Minimum mean square error estimator for mobile location using time-difference-of-arrival measurements

Huang, Jiyan; Wan, Qun; Wang, P.

doi:10.1049/iet-rsn.2010.0061

Cited by 12 publications

(23 citation statements)

References 15 publications

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“…The first step MMSEE [11] of Z is: x x y y in the units of m, and are obtained from the average of 5000 independent runs. The RMSEs are evaluated using traditional MLE method [8], traditional MMSEE method [9], the proposed MLE method and the proposed MMSEE method.…”

Section: Improved Mmsee Algorithmmentioning

confidence: 99%

“…This method is an unbiased, and performs better than Caffery's method. Further improvement was obtained using minimum mean square error estimator (MMSEE) in [9]. Huang [9] proposes a method based on MMSEE to improve the accuracy of mobile location using statistical characteristics of MS position as prior information.…”

Section: Introductionmentioning

confidence: 99%

“…Further improvement was obtained using minimum mean square error estimator (MMSEE) in [9]. Huang [9] proposes a method based on MMSEE to improve the accuracy of mobile location using statistical characteristics of MS position as prior information. Due to the use of the prior information, the MMSEE has the lower Cramer-Rao lower bound (CRLB) than the MLE in [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel TOA location algorithms based on MLE and MMSEE for NLOS environments

Long

Huang

Pan

et al. 2013

2013 International Conference on Communications, Circuits and Systems (ICCCAS)

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Wireless positioning has now gained considerable attention due to its wide applications in military, environmental, health and commercial systems. One of the main problems facing accurate positioning in actual wireless positioning system is non-line-of-sight (NLOS) propagation where the signal arrives at a base station from reflections. Although maximum likelihood estimator (MLE) and minimum mean square error estimator (MMSEE) have been successfully applied in time-difference-of-arrival (TDOA) based localization methods for LOS environments, the problem of mobile location for time-of-arrival (TOA) positioning technique is still an open issue. Moreover, the performance of MLE and MMSEE may be sharply degraded in a real wireless channel due to the large errors of range measurements caused by NLOS propagation. In this paper, effective location algorithms based on NLOS weighting are proposed to mitigate NLOS errors. Main contributions of this paper are listed as follows: (1) both MLE and MMSEE are extended from TDOA to TOA; (2) weight matrix of the proposed method considers the effects of NLOS errors such that NLOS errors can be effectively suppressed. The simulation results show that the proposed method has better positioning accuracy than existing methods.

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Section: Improved Mmsee Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel TOA location algorithms based on MLE and MMSEE for NLOS environments

Long

Huang

Pan

et al. 2013

2013 International Conference on Communications, Circuits and Systems (ICCCAS)

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“…N numbers of GRSs with a single GRS as reference results in N-1 nonlinear hyperbolic equations that describe the TDOA measurement input and the position of emitter [12]. Due to this nonlinearity, several methods are developed to solve these equations [12][13][14][15][16][17][18][19][20][21][22][23][24][25] either in linear/closed-form method or nonlinear/open-form method [1,12]. In the nonlinear method, the first step is to linearize the nonlinear hyperbolic equations using series expansion such as the Taylor series expansion method [13][14][15].…”

mentioning

confidence: 99%

“…Most of the published work emphasized on reducing the PE error of the lateration algorithm at high TDOA estimation error [17,20,[23][24][25] or on performance analysis [18,19]. The use of multiple GRSs as references provides further improvement on the PE accuracy of emitter [17,23].…”

mentioning

confidence: 99%

Position Estimation Error Performance Model for a Minimum Configuration 3-D Multilateration

Yaro¹,

Sha’ameri²,

Kamel³

2018

IJEEI

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A multilateration system estimates the position of emitter using time difference of arrival (TDOA) measurements with a lateration algorithm. It involves solving a set of hyperbolic plane equations to determine the position of the emitter given the TDOA measurements that corresponds to the path difference (PD) measurement in distance. A performance model is developed using the relative maximum error bound (RMEB) which relates the plane equation condition number, the relative ground receiving station (GRS) geometry and the PD measurement error to estimate the position estimation (PE) error. By using air traffic monitoring for civil aviation as an application, Monte Carlo simulation verifies the PE error of the performance model for a square GRS configuration. The coverage assumed a 360 0 bearing, a range of up to 200 km and a maximum altitude of 15 km. Simulation results also show that the performance model estimates the horizontal position error with a maximum absolute error of 0.1 km up to a range of 200 km at an altitude of 15 km and a minimum absolute error of 0.2 km at an altitude of 15 km.

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Robust TOA source localisation algorithm using prior location

Park

Chang

2019

IET Radar, Sonar & Navigation

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Minimum mean square error estimator for mobile location using time-difference-of-arrival measurements

Cited by 12 publications

References 15 publications

Novel TOA location algorithms based on MLE and MMSEE for NLOS environments

Novel TOA location algorithms based on MLE and MMSEE for NLOS environments

Position Estimation Error Performance Model for a Minimum Configuration 3-D Multilateration

Robust TOA source localisation algorithm using prior location

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