Received signal strength-based sensor localization in spatially correlated shadowing

Vaghefi, Reza Monir; Buehrer, R. Michael

doi:10.1109/icassp.2013.6638425

Cited by 31 publications

(30 citation statements)

References 23 publications

(70 reference statements)

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“…This minimum distance is called far-field distance [57]. Far-field distance, , is given by (56) where and represent the largest linear dimension of antenna and carrier wavelength respectively. Now, far-field distance for the Crossbow motes can be computed as in (57) for placing the receiving mote.…”

Section: B Experimental Results On Real-time Node Localization and Tmentioning

confidence: 99%

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An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

Kumar

Hegde

2015

IEEE Trans. Signal Process.

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In this paper, an efficient compartmental model for real-time node tracking over cognitive wireless sensor networks is proposed. The compartmental model is developed in a multi-sensor fusion framework with cognitive bandwidth utilization. The multi-sensor data attenuation model using radio, acoustic, and visible light signal is first derived using a sum of exponentials model. A compartmental model that selectively combines the multi-sensor data is then developed. The selection of individual sensor data is based on the criterion of bandwidth utilization. The parameters of the compartmental model are computed using the modified Prony estimator, which results in high tracking accuracies. Additional advantages of the proposed method include lower computational complexity and asymptotic distribution of the estimator. Cramer-Rao bound and elliptical error probability analysis are also discussed to highlight the advantages of the compartmental model. Experimental results for real-time node tracking in indoor environment indicate a significant improvement in tracking performance when compared to state-of-the-art methods in literature.

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Section: B Experimental Results On Real-time Node Localization and Tmentioning

confidence: 99%

“…To account for spatial correlation due to shadow fading [56] in an indoor environment, following model of covariance matrix can be used (55) where denotes the covariance matrix between th anchor and th node.…”

Section: A Experimental Conditionsmentioning

confidence: 99%

An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

Kumar

Hegde

2015

IEEE Trans. Signal Process.

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“…, N. It is known from Equation (3) that Q can be transformed into white noise using the Cholesky decomposition as Q = BB T with lower-triangular matrix B. This implies a linear transformation n = Bw, where w ∼ N (0, σ 2 I N ), and I N is the order-N unity matrix [17]. Let P = (P 1 , P 2 , · · · , P N ) T ∈ R N be the vector of observations given by Equation (1).…”

Section: The System Modelmentioning

confidence: 99%

“…The figure shows that |ν| 1 in this case. Therefore, when the absorption coefficient α f is sufficiently small, it is reasonable to approximate Equation (17) by using the first-order Taylor expansion about ν = 0 as follows:…”

Section: Target Localization In the Unknown Transmit Power Casementioning

confidence: 99%

Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements

Chang

et al. 2018

Applied Sciences

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This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer-Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment.

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“…, g is the path loss exponent (PLE), and in this article, we assume that the propagation with respect to all anchor nodes can be accurately modeled using the same PLE g which usually varies between 2 and 4 according to different propagation environment, 19 let n = (n 1 , n 2 , . .…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Received signal strength–based target localization under spatially correlated shadowing via convex optimization relaxation

Chang

Wang

et al. 2018

International Journal of Distributed Sensor Networks

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Received signal strength-based target localization methods normally employ radio propagation path loss model, in which the log-normal shadowing noise is generally assumed to follow a zero-mean Gaussian distribution and is uncorrelated. In this article, however, we represent the simplified additive noise by the spatially correlated log-normal shadowing noise. We propose a new convex localization estimator in wireless sensor networks by using received signal strength measurements under spatially correlated shadowing environment. First, we derive a new non-convex estimator based on weighted least squares criterion. Second, by using the equivalence of norm, the derived estimator can be reformulated as its equivalent form which has no logarithm in the objective function. Then, the new estimator is relaxed by applying efficient convex relaxation that is based on second-order cone programming and semi-definite programming technique. Finally, the convex optimization problem can be efficiently solved by a standard interior-point method, thus to obtain the globally optimal solution. Simulation results show that the proposed estimator solves the localization problem efficiently and is close to Cramer-Rao lower bound compared with the state-of-the-art approach under correlated shadowing environment.

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Received signal strength-based sensor localization in spatially correlated shadowing

Cited by 31 publications

References 23 publications

An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements

Received signal strength–based target localization under spatially correlated shadowing via convex optimization relaxation

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