Explore MATLAB source code curated for "信号处理" with clean implementations, documentation, and examples.
MATLAB simulation of radar Linear Frequency Modulated (LFM) signals with verified implementation, suitable for beginners in radar signal processing with detailed code-level explanations
Wavelet Transform Threshold Denoising Method, first proposed by Professors Johnstone and Donoho in 1992, is a nonlinear denoising technique. It achieves near-optimal performance in terms of minimum mean square error while featuring the simplest implementation and minimal computational complexity. The core principle: orthogonal wavelet decomposition provides time-frequency localization, where signal components exhibit larger wavelet coefficients while noise distributes uniformly across high-frequency bands. Implementation involves threshold selection, coefficient shrinkage, and signal reconstruction - typically implemented using soft/hard thresholding functions in wavelet toolkits.
Beamforming, also known as spatial filtering, is a signal processing technique used with sensor arrays for directional signal transmission or reception. This technique involves combining phased array elements in a way that signals at specific angles experience constructive interference while experiencing destructive interference at other angles. Beamforming can be used at both transmit and receive ends to achieve spatial selectivity. Compared to omnidirectional patterns, the enhancement in reception/transmission is referred to as receive/transmit gain (or loss). Beamforming can be implemented with radio waves or acoustic waves, finding applications in radar, sonar, seismology, wireless communications, radio astronomy, acoustics, and biomedical fields. Adaptive beamforming represents
PAST (Projection Approximation Subspace Tracking) algorithm implementation for signal processing applications including blind source separation, featuring subspace tracking with recursive updates and eigenvalue decomposition
Signal processing implementation involving data visualization, FFT transformation, integration, peak detection and display
A modern signal processing program implementing an enhanced version of the basic ESPRIT algorithm, featuring statistical simulation experiments for signal frequency estimation with code implementation details.
Hilbert transform implementation function for processing time-domain signals to extract phase variations, with detailed code-approach explanations
A compact MATLAB application for signal processing operations, featuring Fourier Transform and Inverse Fourier Transform implementations with algorithmic explanations
Signal Processing - Filter Design (MATLAB and Mathematica-Based Approaches) featuring original implementations for multiplier-free elliptic IIR filter design with algorithm explanations.
Comprehensive guide to spectrum estimation techniques in MATLAB for modern signal processing applications, including code implementation details and algorithm explanations