Kalman Filter-Based Target Tracking Algorithm for Wireless Sensor Networks

In this research, we present a target tracking algorithm for wireless sensor networks based on Kalman filtering. The algorithm implements three distinct Kalman filtering approaches: Standard Kalman Filter (KF), Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF). The standard Kalman filter method operates as a linear filtering technique, ideal for processing linear systems with Gaussian noise. The Extended Kalman Filter extends this capability to nonlinear systems through first-order Taylor series linearization of the system model. The Unscented Kalman Filter represents an enhanced version of EKF that utilizes unscented transformation to approximate nonlinear functions without direct linearization, providing better performance for highly nonlinear systems. In practical implementation, the KF algorithm typically involves prediction and update steps with state transition matrices, while EKF requires Jacobian matrix calculations for linearization, and UKF employs sigma points propagation through nonlinear functions. This research aims to improve the accuracy and stability of target tracking in wireless sensor networks, offering valuable reference material for researchers in related fields through comprehensive algorithm comparisons and implementation insights.