Modified Taylor-series Method for Source and Receiver Localization Using TDOA Measurements with Erroneous Receiver Positions

Kovavisaruch, La-or; Ho, K. C.

doi:10.1109/iscas.2005.1465082

Cited by 31 publications

(22 citation statements)

References 9 publications

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“…In this section, we conduct two sets of experiments in order to compare our proposed method with the original Taylor-series method [1] and modified Taylor-series method [9] in terms of the improvements in the performance of estimating source location and receiver positions. In the first set of experiments, we study the performance of three algorithms when the TDOA noise power is fixed and the receiver noise power varies.…”

Section: Resultsmentioning

confidence: 99%

“…It is an important problem in many applications including radar, sonar, seismology, radio astronomy, and mobile communications [1]- [9]. Various Algorithms have been proposed to estimate the source location.…”

Section: Introductionmentioning

confidence: 99%

“…Some study has indicated that even relatively small amount of receiver location uncertainty can seriously deteriorate the source localization accuracy [3]- [4]. Consequently, improving the source location estimate under receiver position errors is an important issue and has attracted much effort over the years [3]- [9]. Rockah and Schultheiss [3]- [4] utilized the CRLB to derive a Direction of Arrival (DOA) estimation method in the presence of receiver position uncertainty, which assumes that the knowledge of one receiver location and its direction to the second receiver is known exactly.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method provides the source location estimate only and the solution accuracy does not reach the CRLB. Kovavisaruch and Ho [9] proposed an iterative algorithm based on the Taylor-series for estimating the location of source and the positions of receivers using TDOA measurements, when the receivers have random position errors. However, the proposed method requires good initial solution guess close to the true solution to begin with in order to ensure convergence.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Source and Receiver Localization Method with Erroneous Receiver Positions

Lei

Xia

2009

Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence

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Abstract. Receiver location uncertainty plays an important role in passive source localization since it can significantly deteriorate the accuracy of the source location. In this paper, we first derive a joint position estimation model for source location and receiver positions with erroneous receiver positions, and then, based on this model, an efficient algorithm for estimation the location of source and the positions of receivers using Time Difference of Arrival (TDOA) measurements is proposed. The proposed method employs modified Genetic Algorithm (MGA) and jointly estimates both source and receiver positions simultaneously. Simulation results show that when receiver position or TDOA measurement errors are large, the proposed method can achieve better performance than the modified Taylor method and the original Taylor method. It is more suitable for adverse measurement conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Source and Receiver Localization Method with Erroneous Receiver Positions

Lei

Xia

2009

Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence

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“…It does not require knowledge of absolute time of the transmission. Therefore, only the synchronicity of the reference sensor and other sensors is necessary for TDOAbased positioning [1][2] [3]. The major sources of error in TDOA measurements are measurement noises and non-lineof-sight (NLOS) signal propagations.…”

Section: Introductionmentioning

confidence: 99%

Road-constraint assisted target tracking in mixed LOS/NLOS environments based on TDOA measurements

Razul²,

Lin

et al. 2012

2012 IEEE International Symposium on Circuits and Systems

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This paper proposes an approach to improving the target tracking accuracy in mixed line-of-sight/non-lineof-sight (LOS/NLOS) environments based on individual measurement detection (IMD) and road constraints. Utilizing the IMD algorithm, most LOS time-difference-of-arrival (TDOA) measurements can be correctly selected from mixed LOS/NLOS TDOA measurements. Incorporating the prior knowledge of the road constraint as a pseudo-measurement, the augmented dynamic extended Kalman filter (EKF) is then implemented to track a moving target. Simulation results demonstrate that the road constraint assisted algorithm, in conjunction with the IMD method, performs better than the road constraint assisted algorithm without the IMD method, or tracking algorithm with the IMD method but without the road constraint. The proposed method is especially useful when the number of selected LOS TDOA measurements is less than three.

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