A Direct Position Determination Approach for Underwater Acoustic Sensor Networks

In this paper, we consider the source localization problem with an underwater acoustic sensor network (UASN) consisting of a number of platforms each equipped with an array, such as an autonomous underwater vehicle (AUV). Direct position determination (DPD) has been a promising topic in recent years. Existing DPD methods can be divided into two categories: global coherent processing and non-coherent processing. The former is easy to implement in practical tasks but the coherence between the received signals at different nodes are not exploited, while the requirements for coherent processing may not be fulfilled in real environments. A multi-cluster array model is constructed considering the deployment of the nodes with the intention to exploit the available coherence in the UASN and avoiding the side effects of imperfect spatial coherence. A weighted MUSIC direct source localization approach is proposed based on this model. The arrays within a cluster work coherently as a large array and contribute a MUSIC spectrum. The final source position estimate is obtained from a weighted combination of these spectra, and the weights are dependent on the eigenstucture and noise variance of each cluster. We first investigate the theoretical gain of inter-array coherence exploitation on localization accuracy by means of Cramer-Rao bound analysis. Then the model for imperfect spatial coherence is generalized to the multi-cluster system to examine the performance of the proposed approach when the inherent constraints for intra-cluster coherent processing can not be guaranteed. Numerical results demonstrate the superior resolution capability of the proposed approach and its robustness to imperfect spatial coherence.