MATLAB Implementation of Kalman Filter for Passive Localization Algorithm Research

This article explores a passive localization algorithm implemented using Kalman filter techniques. In passive localization systems, sensors detect target object positions, but environmental interference often leads to inaccurate positioning. To address this challenge, we employ Kalman filters to process sensor data, reducing errors and improving localization accuracy. The article provides a detailed explanation of Kalman filter working principles and their application in passive localization systems. Additionally, we demonstrate MATLAB implementation of this algorithm using key functions like kalman for state estimation and predict/correct methods for recursive filtering. The implementation includes sensor data preprocessing, state-space modeling, and measurement update procedures. Sample code examples are provided to illustrate practical implementation aspects, covering initialization parameters, covariance matrix configuration, and real-time filtering workflows. This comprehensive guide aims to enhance understanding of both Kalman filter mechanics and passive localization algorithm applications in practical scenarios.