Distributed digital subarray analysis

clc; clear all; close all;

f = 3e8;
Nx = 10; % number of antennas in subarray
Ns1 = 5;% number of sub array
h = 8; % number of virtual array between subarray
c = 3e8;
lambda = c / f;
dx = 0.42*lambda;
beta = 2 * pi / lambda;
theta = [20];
K = length(theta);
L = round(Nx / 3);
Ri = ones(1,K);
A = 1000;
SNR1 =20;
%% sub array
for Ns = 1 : Ns1
for SNR = 1: SNR1
for l = 1 :A
for m = 1: Ns
for p = 1 : Nx
pos = (m-1)*Nx+p;
P_mp(pos) = (p-(Ns*Nx+(Ns-1)*h+1)/2+(m-1)*(Nx+h));
P_mp1(m,p) = (p-(Ns*Nx+(Ns-1)*h+1)/2+(m-1)*(Nx+h));
end
end

% P_mp1 = P_mp1 + 2*(0.5-rand(Ns,Nx))*pos_var_amp;
A_mp = [];
A_mp_a = [];
E_mp_a = [];
for i = 1:Ns
E_mp(:,:,i) = exp(-1j*beta*P_mp1(i,:)’*dx*sind(theta)); % pole 값
A_mp(:,:,i) = Ri.*E_mp(:,:,i); % 측정 값
A_mp = awgn(A_mp, SNR, ‘measured’); % noise 추가
A_mp1 = sum(A_mp,2);

A_mc(:,:,i) = A_mp1(1:Nx – L + 1,:,i);
A_mr(:,:,i) = A_mp1(Nx – L + 1:Nx,:,i);
A_mk(:,:,i) = hankel(A_mc(:,:,i), A_mr(:,:,i)); % Create Hankel matrix with A_c as the first column and A_r as the last row

E_mp_a = [E_mp_a; E_mp(:,:,i)];
A_mp_a = [A_mp_a ;A_mp1(:,:,i)];

end

%% Hankel stack
Y_ss = A_mk(:,:,1);

if Ns> 1
for z = 2:Ns
Y_ss = [Y_ss ; A_mk(:,:,z)];
end
else
Y_ss = A_mk(:,:,1);
end

[U, S, V] = svd(Y_ss);
V0 = V(:, 1:K);
V1 = V0(1:end-1, :);
V2 = V0(2:end, :);

z_k = eig(pinv(V1) * V2);
find_theta_realarray_MPM(l,:)= asind((imag(log(z_k))*lambda)/(2*pi*dx))’;

rmse_real_mpm(l,:) = sort(theta) – sort(find_theta_realarray_MPM(l,:));
end

rmse_real_MPM_sum = sum(sum(abs(rmse_real_mpm).^2,1),2);
rmse_real_MPM_sum = sqrt(rmse_real_MPM_sum/(A*K));
rmse_real_MPM_sum1(SNR,Ns)= rmse_real_MPM_sum(rmse_real_MPM_sum ~= 0);
end
end

plot(1:SNR1 , rmse_real_MPM_sum1,’-d’,’LineWidth’,1.4)
legend(‘1-subarray’,’2-subarray’,’3-subarray’,’4-subarray’,’5-subarray’)
I think the RMSE should be lower as the subarray increases, but why is the RMSE reversed when the SNR is low?
plz help me…..clc; clear all; close all;

E_mp_a = [E_mp_a; E_mp(:,:,i)];
A_mp_a = [A_mp_a ;A_mp1(:,:,i)];

end

%% Hankel stack
Y_ss = A_mk(:,:,1);

if Ns> 1
for z = 2:Ns
Y_ss = [Y_ss ; A_mk(:,:,z)];
end
else
Y_ss = A_mk(:,:,1);
end

[U, S, V] = svd(Y_ss);
V0 = V(:, 1:K);
V1 = V0(1:end-1, :);
V2 = V0(2:end, :);

z_k = eig(pinv(V1) * V2);
find_theta_realarray_MPM(l,:)= asind((imag(log(z_k))*lambda)/(2*pi*dx))’;

rmse_real_mpm(l,:) = sort(theta) – sort(find_theta_realarray_MPM(l,:));
end

rmse_real_MPM_sum = sum(sum(abs(rmse_real_mpm).^2,1),2);
rmse_real_MPM_sum = sqrt(rmse_real_MPM_sum/(A*K));
rmse_real_MPM_sum1(SNR,Ns)= rmse_real_MPM_sum(rmse_real_MPM_sum ~= 0);
end
end

E_mp_a = [E_mp_a; E_mp(:,:,i)];
A_mp_a = [A_mp_a ;A_mp1(:,:,i)];

end

%% Hankel stack
Y_ss = A_mk(:,:,1);

if Ns> 1
for z = 2:Ns
Y_ss = [Y_ss ; A_mk(:,:,z)];
end
else
Y_ss = A_mk(:,:,1);
end

[U, S, V] = svd(Y_ss);
V0 = V(:, 1:K);
V1 = V0(1:end-1, :);
V2 = V0(2:end, :);

z_k = eig(pinv(V1) * V2);
find_theta_realarray_MPM(l,:)= asind((imag(log(z_k))*lambda)/(2*pi*dx))’;

rmse_real_mpm(l,:) = sort(theta) – sort(find_theta_realarray_MPM(l,:));
end

rmse_real_MPM_sum = sum(sum(abs(rmse_real_mpm).^2,1),2);
rmse_real_MPM_sum = sqrt(rmse_real_MPM_sum/(A*K));
rmse_real_MPM_sum1(SNR,Ns)= rmse_real_MPM_sum(rmse_real_MPM_sum ~= 0);
end
end

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