A statistics-based least squares (SLS) method for non-line-of-sight error of indoor localization

2013 ◽  
Author(s):  
Yuan Yang ◽  
Yubin Zhao ◽  
Marcel Kyas
Keyword(s):  
Least Squares ◽  
Indoor Localization ◽  
Line Of Sight ◽  
Non Line Of Sight

IMU-Aided Dual-Frequency RFID Based Localization in LOS/NLOS Hybrid Environment

2017 ◽  
Author(s):  
Jie Wu ◽  
MingHua Zhu ◽  
Bo Xiao ◽  
Wei He
Keyword(s):  
Indoor Localization ◽  
Measurement Unit ◽  
Line Of Sight ◽  
Dual Frequency ◽  
Site Survey ◽  
Line Of Sight Propagation ◽  
The Mean ◽  
Position Precision ◽  
Rf Signal ◽  
Non Line Of Sight

The mitigation of NLOS (non-line-of-sight) propagation conditions is one of main challenges in wireless signals based indoor localization. When RFID localization technology is applied in applications, RSS fluctuates frequently due to the shade and multipath effect of RF signal, which could result in localization inaccuracy. In particularly, when tags carriers are walking in LOS (line-of-sight) and NLOS hybrid environment, great attenuation of RSS will happen, which would result in great location deviation. The paper proposes an IMU-assisted (Inertial Measurement Unit) RFID based indoor localization in LOS/NLOS hybrid environment. The proposed method includes three improvements over previous RSS based positioning methods: IMU aided RSS filtering, IMU aided LOS/NLOS distinguishing and IMU aided LOS/NLOS environment switching. Also, CRLB (Cramér-Rao Low Bound) is calculated to prove theoretically that indoor positioning accuracy for proposed method in LOS/NLOS mixed environment is higher than position precision of only use RSS information. Simulation and experiments are conducted to show that proposed method can reduce the mean positioning error to around 3 meters without site survey.

Improved D/TA and Information Fusion Based on HMM Indoor Localization

2013 ◽  
Vol 373-375 ◽  
pp. 916-921 ◽  
Author(s):  
Jing Yu Ru ◽  
Cheng Dong Wu ◽  
Yun Zhou Zhang ◽  
Rong Fen Gong ◽  
Peng Da Liu
Keyword(s):  
Information Fusion ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Transition Probability ◽  
Line Of Sight ◽  
Ml Estimation ◽  
Mixed Line ◽  
Grid Based ◽  
Accuracy And Stability ◽  
Non Line Of Sight

This paper describes an efficient Bayesian framework for localization based on Ultra-wide Bandwidth (UWB) system. Approximate grid-based method based on the Hidden Markov Model (HMM) is an effective method to estimate the position of the Moving Terminal (MT) with the mixed line-of-sight/non-line-of-sight (LOS/NLOS) situation. This article proposes an algorithm by modifying the Position Transition Probability (PTP) according to the practical dynamic model and uses the information fusion effectively. We compare the Maximum Likelihood (ML) estimation with Detection/Tracking Algorithm (D/TA) estimation and its improved algorithm by simulation, in which the localization to an identical trajectory has been tested. The results of the analysis show that the proposed method has better accuracy and stability.

scholarly journals Time-of-arrival source localization based on weighted least squares estimator in line-of-sight/non-line-of-sight mixture environments

2016 ◽  
Vol 12 (12) ◽  
pp. 155014771668382 ◽  
Author(s):  
Chee-Hyun Park ◽  
Joon-Hyuk Chang
Keyword(s):  
Least Squares ◽  
Source Localization ◽  
Weighted Least Squares ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Mean Square ◽  
Localization Algorithm ◽  
Absolute Deviation ◽  
Weighted Least Squares Estimator ◽  
Non Line Of Sight

In this article, we propose a line-of-sight/non-line-of-sight time-of-arrival source localization algorithm that utilizes the weighted least squares. The proposed estimator combines multiple sorted measurements using the spatial sign concept, Mahalanobis distance, and Stahel–Donoho estimator, that is, assigning less weight to the samples as they are far from the center of inlier distribution. Also, the eigendecomposition Kendall’s [Formula: see text] covariance matrix is utilized as the scatter measure instead of the conventional median absolute deviation. Thus, the adverse effects by outliers can be attenuated effectively. To validate the superiority of the proposed methods, the root mean square error performances are compared with that of the existing algorithms via extensive simulation.

scholarly journals Location of Moving Targets in Substation Non-Line-of-Sight Environment

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5321
Author(s):  
Yubo Wang ◽  
Weimin Yang ◽  
Zheng Wang ◽  
Wenjun Zhou ◽  
Liang Li ◽  
...  
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Least Squares ◽  
Probability Distribution Function ◽  
Weighted Least Squares ◽  
Signal Propagation ◽  
Propagation Path ◽  
Line Of Sight ◽  
Measured Signal ◽  
Non Line Of Sight

In substations, a localization system based on a wireless sensor network (WSN) is a challenge, because the propagation of the measured signal could be blocked by various devices. In other words, non-line-of-sight (NLOS) propagation, where the signal propagation path is occluded, will affect measurement accuracy. A novel localization method based on a two-step weighted least squares and a probability distribution function is proposed to reduce the influence of NLOS error on the localization result. In this method, the initial multi-group localization result is obtained by the two-step weight weighted least-squares method, and the probability distribution function of the target is constructed by using the initial localization results, which can effectively reduce the influence of the NLOS error on the localization result. The simulation and test results show that the proposed method can keep the coordinate error within 30 cm in the substation. Compared with the localization result of two-step weighted least-squares (TSWLS) method, the average localization error is reduced by more than 1 m. Compared with the other two similar algorithms, the localization accuracy is improved by more than 50%. The tested results show that the localization performance of the method is robustness in the NLOS environment of the substation. While ensuring stability, the proposed algorithm is less efficient than some existing ones. However, under the calculation conditions of ordinary computers, the average single-point calculation time is less than 0.1 s, which can meet the needs of applications in substations.

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