Cognitive Continuous Tracking Algorithm for Centralized Multistatic Sonar Systems
Shuping Lu, Yang Chen, Fangxiang Chen, Feng Ding, Ranwei Li · 2021 OES China Ocean Acoustics (COA) · 2021
This paper proposes a novel detection and tracking algorithm to improve the performance of continuous tracking of submarine for multistatic sonar systems. The algorithm focuses on a centralized fusion architecture and a cognitive closed loop. In the following trail of submarine, the future trajectory of the submarine and its echo intensity for different transmit-receive combinations are roughly predicted, where the target echo model is assumed to be a priori. These predicted echo intensity is fed back to the frontend detection and tracking processes. Then the proposed algorithm could adaptively adjust the key parameters of the centralized fusion rule. Moreover, the track management strategy is also adjusted based on the feedback information. At the beginning of another cycle after tracking, the future trajectory and the echo intensity of the target are predicted again. We use numerical simulations to evaluate the behavior of the proposed algorithm. It is demonstrated that the cognitive approach achieves a better performance of continuous tracking compared with the conventional non-cognitive method in terms of track probability of detection and track fragmentation rate.