Enhanced Kalman Filter Algorithm using the Invariance Principle

Target tracking in multistatic active sonar systems is often limited in shallow-water environments due to the high level of bottom reverberation that produces false detections. Past research has shown that these false alarms may be mitigated when complete knowledge of the environment is available for discrimination, but these methods are not robust to environmental uncertainty. Recent work has demonstrated the existence of a waveguide invariant for active sonar geometries. Since this parameter is independent of specifics of the environment, it may be used when the environment is poorly known. In this paper, the invariance extended Kalman filter (IEKF) is proposed as a new tracking algorithm that incorporates dynamic frequency information in the state vector and uses the invariance relation to improve tracker discrimination. IEKF performance is quantified with both simulated and experimental sonar data and results show that the IEKF tracks the target better than the conventional extended Kalman filter (CEKF) in the presence of false detections