EM- and JMAP-ML Based Joint Estimation Algorithms for Robust Wireless Geolocation in Mixed LOS/NLOS Environments

Yin, Feng; Fritsche, Carsten; Gustafsson, Fredrik; Zoubir, Abdelhak M.

doi:10.1109/tsp.2013.2286779

Cited by 54 publications

(41 citation statements)

References 28 publications

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“…where is the ith RSS for the sensor in decibel (dB), is the signal strength at the reference distance ( ), is set to 1 m for convenience, is the true range (distance) to be estimated, is the path loss exponent, is distributed by (1 − ) (0, 2 1 ) + ( 2 , 2 2 ) with denoting samples in the sensor, and ( , 2 ) is the Gaussian probability density function (PDF) with mean and variance 2 , respectively [17]. It is assumed that and are known a priori from the calibration campaign [18,19].…”

Section: Problem Formulationmentioning

confidence: 99%

Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

Park

Chang

2019

Wireless Communications and Mobile Computing

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Herein, we present robust shrinkage range estimation algorithms for which received signal strength measurements are used to estimate the distance between emitter and sensor. The concepts of robustness for the Hampel filter and skipped filter are combined with shrinkage for the positive blind minimax and Bayes shrinkage estimation. It is demonstrated that the estimation accuracies of the proposed methods are higher than those of the existing median-based shrinkage methods through extensive simulations.

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Section: Problem Formulationmentioning

confidence: 99%

Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

Park

Chang

2019

Wireless Communications and Mobile Computing

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“…The fourth method is the source localization without distinguishing LOS and NLOS in mixed LOS/NLOS environments, where the key point is to design a robust source position estimator via jointly estimating mixture distribution and source position, for example, expectation-maximization (EM) and joint maximum a posteriori-maximum likelihood (JMAP-ML) in [44], adaptive kernel density estimation in [45], and machine learning in [46]. There also exists literature dedicated to the source localization under only NLOS measurements [47][48][49].…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

TDOA-Based Source Collaborative Localization via Semidefinite Relaxation in Sensor Networks

Yan

Wang

Shen

et al. 2015

International Journal of Distributed Sensor Networks

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The time delay of arrival-(TDOA-) based source localization using a wireless sensor network has been considered in this paper. The maximum likelihood estimate (MLE) is formulated by taking the correlated TDOA noise into account, which is caused by the difference with the TOA of the reference sensor. The global optimal solution is difficult to obtain due to the nonconvex nature of the ML function. We propose an alternative semidefinite programming method, which transforms the original ML problem into a convex one by relaxing nonconvex equalities into convex matrix inequalities. In addition, the source localization algorithm in the presence of sensor location errors and non-line-of-sight (NLOS) observations is developed. Our simulation results demonstrate the potential advantages of the proposed method. Furthermore, the proposed source localization algorithm by taking the NLOS TOA measurements as the constraints of the convex problem can provide a good estimate.

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“…Whenever measured by received signal strength (RSS) or angle of arrival (AOA), NLOS propagation often induces transmission delay or dropout of the packets, and thus large localization errors might occur if not addressed [11]. Several strategies have been proposed for mitigating the bad effect of NLOS errors, such as statistical identification [12], hypothesis test [13], and robust parameter estimation [14]. In [15], the switching behavior of NLOS and the lineof-sight (LOS) has been described by a hidden Markov Model, and an estimator has been designed by combining Kalman filter with the interacting multiple model (IMM) approach.…”

Section: Introductionmentioning

confidence: 99%

“…, − 1) is introduced to represent the failure of the preestimating algorithm(19) at time , with the reason that the numbers of reorganized CMs̃ℏ | ( ) or̃− | ( ) are less than a lower limit[9,12]. Thus, through preestimating transformation, the remote sensing CMs in(14) with high dimension are transformed into the data missing in(20) with lower dimension.Besides, and(̃[ ] ( ), [ ] ( )) in(19) will be deduced through a complementary fusion strategy, with the details as follows.3.2. Complementary Fusion Strategy.Let {̃[ ] ( ), = 1, 2, .…”

mentioning

confidence: 99%

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Gao

Zhao

et al. 2016

Mathematical Problems in Engineering

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Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.

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EM- and JMAP-ML Based Joint Estimation Algorithms for Robust Wireless Geolocation in Mixed LOS/NLOS Environments

Abstract: Abstract-We consider robust geolocation in mixed line-ofsight (LOS)/non-LOS (NLOS) environments in cellular radio

Cited by 54 publications

References 28 publications

Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

TDOA-Based Source Collaborative Localization via Semidefinite Relaxation in Sensor Networks

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

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