Individual aoameasurement detection algorithm for target tracking in mixed LOS/NLOS environments

Yi, Lili; Razul, Sirajudeen Gulam; Lin, Zhiping; See, Chong-Meng Samson

doi:10.1109/icassp.2013.6638394

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…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. These results are further extended to nonlinear AOA measurement by using the MUSIC method in [16]. Similar approach has also been used to observe a mobile objective in mixed LOS/NLOS environments by using individual measurement and LOS detection [17].…”

Section: Introductionmentioning

confidence: 92%

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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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Section: Introductionmentioning

confidence: 92%

“…including relative distance and relative angle, are assumed to be calculated by RSS and AOA, respectively. The detailed deductions can be found in [12,16], with the basic equations as…”

Section: Nns Model and Problem Formulationmentioning

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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“…Approaches robust to NLOS errors have been proposed in [9], [10]. These approaches assume that a sufficient number of sensors receive the LOS signals, and NLOS errors are filtered out by making use of either LOS measurements or a predicted target position.…”

Section: B Related Workmentioning

confidence: 99%

Distributed Localization of a RF Target in NLOS Environments

Quitin

Leng

et al. 2015

IEEE J. Select. Areas Commun.

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We propose a novel distributed expectation maximization (EM) method for non-cooperative RF target localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.Index Terms-Target localization, expectation maximization algorithms, distributed algorithms, wireless sensor networks 0733-8716 (c)

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“…A method based on detection and elimination of NLOS components in MPCs is proposed in [13]. Moreover, [14] and [15] use plentiful LOS components to replace NLOS measurements. However, the performance of these methods become very poor when sensors cannot receive enough LOS components.…”

Section: Introductionmentioning

confidence: 99%

Distributed Target Localization with Inaccurate Collaborative Sensors in Multipath Environments

2019

KSII TIIS

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Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

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Individual aoameasurement detection algorithm for target tracking in mixed LOS/NLOS environments

Cited by 9 publications

References 13 publications

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

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

Distributed Localization of a RF Target in NLOS Environments

Distributed Target Localization with Inaccurate Collaborative Sensors in Multipath Environments

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