scholarly journals Non-line-of-sight mobile station positioning algorithm using TOA, AOA, and Doppler-shift

2014 ◽  
Author(s):  
Rohan Ramlall ◽  
Jie Chen ◽  
Arnold Lee Swindlehurst
Keyword(s):  
Doppler Shift ◽  
Mobile Station ◽  
Line Of Sight ◽  
Positioning Algorithm ◽  
Non Line Of Sight

Based on Wavelet Transform of TDOA/AOA Location Tracking Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1772-1775
Author(s):  
Bao Quan Chen ◽  
Yong Yi Mao ◽  
Yang Yang ◽  
Ping Xu
Keyword(s):  
Wavelet Transform ◽  
Mobile Station ◽  
Line Of Sight ◽  
Location Tracking ◽  
Distance Threshold ◽  
Location Algorithm ◽  
Positioning Algorithm ◽  
Station Location ◽  
Better Than ◽  
Non Line Of Sight

In cellular network mobile station location, the actual measured values non-line-of-sight error exists, makes the precision positioning algorithm based on the measured value is lower, aiming at the effects of non-line-of-sight error on the positioning performance, this paper proposes a new algorithm to reduce non line-of-sight error. Using wavelet transform to signal de-noising effect, eliminate the error in the measured value and reuse method of TDOA/AOA location algorithm to estimate mobile station location, with a certain distance threshold to mobile station location tracking. Simulation results show that the algorithm can effectively improve the positioning precision in non line-of-sight environments, positioning result was significantly better than Chan algorithm and TDOA/AOA location method algorithm.

scholarly journals Non-Line-of-Sight Error Mitigation in Wireless Communication Systems

2011 ◽  
Vol 1 ◽  
pp. 173-177
Author(s):  
Szu Lin Su ◽  
Yi Wen Su ◽  
Ho Nien Shou ◽  
Chien Sheng Chen
Keyword(s):  
Communication Systems ◽  
Hybrid Methods ◽  
Mobile Station ◽  
Base Stations ◽  
Superior Performance ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Error Statistics ◽  
Non Line Of Sight

When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

scholarly journals NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2991 ◽  
Author(s):  
Jingyu Hua ◽  
Yejia Yin ◽  
Weidang Lu ◽  
Yu Zhang ◽  
Feng Li
Keyword(s):  
Hypothesis Test ◽  
Intermediate Position ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Algorithms ◽  
Anchor Nodes ◽  
Positioning Algorithm ◽  
Non Line Of Sight

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

scholarly journals A non-line-of-sight error mitigation method for location estimation

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668273 ◽  
Author(s):  
Chien-Sheng Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mobile Station ◽  
Location Estimation ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location ◽  
Non Line Of Sight

Because there are always non-line-of-sight effects in signal propagation, researchers have proposed various algorithms to mitigate the measured error caused by non-line-of-sight. Initially inspired by flocking birds, particle swarm optimization is an evolutionary computation tool for optimizing a problem by iteratively attempting to improve a candidate solution with respect to a given measure of quality. In this article, we propose a new location algorithm that uses time-of-arrival measurements to improve the mobile station location accuracy when three base stations are available. The proposed algorithm uses the intersections of three time-of-arrival circles based on the particle swarm optimization technique to give a location estimation of the mobile station in non-line-of-sight environments. An object function is used to establish the nonlinear relationship between the intersections of the three circles and the mobile station location. The particle swarm optimization finds the optimal solution of the object function and efficiently determines the mobile station location. The simulation results show that the proposed algorithm performs better than the related algorithms in wireless positioning systems, even in severe non-line-of-sight propagation conditions.

scholarly journals ANALISIS PENGARUH PERGERAKAN USER TERHADAP KUALITAS SINYAL SUARA PADA JARINGAN WIMAX IEEE 802.16

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Aris Hartaman ◽  
Uke Kurniawan Usman ◽  
Budi Prasetya
Keyword(s):  
Mobile Station ◽  
Multipath Fading ◽  
Ieee 802.16 ◽  
Wireless Access ◽  
Doppler Spread ◽  
Line Of Sight ◽  
Ieee 802.16E ◽  
Mobile Wireless ◽  
Broadband Wireless ◽  
Non Line Of Sight

