Explore MATLAB source code curated for "杂波" with clean implementations, documentation, and examples.
This program implements space-time adaptive processing (STAP) principles to simulate 2D clutter patterns, computes the clutter covariance matrix, and visualizes the 2D clutter spectrum through comprehensive signal processing operations.
MATLAB-based CFAR program implementation for effective separation of target signals from background clutter in radar systems
This program simulates sea and ground clutter where echo amplitudes follow a log-normal distribution under conditions of specified resolution and low grazing angles, implementing statistical modeling and radar signal processing techniques.
MATLAB code implementation for target tracking simulation, including measurement generation, noise and clutter modeling, as well as various target motion dynamics with detailed algorithmic explanations
This radar clutter simulation program generates Weibull-distributed amplitude clutter with Gaussian power spectrum, implemented in time-domain I/Q channels representing real and imaginary components respectively.
Simulation of target echo signals for Doppler radar with integrated noise and clutter modeling
MTI Radar (Moving Target Indicator Radar) enables clutter suppression and target signal preservation. This work provides MATLAB simulation source code analyzing single-delay-line MTI canceller frequency response, double-delay-line MTI canceller frequency response, blind speed mitigation through staggered PRF techniques, and comprehensive evaluation of how different stagger ratios impact blind speed performance in radar systems.
Radar Clutter Simulation Program Generates Range-Doppler Maps with Signal Processing Implementation
Full-degrees-of-freedom space-time adaptive processing elucidates the spatiotemporal relationships among clutter, interference, and signals, involving techniques like covariance matrix estimation, adaptive weight calculation, and filtering implementation.
This program implements SAR image segmentation using Constant False Alarm Rate (CFAR) detection to separate targets from clutter background with improved accuracy through adaptive thresholding techniques.