Abstract
Localization is an indispensable technology for underwater wireless sensor networks (UWSNs). In what concerns UWSNs, the accurate location information is not only the requirement of the marine field applications but also the basis of the other corresponding research, for instance, network routing and topology control. Recently, an astonishing surge of interest has been drawn in the received signal strength (RSS)-based scheme due to cost-effectiveness and synchronization-free compared with others. However, unlike the terrestrial wireless sensor networks (WSNs), the acoustic signal may suffer the absorption loss in the underwater environment besides the path loss, which degrades the localization accuracy and limits the capability of the RSS-based technology in UWSNs. In this context, a robust localization method with an absorption mitigation technique (AMT) is developed. First, an RSS-based analytically tractable measurement model is conducted, where the maximum likelihood estimator (MLE) is derived. Nevertheless, it is quite challenging to solve the problem using MLE under a non-convex expression. Therefore, by exploiting certain approximations, the considered localization problem is converted into an optimization expression with a maximum absorption loss involved. A min–max strategy is then presented, with which the problem is turned to minimize the worst situation of the absorption loss. After a simple manipulation, the problem is further investigated as a generalized trust region sub-problem (GTRS) framework. Although the GTRS is a non-convex scheme, the solution can be obtained through an iteration method by introducing a multiplier. In addition, the closed-form expression of the Cramer–Rao lower bound (CRLB) of the analytically tractable measurement model is derived. Numerical simulations demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches in different scenarios.
Highlights
The ocean is vast, covering 140 million square miles, some 72 percent of the Earth’s surface, one of the most valuable natural resources that attract people to explore [1]
The considered localization problem is reshaped to a generalized trust region sub-problem (GTRS) framework via a set of tight approximations for small noise powers
A min–max strategy is presented to minimize the worst situation for the absorption, wherein the problem is divided into two subproblems and jointly solved by a bisection method
Summary
The ocean is vast, covering 140 million square miles, some 72 percent of the Earth’s surface, one of the most valuable natural resources that attract people to explore [1]. As an iterative method via first-order Taylor linearization approximation, the localization accuracy of MABL may not be guaranteed if the quality of the initial guess is terrible In this case, the authors in [24] have proposed an initial guess-free method, which converted the considered localization problem into a generalized trust-region subproblem (GTRS). The same transformation strategy has been developed in [11], different from [24], the authors transformed the original problem into a mixed semidefinite programming/second-order cone programming (SD/SOCP) problem for reaching an efficient solution Both NWLS in [24] and SD/SOCP in [11] were investigated under the low transmission frequency with a relatively small absorption loss. The authors in [19] have investigated the localization error caused by inhomogeneous underwater medium and presented an oversampled matched filter-based RSS localization method (OSMF-RSS) under a low transmission frequency. (2) The considered localization problem is converted into an optimization by exploiting
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