A three dimensional tracking scheme for underwater non-cooperative objects in mixed LOS and NLOS environment

Underwater positioning and tracking scheme for non-cooperative objects is of great essence to explore unknown fields. Due to the high response time and non-line-of-sight(NLOS) propagation in the underwater acoustic sensor networks (UASNs), the existed range-based 3D target tracking algorithms are generally inaccurate on detecting underwater non-cooperative objects. In order to solve the problems above, the corresponding solutions are presented respectively in this paper. Although it is hard to change the inherent property of the underwater acoustic propagation, reducing the communication time is another way to solve the problem indirectly. Since the ranging phase and synchronize phase occupy most of the communication time, the presented novel ranging scheme for non-cooperative objects reduces the redundant time consumption, and further eliminates the necessity of synchronization process in advanced. For NLOS propagation, a distributed residual weighting discrimination (DRWD) algorithm based on grouping strategy is proposed for non-cooperative objects. The position estimations of the groups containing the NLOS link error are always distributed in isolation, and the estimations without the NLOS link errors are always concentrated in a small range. According to this feature, a low computational complexity approach namely two-step least square (LS) is proposed to determine the best location by analyzing the distribution of estimated coordinates. Meanwhile, a parameterized selection strategy is proposed first time to evaluate the construction of reference nodes in 3D target tracking. We provide a mathematical proof for our strategy, which avoids the ambiguity occurrence caused by the distribution of reference nodes. The new scheme provided for underwater acoustic tracking (UWAT) greatly improves the positioning accuracy in mixed LOS/NLOS environment. At the end of the paper, simulations are illustrated to evaluate and validate the algorithmic superiority and effectiveness.