Matrix Factorization-Based Target Localization via Range Measurements With Uncertainty in Transmit Power

Mei, Xiaojun; Wu, Huafeng; Xian, Jiangfeng

doi:10.1109/lwc.2020.2998609

Cited by 22 publications

(14 citation statements)

References 21 publications

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“…In this section, several numerical experiments are conducted in different scenarios to evaluate the performance of IPM-MM. To verify the effectiveness of our proposed method, we compare it with LS [ 19 ], SRWLS [ 20 ], ASM [ 33 ], RNLA [ 28 ], and CRLB in MATLAB under 3-Dimension. Due to the ocean environment dynamics, we assume that the target and anchor nodes’ positions are not fixed at a cube area with side length

in each Monte Carlo trial.…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

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

Zhang

Mei

et al. 2021

Sensors

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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.

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in each Monte Carlo trial.…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

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

Zhang

Mei

et al. 2021

Sensors

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“…Several state-of-the-art methods are discussed for the comparison in terms of the complexity in this part, i.e., weighted least square (WLS) with RSS-only in the non-cooperative scheme in [ 20 ], NWLS-K in [ 24 ], active set method (ASM) in [ 8 ], RNLA in [ 14 ], and unconstrained squared range majorization-minimization (USRMM) in [ 21 ]. It is noteworthy that the acquirement of an estimate of the target’s location via AMT is by the bisection principle.…”

Section: Proposed Methods (Amt)mentioning

confidence: 99%

“…In this section, a set of numerical simulations are carried out in Matlab to assess the proposed method, compared with WLS [ 20 ], NWLS-K [ 24 ], ASM [ 8 ], USRMM [ 21 ], RNLA [ 14 ], and CRLB conducted in (26) in different scenarios. It should be noted that the positions of anchors and the target are not fixed due to the dynamics of the currents.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…As an efficient tool for exploration, underwater wireless sensor networks (UWSNs) have played a pivotal role in a vast number of scientific or commercial missions in civilian and military applications [ 2 , 3 , 4 , 5 , 6 , 7 ]. It is worth noting that the data collected by sensors are meaningful only when the latter are geo-referenced [ 8 ]. However, it is infeasible to obtain the location information because traditional localization methods that rely on GPS techniques cannot be utilized in the underwater environment [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…The general system architecture of UWSNs is shown in Figure 1 . With the knowledge of some internode distances (ranges), the targets’ position would be determined, and thus a considerable body of research has been investigated in the literature regarding localization in UWSNs [ 4 , 6 , 7 , 8 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Absorption Mitigation Technique for Received Signal Strength-Based Target Localization in Underwater Wireless Sensor Networks

Mei

Saeed

et al. 2020

Sensors

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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.

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Real-time RSS-based target localization for UWSNs using an IDE-BP neural network

Zhang,

Wu,

Gulliver

et al. 2024

J Supercomput

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Matrix Factorization-Based Target Localization via Range Measurements With Uncertainty in Transmit Power

Cited by 22 publications

References 21 publications

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

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

An Absorption Mitigation Technique for Received Signal Strength-Based Target Localization in Underwater Wireless Sensor Networks

Real-time RSS-based target localization for UWSNs using an IDE-BP neural network

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