Linear Least Squares Approach for Accurate Received Signal Strength Based Source Localization

So, Hing Cheung; Lin, Lanxin

doi:10.1109/tsp.2011.2152400

Cited by 216 publications

(138 citation statements)

References 17 publications

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“…Following [9] and [12], a second WLS step which utilizes the relationship between the unknown position x and extra variable R, can increase the estimation performance. Based on x 2 + y 2 = R and under sufficiently small noise conditions such that x ≈ [ θ] 1 and y ≈ [ θ] 2 , we can construct:…”

Section: Improved Versionmentioning

confidence: 99%

“…Maximum likelihood (ML) [4], semi-definite relaxation (SDR) [5], centroid [6] and weighted least squares (WLS) [7,8,9] approaches are the common solutions for source localization using RSS measurements. It has been shown in [4] that the positioning accuracy of the ML methodology attains Cramér-Rao lower bound (CRLB) at sufficiently small noise conditions.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the WLS approach converts the RSS measurements to a set of linear equations in the source position, resulting in a computationally simple solution. Assuming that the source transmit power is known, we have recently devised a WLS RSS-based positioning algorithm [9] whose performance can achieve the CRLB in the presence of white Gaussian disturbances. Nevertheless, to the best of our knowledge, there is no optimum WLS estimator for the more challenging scenario of unknown source transmit power in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Accurate and simple source localization using differential received signal strength

Lin

Chan

2013

Digital Signal Processing

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Locating an unknown-position source using received signal strength (RSS) measurements in an accurate and low-complexity manner is addressed in this paper. Given that the source transmit power is unknown, we employ the differential RSS information to devise two computationally attractive localization methods based on the weighted least squares (WLS) approach. The main ingredients in the first algorithm development are to obtain the unbiased estimates of the squared ranges and introduce an extra variable. The second method improves the first version by implicitly exploiting the relationship between the extra variable and source location through a second WLS step. The performance of the two estimators is analyzed in the presence of zero-mean white Gaussian disturbances. Numerical examples are also included to evaluate their localization accuracy by comparing with the maximum likelihood approach and Cramér-Rao lower bound.

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Section: Improved Versionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate and simple source localization using differential received signal strength

Lin

Chan

2013

Digital Signal Processing

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“…The vast majority of studies consider geolocation based on a single-mode propagation model. For instance, [11,12] estimate the model parameters a priori to the position estimation; while [16,18,19] assume some or all of the model parameters are unknown and propose to estimate them together with the position. In mixed LOS/NLOS environments, field-trial results motivate us to consider a mode-dependent propagation model.…”

Section: Relation To Prior Work and Conclusionmentioning

confidence: 99%

Received signal strength-based joint parameter estimation algorithm for robust geolocation in LOS/NLOS environments

Yin

Fritsche²,

Gustafsson

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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We consider received-signal-strength-based robust geolocation in mixed line-of-sight/non-line-of-sight propagation environments. Herein, we assume a mode-dependent propagation model with unknown parameters. We propose to jointly estimate the geographical coordinates and propagation model parameters. In order to approximate the maximum-likelihood estimator (MLE), we develop an iterative algorithm based on the well-known expectation and maximization criterion. As compared to the standard ML implementation, the proposed algorithm is simpler to implement and capable of reproducing the MLE. Simulation results show that the proposed algorithm attains the best geolocation accuracy as the number of measurements increases

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“…SDR is a promising technology and has been chosen because it is a cost-effective, portable, and readily available [7]. Some of the key localization approaches that have been used in the past include time of arrival (TOA), time difference of arrival (TDOA) and angle of arrival (AOA) [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Partial discharge localization based on received signal strength

Mohamed

Lazaridis

Upton

et al. 2017

2017 23rd International Conference on Automation and Computing (ICAC)

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Abstract-Partial Discharge (PD) occurs when insulation containing defects or voids is subject to high voltages. If left untreated PD can degrade insulation until, eventually, catastrophic insulation failure occurs. The detection of PD current pulses, however, can allow incipient insulation faults to be identified, located and repaired prior to plant failure. Wireless technology has paved the path for PD detection and monitoring. Software Defined Radio (SDR) is a promising technology. Signals from two PD sources are received at six outdoors locations using an SDR USRP N200 which is connected to a laptop. PD sources, thereafter, are localized based on received signal strengths.

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Linear Least Squares Approach for Accurate Received Signal Strength Based Source Localization

Cited by 216 publications

References 17 publications

Accurate and simple source localization using differential received signal strength

Accurate and simple source localization using differential received signal strength

Received signal strength-based joint parameter estimation algorithm for robust geolocation in LOS/NLOS environments

Partial discharge localization based on received signal strength

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