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

Abstract

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.

1. Introduction

With the increasing emphasis on marine resources and security, the Internet of Underwater Things (IoUT) has gradually started to take shape (Jahanbakht et al., 2021). As an early practical form of IoUT, underwater sensor networks (USNs) have been widely used in underwater resource exploration, marine environmental monitoring, and marine military applications (Alostad, 2020; Mohsan et al., 2022). Among them, target tracking technology based on USNs has become a research hotspot due to its stealth, low cost, and flexibility, and it has been applied in scenarios such as diver navigation, underwater search and rescue, and hostile target monitoring (Tang et al., 2024). Target tracking technology based on USNs faces two main challenges—the complex underwater acoustic environment, characterized by narrow bandwidth and significant transmission delays, which makes tracking difficult, and energy constraints on USN nodes, which cannot have their batteries replaced, resulting in a limited lifetime. The problem of limited bandwidth resources in underwater acoustic channel communication can be addressed by improving the communication bandwidth utilization rate. Zhang SL et al. (2017) quantized the sensor communication data to reduce the communication bandwidth occupation and improved the quantization error by selecting quantization bounds. Luo et al. (2019) further improved underwater target tracking performance by selecting different quantization bits for different sensor nodes. Zhang Q et al. (2015) derived the Cramér–Rao lower bound under quantized measurements and used it as an optimization objective to select the best fusion node to improve the tracking performance. To address the issue of transmission delays in underwater acoustic signals, Su et al. (2020) considered the impact of transmission delay of ranging echoes and proposed an unscented Kalman filter (UKF) fusion method based on delay estimation. On this basis, Zhang ZK et al. (2023) improved the calculation of transmission delay and proposed an improved asynchronous fusion method for particle filters.

Many studies have focused on improvements in the energy utilization of USNs. Li et al. (2025) used the topological structure and energy consumption of nodes as indicators to select neighboring nodes for tracking, thereby reducing energy consumption while meeting tracking accuracy requirements. Zhang D et al. (2019) studied the relationship between node transmission energy consumption and the Fisher information matrix (FIM), on the basis of which the energy of sensor nodes in a period of time is reasonably allocated. Tian and Zhang (2022) considered the errors caused by the positional uncertainty of USNs. Using the FIM, mutual information, and the number of nodes as objectives, they employed the non-dominated sorting genetic algorithm II (NSGA-II) and the technique for order preference by similarity to ideal solution (TOPSIS) to select sp...