A clutter rejection filter for sonar feature based navigation in marine environments

This paper examines the detection of landmarks in the presence of false measurements from a blazed array sonar using random finite set models. A clutter rejection filter that is based on the fusion of the moment-approximation of the posterior density, also known as probability hypothesis density (PHD), within the random finite set framework with the conventional Extended Kalman Filter simultaneous localization and mapping (EKF-SLAM) framework is presented. The PHD clutter filter is effectively used to reduce false measurements, thereby feeding mainly landmark originated measurements to the EKF based navigational filter. This effectively simplifies the data association technique needed within the navigation filter framework and completely obviates the need for external map/feature management strategies. The efficacy of the proposed approach is demonstrated by controlled field experiments in marine environments using an autonomous surface craft (ASC) equipped with the navigational sensory suite, viz., GPS, triple axis gyroscope, Doppler Velocity Log (DVL) and a payload in form of an underwater blazed array sonar.