Two-Phase Robust Target Localization in Ocean Sensor Networks Using Received Signal Strength Measurements

Abstract

Target localization plays a vital role in ocean sensor networks (OSNs), in which accurate position information is not only a critical need of ocean observation but a necessary condition for the implementation of ocean engineering. Compared with other range-based localization technologies in OSNs, the received signal strength (RSS)-based localization technique has attracted widespread attention due to its low cost and synchronization-free nature. However, maintaining relatively good accuracy in an environment as dynamic and complex as the ocean remains challenging. One of the most damaging factors that degrade the localization accuracy is the uncertainty in transmission power. Besides the equipment loss, the uncertain factors in the fickle ocean environment may result in a significant deviation between the standard rated transmission power and the usable transmission power. The difference between the rated and actual transmission power would lead to an extra error when it comes to the localization in OSNs. In this case, a method that can locate the target without needing prior knowledge of the transmission power is proposed. The method relies on a two-phase procedure in which the location information and the transmission power are jointly estimated. First, the original nonconvex localization problem is transformed into an alternating non-negativity-constrained least square framework with the unknown transmission power (UT-ANLS). Under this framework, a two-stage optimization method based on interior point method (IPM) and majorization-minimization tactic (MMT) is proposed to search for the optimal solution. In the first stage, the barrier function method is used to limit the optimization scope to find an approximate solution to the problem. However, it is infeasible to approach the constraint boundary due to its intrinsic error. Then, in the second stage, the original objective is converted into a surrogate function consisting of a convex quadratic and concave term. The solution obtained by IPM is considered the initial guess of MMT to jointly estimate both the location and transmission power in the iteration. In addition, in order to evaluate the performance of IPM-MM, the Cramer Rao lower bound (CRLB) is derived. Numerical simulation results demonstrate that IPM-MM achieves better performance than the others in different scenarios.

A review of surface acoustic wave sensors: mechanisms, stability and future prospects

“…They have become the teleneuron of robots. Smart wireless sensor networks (WSNs) are the whole neural networks of intelligent applications (Zhang et al, 2022a(Zhang et al, , 2022bZhang et al, 2021aZhang et al, , 2021bWu et al, 2020;Wu et al, 2022). IoT connect all systems for communications and intelligent controls (Suresh et al, 2022;Chen et al, 2018).…”

Novel High-Precision and High-Robustness Localization Algorithm for Underwater-Environment-Monitoring Wireless Sensor Networks

“…Underwater environmental characteristics such as absorption loss are not considered IPM-MM [32] Joint estimation of location and transmission power High computational complexity TP-TSFLA [33] No time synchronization required; using mobile beacons to assist in localization Absorption loss and the curvilinear propagation of hydroacoustic sound are not taken into account PCCP [34] Reduces the impact of hydroacoustic curve propagation…”

“…Therefore, the computational complexity of our NHHL algorithm is O( √ N log(Nθ/c) + τN). Table 4 gives the computational complexity of the WLS [55], SR-LS [56], SR-IRLS [53], RNLA [57], IPM [33], and our NHHL algorithm. In complex and dynamic marine environments, although the computational complexity of our NHHL algorithm is high compared to the selected benchmark algorithms, it achieves the optimal localization accuracy within the acceptable complexity range.…”

“…To simulate the highly dynamic nature of the real marine environment, the locations of the anchor nodes and target nodes are randomly varied in each simulation. As in Figure 2, the motion range of the anchor node is supposed to be restricted to a circle with a radius of 10 m. Comparisons are made with the WLS [55], SR-LS [56], SR-IRLS [53], IPM [33], RNLA [57] and CRLB for different marine simulation scenarios. The RMSE was utilized to calibrate the localization accuracy, which can be expressed as:…”

“…To explore the 3D spatial localization of underwater nodes in non-line-of-sight scenarios, Sah D K et al [32] combined the RSS measurements and TOA measurements to achieve a reliable inter-node distance estimation, and then presented a localization error minimization algorithm to figure out the precise location. Zhang Y et al [33] developed an ocean target localization algorithm for the case of unknown transmission power, which transforms the original localization problem into a least squares framework using the IPM and majorization-minimization strategy for the conjoint estimation of location and transmission power. Luo J et al [34] presented a two-phase localization method which includes the distance-based estimation phase and distance-free evaluation phase.…”