Research for underwater target tracking by using multi-sonar

Abstract

Aim at the underwater target tracking by using multi-sonar, underwater target motion model and sonar observation model are proposed considering the measurement properties of different sonar and the target maneuvering characteristics. Adaptive Extended Kalman Filter (AEKF) based on current statistical model is proposed to improve the performance of underwater maneuvering target positioning and tracking. The AEKF approach is using new observational data to correct the signal model and noise statistics to maintain optimal filter and inhibit the filter divergence phenomenon. As to track fusion for multi-sonar, application of interacting multiple model (IMM) approach is presented. The IMM can be a self-adjusting variable bandwidth filter to estimate the state of a dynamic system. The simulation result presents that AEKF is good at maneuvering target tracking, meanwhile track fusion of Multi-Sonar is fit well for the real.