c / f; 28 | k = (2 * pi) / lambda; 29 | PHI = (k * d * cos(theta)); 30 | 31 | n = 1:1:N; 32 | AV = weights .* exp(j * PHI * (n-1)); 33 | AF = sum(AV); 34 | 35 | AF_dB = 10*log10(AF*AF'); 36 | 37 | %For sanity check if you like 38 | %(sin((N/2)*phi)/sin((1/2)*phi))^2 - AF*AF' < 1e-14 39 | %20*log10(sin((N/2)*phi)/sin((1/2)*phi)) - AF_dB < 1e-14 40 | 41 | end -------------------------------------------------------------------------------- /Uniform_Planar_Array.m: -------------------------------------------------------------------------------- 1 | function [AF, AF_dB, AV] = Uniform_Planar_Array(phi, theta, f, d, weights) 2 | %Uniform_Planar_Array Calculate array factor of linear antenna array for a given 3 | % theta [rad] based on an array defined by: 4 | % frequency f [Hz], 5 | % element spacing d [m], 6 | % and element complex weight 7 | 8 | % Here a zero value for theta corresponds to the normal vector to the plane 9 | % that the array makes 10 | % i.e. a vector with theta zero points directly in perpendicular to the 11 | % array face and a theta of 90 degrees if parallel/in plane to the face of the array 12 | 13 | % Note that this planar array has a reference point that is at the very end 14 | % of the array in the corner and not in the center 15 | 16 | % Also note that this planar array does NOT need to be square 17 | % it is M elements in the x and N elements in the y direction 18 | 19 | % Since k^2 = (2*pi*f)^2*mu*epsi then k = 2*pi*f*sqrt(mu_r*mu_0*epsi_r*epsi_0) 20 | % and c = lambda * f => f = c / Lambda and c = 1/sqrt(mu_r*mu_0*epsi_r*epsi_0) 21 | % k = 2*pi*(c / lambda)*sqrt(mu_r*mu_0*epsi_r*epsi_0) => k = (2*pi)/lambda 22 | 23 | % Physical constants 24 | c = 299792458; 25 | 26 | % Derived values 27 | M = size(weights, 1); 28 | N = size(weights, 2); 29 | lambda = c / f; 30 | k = (2 * pi) / lambda; 31 | 32 | PHI_X = (k * d * sin(theta) * cos(phi)); 33 | PHI_Y = (k * d * sin(theta) * sin(phi)); 34 | 35 | m = 1:1:M; n = 1:1:N; 36 | M_VECTOR = exp(j * PHI_X * (m-1)); 37 | N_VECTOR = exp(j * PHI_Y * (n-1)); 38 | AV = weights .* ((M_VECTOR.')*(N_VECTOR)); 39 | AF = sum(sum(AV)); 40 | 41 | AF_dB = 10*log10(AF*AF'); 42 | 43 | end -------------------------------------------------------------------------------- /generate_HFSS_field_vectors.m: -------------------------------------------------------------------------------- 1 | function [phi_theta_vector, all_theta, all_phi] = generate_HFSS_field_vectors(phi_theta, phi, theta) 2 | 3 | all_theta = repelem(theta, length(phi)); 4 | all_phi = repelem(phi, length(theta)); 5 | index = 1; 6 | for n = 0:1:(length(theta) - 1) 7 | for nn = 0:1:(length(phi) - 1) 8 | phi_theta_vector(index) = phi_theta(n + 1, nn + 1); 9 | index = index + 1; 10 | end 11 | end 12 | 13 | end -------------------------------------------------------------------------------- /generate_phi_theta.m: -------------------------------------------------------------------------------- 1 | function [phi_theta, phi_theta_dB, phi, theta] = generate_phi_theta(frequency, d_a, weights, array_type, element_phi_theta) 2 | 3 | phi = 0:1:359; 4 | theta = 0:1:179; 5 | phi_theta = zeros(length(theta), length(phi)); 6 | 7 | switch lower(array_type) 8 | case 'linear' 9 | for n = theta 10 | for nn = phi 11 | [phi_theta(n + 1, nn + 1), phi_theta_dB(n + 1, nn + 1), ~] = ... 12 | Uniform_Linear_Array(n * (pi / 180), frequency, d_a, weights); 13 | end 14 | end 15 | case 'planar' 16 | for n = theta 17 | for nn = phi 18 | [phi_theta(n + 1, nn + 1), phi_theta_dB(n + 1, nn + 1), ~] = ... 19 | Uniform_Planar_Array(nn * (pi / 180), n * (pi / 180), frequency, d_a, weights); 20 | end 21 | end 22 | case 'circular' 23 | for n = theta 24 | for nn = phi 25 | [phi_theta(n + 1, nn + 1), phi_theta_dB(n + 1, nn + 1), ~] = ... 26 | Uniform_Circular_Array(nn * (pi / 180), n * (pi / 180), frequency, d_a, weights); 27 | end 28 | end 29 | otherwise 30 | phi_theta=[]; phi=[]; theta=[]; 31 | error('The array geometry %s is not currently supported.', array_type); 32 | end 33 | 34 | switch nargin 35 | case 4 36 | case 5 37 | phi_theta = phi_theta .* element_phi_theta; 38 | phi_theta_dB = phi_theta_dB + 10*log10(element_phi_theta .* element_phi_theta'.') 39 | otherwise 40 | phi_theta=[]; phi=[]; theta=[]; 41 | error('Not enough input arguements provided'); 42 | end 43 | 44 | end --------------------------------------------------------------------------------