Tracking Multiple Objects Under Bearings-Only Measurements of Multiple Heterogeneous Sonar Sensors in Clutter Environments

This study handles tracking multiple objects under bearings-only measurements of multiple heterogeneous sonar sensors in cluttered underwater environments. In order to track multiple objects in clutter environments, we apply Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. In the GM-PHD filter, the Extended Kalman Filter (EKF) is applied, since the EKF can cope with non-linearity of bearings-only measurements. However, range uncertainty of the object is large for bearings-only measurements, which can generate poor performance in the GM-PHD. The bearing measurements of any two passive sonar sensors can intersect at one point, and the point provides the viable location of an object. Hence, if an intersection point of two bearing sensors satisfies the maximum sensing range constraint, then the point can generate an object sample in our GM-PHD Kalman filter. In addition, every object sample is updated under the measurement update step of the EKF. To the best of our knowledge, this paper is novel in tracking multiple objects in clutter, under bearings-only measurements of multiple heterogeneous sonar sensors. The efficacy of the proposed GM-PHD Kalman filter is demonstrated under MATLAB simulations.