Bearing only Tracking in Cluttered Environment using Seismic Sensors

This paper presents a study of the problem of tracking a single moving ground target using true seismic data provided by two seismic sensors in cluttered environment. An extended Kalman filter formulated for the bearing-only-tracking applications is used in estimating the target track. Nearest Neighbor (NN) and probabilistic data association filter (PDAF) algorithms are used to solve the data association problem. Results reveal that both algorithms provide almost the same behavior in low and medium clutter densities. In heavy clutter densities PDAF algorithm is much more superior to NN algorithm.