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Multiobserver Trajectories Optimization Based on APSO Algorithm in Underwater BOT System
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AI
DOI：10.1109/JSEN.2024.3493935.png)
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Abstract
En 中文
Aiming at solving the problem of underwater passive bearing-only tracking (BOT) system, it is difficult to judge the rule of bearing change, which leads to the slow convergence or low tracking accuracy with the predetermined unmanned underwater vehicle's (UUV) route. In this article, the observer's model is based on the UUV's kinematic model. Then, the underwater multiobserver BOT system is designed. To improve the collaborative tracking performance, the recursive form of Fisher information matrix (FIM) is deduced and introduced as the optimal index for multi-UUV trajectories' planning. From the perspective of UUV's kinematic properties, the mathematical algorithm based on gradient method is proposed. In addition, the adaptive particle swarm optimization (APSO) algorithm is designed with four operators which could balance the process of exploration and exploitation adaptively. Finally, the Monte Carlo simulation proves the effectiveness of these two proposed algorithms. Sea trial demonstrates the robustness and adaptability. The tracking performance has been improved through APSO algorithm even with small-angle maneuvering.
Keywords:
Observers
Target tracking
Observability
Chatbots
Sensors
Cost function
Trajectory optimization
Radar tracking
Sonar
Mathematical models
Bearing-only
evolutionary computing
observers' trajectories optimization
target tracking
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W