Explore MATLAB source code curated for "雷达数据处理" with clean implementations, documentation, and examples.
Source code implementation of the Probabilistic Data Association Filter (PDAF) algorithm from He You's "Radar Data Processing and Applications," featuring multi-target tracking with measurement association probabilities and Kalman filter integration.
The application context of target tracking involves radar data processing, where radar systems detect targets and record their positional data. The measured target position data (referred to as plots) are processed to automatically form trajectories and predict target positions at subsequent time steps. Implementation typically involves data association algorithms and state estimation techniques to handle measurement uncertainties and target dynamics.
The application background of target tracking problems lies in radar data processing, where radar systems detect targets and record position measurements (known as plots), then process these measurements to automatically form target trajectories and predict future positions. This article briefly discusses using Kalman filtering method for single target trajectory prediction, with MATLAB simulation tools employed to evaluate experimental performance. The trajectory.m file generates theoretical trajectories and visualization plots, while Kalman_filter.m implements Kalman filtering algorithm for target trajectory estimation. The filter_result.m file provides Kalman filtering estimation results along with mean error and standard deviation curves for performance analysis.
The target tracking problem finds its application background in radar data processing, where radar systems detect targets, record position data (called plots), process these measurements to automatically form tracks, and predict target positions at the next time step. This study briefly discusses using Kalman filtering for single-target track prediction and evaluates experimental results through MATLAB simulation. The package includes three source code files and an experimental report containing detailed algorithm analysis and scenario assumptions with code implementation insights.
The application background of target tracking lies in radar data processing, where radar systems detect targets, record positional data (called plots), and automatically form tracks while predicting targets' future positions. This article briefly discusses using Kalman filtering for single-target trajectory prediction and evaluates experimental results through MATLAB simulation. The implementation includes state-space modeling, prediction-correction cycles, and performance metrics calculation using MATLAB's built-in functions like "kalman" or custom implementations with matrix operations for state estimation.