Investigation of Weighted Least Squares Methods for Multitarget Tracking with Multisensor Data Fusion

Target localization in a wireless sensor network (WSN) has received more and more attention in recent years, and has promoted many new applications due to the low cost, low bandwidth, low energy consumption, and collision avoidance mechanism. How to provide accurate location information has always been a hot research topic in 5G/B5G application scenarios. In this paper, the path loss information or received signal strength (RSS) of the received signal is considered in a WSN for the extended Kalman filter (EKF) to realize trajectory tracking of multiple targets, and the tracked targets are then localized through multiple sensors. Moreover, since there may be several objects or clutter interference in the communication environment, in order to reduce the impact of interference, we consider the probabilistic data association filter (PDAF) or probability hypothesis density filter (PHDF) to improve the tracking performance. Each sensor sends the received distance estimation information to the fusion center (FC), which calculates the optimal position for each target. Through simulation results, the proposed weighted least squares (WLS) trilateration method in this paper can effectively improve the average root mean squared error (RMSE) performance as sensors are evenly distributed around the tracking trajectories.