A Bias-Reduced Nonlinear WLS Method for TDOA/FDOA-Based Source Localization

Wang, Gang; Cai, Shu; Li, Youming; Ansari, Nirwan

doi:10.1109/tvt.2015.2508501

Cited by 73 publications

(31 citation statements)

References 24 publications

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“…methods [38][39][40][41][42][43][44][45], the proposed method considers both receiver position errors and synchronization clock bias. 2.…”

Section: Different From Most Existing Bias Reductionmentioning

confidence: 99%

“…3. All previous studies [38][39][40][41][42][43][44][45] aim at reducing the bias of the source position. We develop a bias-reduced method that effectively reduces not only the bias of the source position but also the bias of refined receiver positions and estimated clock bias vector.…”

Section: Different From Most Existing Bias Reductionmentioning

confidence: 99%

“…It is therefore necessary to reduce the bias to improve the localization performance. Over the years, many studies have reduced the bias of an estimator using TDOAs [38][39][40][41][42][43][44][45]. Two methods of reducing the bias of the closed-form solution using TDOAs have been proposed [38], but the receiver position errors and synchronization clock bias were not taken into account.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by previous works [38][39][40][41][42][43][44][45], this paper proposes a bias-reduced method of reducing the bias of estimates from [36] using TDOAs in the presence of receiver position errors and synchronization clock bias. The study begins with a bias analysis for the original TSWLS solution.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bias reduction for TDOA localization in the presence of receiver position errors and synchronization clock bias

Chen

Wang

Yin

et al. 2019

EURASIP J. Adv. Signal Process.

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Time difference of arrival (TDOA) localization does not require time stamping of the source signal and is playing an increasingly important role in passive location. In addition to measurement noise, receiver position errors and synchronization clock bias are two important factors affecting the performance of TDOA positioning. This paper proposes a bias-reduced solution for passive source localization using TDOA measurements in the presence of receiver position errors and synchronization clock bias. Like the original two-step weighted least-squares solution, the new technique has two stages. In the first stage, the proposed method expands the parameter space in the weighted least-squares (WLS) formulation and imposes a quadratic constraint to suppress the bias. In the second stage, an effective WLS estimator is given to reduce the bias generated by nonlinear operations. With the aid of second-order error analysis, theoretical biases for the original solution and proposed bias-reduced solution are derived, and it is proved that the proposed bias-reduced method can achieve the Cramér-Rao lower bound performance under moderate Gaussian noise, while having smaller bias than the original algorithm. Simulation results exhibit smaller estimation bias and better robustness for all estimates, including those of the source position, refined receiver positions, and clock bias vector, when the measurement noise or receiver position error increases.

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“…methods [38][39][40][41][42][43][44][45], the proposed method considers both receiver position errors and synchronization clock bias. 2.…”

Section: Different From Most Existing Bias Reductionmentioning

confidence: 99%

Section: Different From Most Existing Bias Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bias reduction for TDOA localization in the presence of receiver position errors and synchronization clock bias

Chen

Wang

Yin

et al. 2019

EURASIP J. Adv. Signal Process.

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“…The former is to estimate the position of target by measuring some parameters, such as received signal strength (RSS) [4], time-of-arrival (TOA) [5], time-difference-of-arrival (TDOA) [6], and angle-of-arrival (AOA) [7], or their combinations. By transforming these parameters into the distances, one can localize the interested targets by using the so-called trigonometry.…”

Section: Introductionmentioning

confidence: 99%

An Improved Outliers Detection Algorithm for Received Signal Strength Measurements in Building Environment

Zhang¹,

Lü²,

Guo³

2017

dtcse

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Abstract. In this paper, we propose a new algorithm to detect outliers for RSS measurements in building environment. RSS measurements fluctuate severely in complex building environment because multi-path, obstacles, and moving people, can distort the propagation of radio wave, which leads to some inevitable large values in RSS measurement, known as outliers. Some classical outliers detection algorithm fail to detect them when outliers rate is higher. Based on this observation, we proposed a modified Hample filter algorithm to detect outliers. Simulation results show that our proposed algorithm can overcome the fluctuation of RSS in building localization environment.

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A bias‐reduced solution for target localization with illuminator of opportunity passive radar

Wang³

2019

Int J Communication

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Summary An improved solution for target localization on the basis of the realistic distance‐dependent noises in illuminator of opportunity passive radar and an algebraic reduction method of the bias, which exists in the two‐stage weighted least squares (TSWLS) method is proposed. The classic TSWLS and its improved solutions have great localization performances just on the basis of two approximations; that is, setting the noise to a constant and ignoring the high‐order terms of measurement noises. It is these two approximations that lead to a suboptimal solution with bias. The bias of TSWLS has a significant influence on the target localization in illuminator of opportunity passive radar that has lower measurement accuracy and higher noises than common radars. Therefore, this paper starts by taking into consideration the realistic distance‐dependent characteristics of time‐of‐arrival (TOA) and frequency‐of‐arrival (FOA) noises and improving TSWLS method. Then, the bias of the improved TSWLS method is analyzed and an algebraic bias‐reduced solution on the basis of weighted least squares (WLS) is developed. Numerical simulations demonstrate that, compared with the existing methods, the proposed bias‐reduced solution attains the Cramer‐Rao lower bound (CRLB) better and reduces the bias effectively.

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A Bias-Reduced Nonlinear WLS Method for TDOA/FDOA-Based Source Localization

Cited by 73 publications

References 24 publications

Bias reduction for TDOA localization in the presence of receiver position errors and synchronization clock bias

Bias reduction for TDOA localization in the presence of receiver position errors and synchronization clock bias

An Improved Outliers Detection Algorithm for Received Signal Strength Measurements in Building Environment

A bias‐reduced solution for target localization with illuminator of opportunity passive radar

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