PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter

Abstract

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar

“…According to the target motion, different kinds of motion models can be utilized. For instance, a constant velocity motion model can be employed when the target velocity is constant [ 10 , 26 ]; constant acceleration motion model can be used when the target has a uniformly variable velocity [ 10 ]; ellipsoidal Earth model is generally utilized to precisely describe the gravity of the Earth for space target tracking [ 31 ], etc.…”

“…Inspired by the DOA tracking method [ 24 ] and the multiple sensors tracking method [ 25 , 26 , 27 , 28 , 29 , 30 ], this paper proposes a second probability data association filter (SePDAF)-based tracking method. It uses the unambiguous angle and ambiguous angles as measurements, then twice applying filtering, i.e., EKF and SePDAF, to achieve the high accuracy unambiguous filtering estimate and stable trajectory simultaneously.…”

A Nonlinear Double Model for Multisensor-Integrated Navigation Using the Federated EKF Algorithm for Small UAVs

“…However, the Kalman filter, which is only applicable to linear Gaussian systems, is no longer optimal. Thus, a few improved nonlinear filter algorithms [22] have been proposed by certain scholars, such as the extended Kalman filter (EKF) [23], the unscented Kalman filter (UKF) [24,25], cubature Kalman filter (CKF) [26], particle filter (PF) [27], etc. In order to solve the parameter divergence caused by the strapdown inertial navigation system (SINS)/GPS model error, a quadratic EKF algorithm was proposed by Wang and Liu [28].…”

Improved Multitarget Tracking in Clutter Using Bearings-Only Measurements

“…To alleviate the computational load, the linear multitarget integrated probability data association (LMIPDA) [ 3 ] is then proposed to modulate the clutter measurement density by considering the possible contributions of targets being followed by other tracks, in which the numerical complexity is linear in the number of targets and the number of measurements. Multiple hypothesis tracker (MHT) [ 2 , 8 , 9 ] is another classical tracker for multitarget tracking in clutter. Though many versions of MHT have been developed, most of them can be divided into two categories: track-oriented and measurement-oriented.…”