Hi @Jason,
For SAR simulations, speckle noise is typically the main issue since it's multiplicative, meaning it scales with your signal rather than being added on top of it. So, you’d want to apply it before any RDA processing. A simple way to simulate it in MATLAB is by multiplying your raw data (`s_raw`) by some random noise factor.
Here’s how you can do it:
speckle_std_dev = 0.2; % Controls the noise strength speckle_noise = 1 + speckle_std_dev * randn(size(s_raw)); % Random noise s_raw_with_speckle = s_raw .* speckle_noise; % Apply the speckle noise
You can tweak `speckle_std_dev` to adjust how much noise you want. It’s basically controlling the noise level.
Now, when you’re done with your RDA and you have the final processed data (`s_final`), you’ll probably want to add AWGN (Additive White Gaussian Noise), which is additive and affects the signal after processing. This would be the type of noise you’d usually apply to your final image to simulate sensor or environmental noise. MATLAB's `awgn` function is perfect for this:
s_final_with_awgn = awgn(s_final, SNR_dB); % Add AWGN after RDA
Just set `SNR_dB` to the level of noise you want to introduce. If you know the actual signal power, you can also specify that:
s_final_with_awgn = awgn(s_final, SNR_dB, signalpower);
To combine both types of noise, you would first add speckle noise to the raw data before processing:
speckle_noise = 1 + speckle_std_dev * randn(size(s_raw)); s_raw_with_speckle = s_raw .* speckle_noise;
And then, after processing through RDA, apply AWGN to the final result:
s_final_with_awgn = awgn(s_final, SNR_dB);
This way, you’ve got both the multiplicative speckle noise affecting your raw data and the additive AWGN impacting the final processed data.
Also, if you want your noise to be repeatable across different simulation runs, set the random seed using `rng(seed)` before you call `awgn`. That way, you’ll get the same noise pattern every time.
Hope that clears it up! Let me know if you run into any issues or have more questions.
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