Abstract
The issue of real-time state estimation in passive target tracking using only bearing measurements is addressed in this research. An algorithm has been developed to detect multiple targets and fuse state vectors when a single target is detected. Target detection is carried out considering the state vectors from two different sensor arrays with various noise variances. The algorithm is evaluated against targets having unique and identical parameters such as range, speed and course. The state vectors are determined using three different filtering techniques, namely, extended Kalman filter (EKF), modified gain bearings-only EKF and unscented Kalman filter. Using the MATLAB software environment, Monte-Carlo simulations are conducted to more precisely assess algorithm performance.
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