WiMAX IEEE 802.16e merupakan non-line of sight (NLOS) Broadband Wireless Access (BWA) yang dikeluarkan oleh WiMAX Forum yang dikembangkan berdasarkan standar IEEE 802.16. Teknologi ini mampu diimplementasikan untuk mobile wireless access yang dapat memenuhi kebutuhan layanan data dengan bandwidth yang cukup besar 20 MHz, coverage yang luas 50 km, dengan bitrate yang tinggi 75 Mbps. Pergerakan user/mobile station akan mempengaruhi kualitas layanan suara pada jaringan WiMAX IEEE 802.16e yaitu akan terjadinya efek doppler spread yang mengakibatkan perubahan frekuensi terhadap mobile station yang bergerak. Penelitian mengenai pengaruh pergerakan user/mobile station terhadap kualitas sinyal suara pada jaringan WiMAX IEEE 802.16e ini dilakukan dengan cara membuat simulasi sistem transmisi WiMAX 802.16e yang dilewatkan pada kanal yang bersifat multipath fading karena bersifat komunikasi non-line of Sight (NLOS) dengan cara terdistribusi rayleigh dan noise AWGN dengan pemodelan kanal propagasi SUI (Stanford University Interim). Parameter kecepatan user/mobile station yang disimulasikan mulai 0 km/jam (user statis), <15km/jam (user berjalan kaki), 16-50 km/jam (user berkendaraan sedang), sampai dengan 51-120 km/jam (user kecepatan tinggi). Hasil simulasi dan analisa memperlihatkan bahwa kecepatan pergerakan user sangat berpengaruh terhadap fluktuasi fading pada kanal rayleigh. Berdasarkan standar komunikasi digital yaitu nilai BER sebesar 10, nilai Eb/No yang dihasilkan dibawah 12 dB untuk modulasi BPSK kecepatan user bisa mencapai 50 km/jam, sementara untuk QPSK kecepatan maksimal user dibawah 50 km/jam, dan 8PSK kecepatan user dibawah 30 km/jam. Sehingga modulasi yang paling baik untuk system ini adalah BPSK.

scholarly journals Combination linear lines of position and neural network for mobile station location estimation

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771738 ◽  
Author(s):  
Chien-Sheng Chen
Keyword(s):  
Mobile Station ◽  
Base Station ◽  
Location Estimation ◽  
Base Stations ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Marquardt Algorithm ◽  
Station Location ◽  
Non Line Of Sight

To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors.

Non-Line-of-Sight Positioning Algorithm Based on Robust Principal Component Analysis

2014 ◽  
Vol 998-999 ◽  
pp. 889-893
Author(s):  
Zhu Lin Xiong ◽  
Ce Lun Liu ◽  
Wei Du ◽  
Ze Bin Xie
Keyword(s):  
Principal Component Analysis ◽  
Mobile Station ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Robust Principal Component Analysis ◽  
Error Matrix ◽  
Non Line Of Sight

Non-Line-of-Sight (NLOS) propagation problems badly degrade the accuracy of wireless mobile positioning algorithms, which incurs a large positive bias in the Time-of-Arrival (TOA) measurements. Under several assumptions, the Hankel matrix of TOA data can be decomposed into a low-rank distance matrix and a sparse error matrix. This paper utilizes the robust principal component analysis (RPCA) method to solve the decomposition problem. After estimating the distance, the positioning problem can use existing Line-of-Sight (LOS) based algorithms to calculate the coordinate of the mobile station (MS). Simulation results show our method outperforms other existing NLOS positioning methods and the RPCA based matrix decomposition process can eliminate NLOS effect efficiently.

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