Robust Kalman filter‐based decentralised target search and prediction with topology maps

Abstract

A novel distributed approach for searching and tracking of targets is presented for sensor network environments in which physical distance measurement using techniques such as signal strength is not feasible. The solution consists of a robust Kalman filter combined with a non-linear least-square method, and maximum likelihood topology maps. The primary input for estimating target location and direction of motion is provided by time stamps recorded by the sensor nodes when the target is detected within their sensing range. An autonomous robot following the target collects this information from sensors in its neighbourhood to determine its own path in search of the target. While the maximum likelihood topology coordinate space is a robust alternative to physical coordinates, it contains significant non-linear distortions when compared with physical distances between nodes. The authors overcome this using time stamps corresponding to target detection by nodes instead of relying on distances. The performance of the proposed algorithm is compared with recently proposed pseudo gradient algorithm based on hop count and received signal strength. Even though the proposed algorithm does not depend on distance measurements, the results show that it is able to track the target effectively even when the target changes its moving pattern frequently.