Scholarlink

<p>Because underwater sensor networks (USNs) have limited energy resources due to environmental constraints, it is essential to improve energy utilization. For this purpose, an autonomous underwater vehicle (AUV) with greater onboard computation power is used to process measurement data, and the mobility of the AUV is leveraged to optimize the USN topology, enhancing tracking accuracy. First, to address the transmission delay of underwater acoustic signals, a time-delay compensated centralized extended Kalman filter (TD-CEKF) algorithm is proposed. Next, the mathematical relationship between AUV position and USN topology is established, based on which the optimization target is constructed. Subsequently, a penalty function is introduced to remove the constraints from the objective function, and the optimal AUV position is searched using the gradient descent method to optimize the USN topology. The simulation results demonstrate that the proposed algorithm can effectively overcome the influence of transmission delay on target tracking and achieve improved tracking performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Joint target tracking using an autonomous underwater vehicle and underwater sensor networks for underwater applications

  • 期刊论文

  • 发表日期：

    2025年8月1日

查看全文
  • Zhaohong Lv,
  • Zhenkai Zhang,
  • Boon-Chong Seet,
  • Yi Yang

摘要

Because underwater sensor networks (USNs) have limited energy resources due to environmental constraints, it is essential to improve energy utilization. For this purpose, an autonomous underwater vehicle (AUV) with greater onboard computation power is used to process measurement data, and the mobility of the AUV is leveraged to optimize the USN topology, enhancing tracking accuracy. First, to address the transmission delay of underwater acoustic signals, a time-delay compensated centralized extended Kalman filter (TD-CEKF) algorithm is proposed. Next, the mathematical relationship between AUV position and USN topology is established, based on which the optimization target is constructed. Subsequently, a penalty function is introduced to remove the constraints from the objective function, and the optimal AUV position is searched using the gradient descent method to optimize the USN topology. The simulation results demonstrate that the proposed algorithm can effectively overcome the influence of transmission delay on target tracking and achieve improved tracking performance.