NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

Vilà‐Valls, Jordi; Closas, Pau

doi:10.1186/s13634-017-0498-4

Cited by 18 publications

(10 citation statements)

References 38 publications

(69 reference statements)

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“…Thus, in an effort to keep our mathematical treatment rigorous, we ignore blockages, which then provides a lower bound on the path length of the first-arriving NLOS signal. 5 Lastly, as we are studying NLOS bias, we implicitly assume that the LOS path is blocked throughout this work.…”

Section: Network Model This Section Outlines Important Definitionmentioning

confidence: 99%

“…In 2007, [4] summarized the state of the NLOS bias research: "At the present time, very little is known about the statistics of the NLOS variables [bias] in realistic propagation environments, and there are no established models." Since then, there have been many proposed NLOS bias distributions such as a skew t [5], a half-Gaussian [4], a Rayleigh [6], a shifted Gaussian [6], and a positive uniform [7]. Prior to these, a gamma distribution was also proposed [8] and an exponential distribution was, and still is, a commonly used model [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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A Mathematical Justification for Exponentially Distributed NLOS Bias

O’Lone

Dhillon

Buehrer

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In the past few decades, the localization literature has seen many models attempting to characterize the non-lineof-sight (NLOS) bias error commonly experienced in range measurements. These models have either been based on specific measurement data or chosen due to attractive features of a particular distribution, yet to date, none have been backed by rigorous analysis. Leveraging tools from stochastic geometry, this paper attempts to fill this void by providing the first analytical backing for an NLOS bias error model. Using a Boolean model to statistically characterize the random locations, orientations, and sizes of reflectors, and assuming first-order (i.e., single-bounce) reflections, the distance traversed by the first-arriving NLOS path is characterized. Under these assumptions, this analysis reveals that NLOS bias exhibits an exponential form and can in fact be well approximated by an exponential distribution -a result consistent with previous NLOS bias error models in the literature. This analytically derived distribution is then compared to a common exponential model from the literature, revealing this distribution to be a close match in some cases and a lower bound in others. Lastly, the assumptions under which these results were derived suggest this model is aptly suited to characterize NLOS bias in 5G millimeter wave systems as well.

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Section: Network Model This Section Outlines Important Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Mathematical Justification for Exponentially Distributed NLOS Bias

O’Lone

Dhillon

Buehrer

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…With less training data, SPGP can achieve performance comparable to GP regression. Vilà-Valls proposed a robust Bayesian inference framework to deal with target localization under NLOS conditions in [ 19 ]. The proposed algorithm takes advantage of the conditionally Gaussian formulation of the skew t -distribution.…”

Section: Related Workmentioning

confidence: 99%

A Residual Analysis-Based Improved Particle Filter in Mobile Localization for Wireless Sensor Networks

Cheng

Feng

Wang

2018

Sensors

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Wireless sensor networks (WSNs) have become a popular research subject in recent years. With the data collected by sensors, the information of a monitored area can be easily obtained. As a main contribution of WSN localization is widely applied in many fields. However, when the propagation of signals is obstructed there will be some severe errors which are called Non-Line-of-Sight (NLOS) errors. To overcome this difficulty, we present a residual analysis-based improved particle filter (RAPF) algorithm. Because the particle filter (PF) is a powerful localization algorithm, the proposed algorithm adopts PF as its main body. The idea of residual analysis is also used in the proposed algorithm for its reliability. To test the performance of the proposed algorithm, a simulation is conducted under several conditions. The simulation results show the superiority of the proposed algorithm compared with the Kalman Filter (KF) and PF. In addition, an experiment is designed to verify the effectiveness of the proposed algorithm in an indoors environment. The localization result of the experiment also confirms the fact that the proposed algorithm can achieve a lower localization error compared with KF and PF.

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“…Vila-Valls and Closas 22 provide a robust Bayesian inference framework to deal with target localization under NLOS conditions. The article takes advantage of the conditionally Gaussian formulation of the skew t-distribution, and then uses computationally light Gaussian filtering and smoothing methods as the core of the proposed approach.…”

Section: Related Workmentioning

confidence: 99%

Inertial measurement unit–aided dual-frequency radio frequency identification localization in line-of-sight and non-line-of-sight hybrid environment

Jie

Zhu

Xiao

et al. 2018

International Journal of Distributed Sensor Networks

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The mitigation of non-line-of-sight propagation conditions is one of main challenges in wireless signal-based indoor localization. When radio frequency identification localization technology is applied in applications, the received signal strength fluctuates frequently due to the shade and multipath effect of radio frequency signal, which could result in localization inaccuracy. In particular, when tag carriers are walking in line-of-sight and non-line-of-sight hybrid environment, great attenuation of received signal strength will happen, which would result in great positioning deviation. The article puts forward a dual-frequency radio frequency identification-based indoor localization approach in line-of-sight-nonline-of-sight hybrid environment with the help of inertial measurement unit. Dual-frequency radio frequency identification includes passive radio frequency identification and active radio frequency identification. Passive radio frequency identification is used to assist in determining the tag initial location with passive reader. Active radio frequency identification is used to locate the tag and send the sensor information to active radio frequency identification readers. The proposed method includes three improvements over previous received signal strength-based positioning methods: inertial measurement unit-aided received signal strength filtering, inertial measurement unit-aided line-of-sight/non-line-of-sight distinguishing, and inertial measurement unit-aided line-of-sight/non-line-of-sight environment switching. Also, Cramér-Rao low bound is calculated to prove theoretically that indoor positioning accuracy for the proposed method in line-of-sight and non-line-of-sight mixed environment is higher than position precision using only received signal strength information. Experiments are conducted to show that the proposed method can reduce the mean positioning error to around 3 m without site survey.

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NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

Cited by 18 publications

References 38 publications

A Mathematical Justification for Exponentially Distributed NLOS Bias

A Mathematical Justification for Exponentially Distributed NLOS Bias

A Residual Analysis-Based Improved Particle Filter in Mobile Localization for Wireless Sensor Networks

Inertial measurement unit–aided dual-frequency radio frequency identification localization in line-of-sight and non-line-of-sight hybrid environment

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