Expected a string scalar or character vector for the parameter name

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Sonia
Sonia 2023-12-17
回答： Brahmadev 2023-12-27
Ran in:
% Set parameters
f_c = 0.002; % Hz, sinusoidal signal frequency
theta_range = [-pi, pi]; % radians, phase range
T = 1000; % seconds, signal duration
A = sqrt(2); % amplitude for unit average energy
No = 1; % power spectral density of noise
M = 500; % number of realizations for averaging
% Generate time vector
t = linspace(0, T, 10001);
% Autocorrelation function initialization
R_x_tau = zeros(1, length(t));
% Loop for M realizations
for i = 1:M
% Generate random phase
theta = theta_range(randi(2))*pi;
% Generate sinusoidal signal
x_t = A*cos(2*pi*f_c*t + theta);
% Generate white Gaussian noise
noise = randn(size(t)) * sqrt(No/2);
% Generate signal with noise
x_t_noise = x_t + noise;
end
% Loop for different tau values
for tau = 1:length(t)
% Calculate product for current tau
product_tau = x_t_noise(tau + 1:end) .* x_t_noise(1:end-tau);
% Calculate and accumulate average product
R_x_tau(tau) = R_x_tau(tau) + 1/M * mean(product_tau);
end
% Calculate theoretical autocorrelation function
R_x_theo = 2*A^2*cos(2*pi*f_c*t) + 2*No*delta(t);%%%%%%%%%%%%%%%%%%%%ERROR
delta requires RF PCB Toolbox.
% Plot results
figure;
subplot(211);
plot(t, x_t_noise);
xlabel('Time (s)');
ylabel('Signal with Noise');
title('Sinusoidal Signal with White Gaussian Noise');
subplot(212);
plot(t, R_x_tau);
hold on;
plot(t, R_x_theo);
legend('Estimated Autocorrelation', 'Theoretical Autocorrelation');
xlabel('Time (s)');
ylabel('Autocorrelation Function');
title('Autocorrelation Function of Signal with Noise');
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回答（1 个）

Brahmadev
Brahmadev 2023-12-27
Hi @Sonia,
I understand that you would like to calculate the autocorrelation using the Dirac Delta function, you can use the 'dirac' function. Please refer to the documentation below for more information.
https://www.mathworks.com/help/symbolic/sym.dirac.html
Hope this helps in resolving your query!

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