Simulation Experiment for Signal Frequency Estimation
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Resource Overview
Three complex sinusoidal signals with SNR values of SNR1=30dB, SNR2=30dB, and SNR3=27dB were analyzed using an MVDR method based on singular value decomposition (SVD). The simulation employed 1000 signal samples and a 4-tap FIR filter to estimate the power spectral density function. The implementation involves covariance matrix construction, eigenvalue decomposition, and adaptive weight calculation for spectral estimation.
Detailed Documentation
Assuming three complex sinusoidal signals with signal-to-noise ratios of SNR1=30dB, SNR2=30dB, and SNR3=27dB respectively, we conducted simulation experiments for signal frequency estimation using the MVDR method based on singular value decomposition. The experiment configuration utilized 1000 signal samples and a 4-tap FIR filter structure. Through these simulations, we obtained estimations of the power spectral density function. The implementation typically involves computing the sample covariance matrix from the input signals, performing SVD to separate signal and noise subspaces, and applying MVDR weights to enhance frequency resolution while suppressing noise components.
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