Explore MATLAB source code curated for "功率谱估计" with clean implementations, documentation, and examples.
A MATLAB implementation of power spectrum estimation for multi-frequency sinusoidal signals using autoregressive (AR) modeling with Levinson algorithm, optimal order determination, and comparison with built-in functions.
A. Generate a 1000-length binary random sequence with P(0)=0.8 and P(1)=0.2; B. Apply Return-to-Zero AMI encoding (50% pulse width, 8x symbol rate sampling), plot waveforms for first 20 symbols; C. Implement HDB3 coding and plot corresponding waveforms; D. Switch to Miller coding and visualize waveforms; E. Perform power spectrum estimation for all 1000 symbols; F. Observe AMI power spectrum variations with changing source probabilities. Includes MATLAB implementation details for random sequence generation, coding algorithms, and spectral analysis.
Original algorithm implementation featuring six different power spectrum estimation techniques including FFT-based method, Periodogram, Welch's method, Short-Time Fourier Transform, parametric power spectrum estimation, and adaptive parametric approach. Each method includes corresponding visualization plots for comparative analysis, making this an excellent tool for learning signal processing and spectrum analysis. Available for download and study!
MUSIC algorithm for spatial spectrum estimation and DOA (Direction of Arrival) analysis: A power spectrum estimation method based on matrix eigenvalue decomposition comprising two non-parametric estimation approaches - eigenvector estimation and MUSIC estimation. Eigenvector estimation is primarily suitable for power spectrum estimation of sinusoidal signals contaminated with white noise, while MUSIC estimation is more appropriate for general sinusoidal signal parameter estimation. The algorithm implementation involves covariance matrix computation, eigenvalue decomposition, and noise subspace identification for high-resolution spectral estimation.
MUSIC Algorithm for Power Spectral Estimation via Autocorrelation Matrix Decomposition