Semidefinite Programming Methods for Alleviating Sensor Position Error in TDOA Localization

Zou, Yanzhen; Liu, Huaping; Xie, Wei

doi:10.1109/access.2017.2752206

Cited by 45 publications

(32 citation statements)

References 20 publications

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“…Localization of an emitting source has a variety of applications, such as emergency assistance, people and asset tracking, location-based advertising and wireless sensor network [1][2][3][4][5]. Measurements used typically include distance-based parameters such as time-of-arrival (TOA) [6][7][8][9] and time-difference-of-arrival (TDOA) [10,11], received signal strength (RSS) [12,13], and angle-of-arrival (AOA) [14].…”

Section: Introductionmentioning

confidence: 99%

An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization

Zou

Liu

2020

Sensors

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In time-of-arrival (TOA) localization systems, errors caused by non-line-of-sight (NLOS) signal propagation could significantly degrade the location accuracy. Existing works on NLOS error mitigation commonly assume that NLOS error statistics or the TOA measurement noise variances are known. Such information is generally unavailable in practice. The goal of this paper is to develop an NLOS error mitigation scheme without requiring such information. The core of the proposed algorithm is a constrained least-squares optimization, which is converted into a semidefinite programming (SDP) problem that can be easily solved by using the CVX toolbox. This scheme is then extended for cooperative source localization. Additionally, its performance is better than existing schemes for most of the scenarios, which will be validated via extensive simulation.

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Section: Introductionmentioning

confidence: 99%

An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization

Zou

Liu

2020

Sensors

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“…The research about related technology is considered as a hot topic in many fields. Time difference of arrival (TDOA) is a method of positioning by time difference, since the distance of the signal source can be determined by measuring the time it takes for the signal to arrive at the monitoring station [1]. The accurate location of the source can be determined by finding its distance from three or more monitoring stations [2].…”

Section: Introductionmentioning

confidence: 99%

“…Three-dimensional positioning requires four stations to generate three pairs of hyperboloids, the surface intersection lines and the intersection *Correspondence: qujunsuo@xupt.edu.cn † Junsuo Qu and Haonan Shi contributed equally to this work. 1 School of Automation, Xi'an University of Posts and Telecommunications, 710121 Xi'an, China Full list of author information is available at the end of the article points to determine the location of the radiation source. However, it may not result in a unique solution.…”

Section: Introductionmentioning

confidence: 99%

New three-dimensional positioning algorithm through integrating TDOA and Newton’s method

Shi

Qiao

et al. 2020

J Wireless Com Network

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Passive time difference location is an important method for passive location. There are fuzzy positioning, no solution, and low positioning accuracy with the spherical coordinate conversion method in the four-station TDOA positioning algorithm. Focusing on these problems, we proposed a combination of TDOA and iterative Newton's method. The positioning method uses the result obtained by the four-station TDOA location algorithm as the initial value of the iterative Newton's method and solves the problem of no solution and fuzzy positioning caused by the four-station TDOA location algorithm by using the spherical coordinates conversion method. By simulating the target at a height of 5 km and traveling at a constant speed for 40 km, the positioning accuracy of the root mean square error is less than 45 m, which can achieve the same positioning accuracy of TDOA based on the least square algorithm. As the baseline length increases, the positioning accuracy is better than the least square algorithm.

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“…In the first step, some intermediate parameters that are embedded in the received signals are extracted at several stations or different time slots through signal processing techniques. Intermediate parameters can be characterized by the emitter location and are usually angle of arrival (AOA) [1][2][3], time difference of arrival (TDOA) [4][5][6][7][8][9][10][11][12][13][14][15][16], time of arrival (TOA) [17][18][19][20][21][22], frequency difference of arrival (FDOA) [23][24][25][26][27][28][29][30][31][32], frequency of arrival (FOA) [33], received signal strength (RSS) [34][35][36][37][38], gain ratios of arrival (GROA) [38][39][40][41], etc. In the second step, the transmitter's position is determined by finding the coordinate that best fits the lines of position (LOP) associated with the parameters obtained in the first step.…”

Section: Introductionmentioning

confidence: 99%

“…The methods in the first class require iteration to obtain an accurate location estimate. The most important iterative methods include Taylor-series iterative algorithm [8], constrained total least squares (CTLS) algorithm [11,28], quadratic constraint least squares (QCLS) algorithm [6,14,27,31,38], and interior-point algorithm [9,16,29]. The second category can provide explicit solutions to the target position, and typical closed-form approaches include spherical-interpolation (SI) algorithm [4], two-step weighted least squares (TWLS) algorithm [5,10,15,23,24,26,30,32,39,40], and multidimensional scaling (MDS) algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

On the use of calibration emitters for TDOA source localization in the presence of synchronization clock bias and sensor location errors

Wang

Yin

Chen

et al. 2019

EURASIP J. Adv. Signal Process.

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Time difference of arrival (TDOA) positioning is one of the widely applied techniques for locating an emitting source. Unfortunately, synchronization clock bias and random sensor location perturbations are known to significantly degrade the TDOA localization accuracy. This paper studies the use of a set of calibration sources, whose locations are accurately known to an estimator, to reduce the loss in localization accuracy caused by synchronization offsets and sensor location errors. Under the Gaussian noise assumption, we first derive the Cramér-Rao bound (CRB) for parametric estimation with the use of calibration emitters. Some explicit CRB expressions are obtained, and the performance improvement due to the introduction of the calibration sources is also quantified through the CRB analysis. In order to achieve the optimum localization accuracy, we proceed to propose new localization methods using the TDOA measurements from both target source and calibration emitters. Specifically, two dimension-reduction Taylor-series iterative algorithms are developed, and both of them have two stages. The first stage estimates the clock bias and refines the sensor positions by using the calibration TDOA measurements and the prior knowledge of sensor locations. The second stage provides the estimates of source location by combining the TDOA measurements of target signal and the estimated values in the first phase. The mean square errors (MSEs) of the proposed methods are shown analytically to achieve the corresponding CRB by applying the first-order perturbation analysis. Simulations are used to corroborate and support the theoretical development in this paper.

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Semidefinite Programming Methods for Alleviating Sensor Position Error in TDOA Localization

Cited by 45 publications

References 20 publications

An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization

An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization

New three-dimensional positioning algorithm through integrating TDOA and Newton’s method

On the use of calibration emitters for TDOA source localization in the presence of synchronization clock bias and sensor location errors

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