Performance Analysis and Dimension-Reduction Taylor Series Algorithms for Locating Multiple Disjoint Sources Based on TDOA Under Synchronization Clock Bias

Chen, Xin; Wang, Ding; Yin, Jianxin; Wu, Ying

doi:10.1109/access.2018.2860958

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…Taylor series [25], [26] (TS) is a classical iterative method which is well-known to be locally optimal. Similar to the proposed method, it utilizes the first-order approximation within the whole SOI.…”

Section: Relationship Between the Proposed Methods And Taylor Seriesmentioning

confidence: 99%

An Improved Grid-Search Method for the Identity-Test of Ionosphere-Layer Virtual Heights via TDOA Measurements

et al. 2019

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Sky-wave time-difference-of-arrival (TDOA) localization is a promising technique, which may enable surveillance over large-scale areas via passive radar systems. In a recently published paper, we have proposed a novel sky-wave TDOA localization method by introducing an assumption, i.e., the ionosphere-layer virtual heights (IVHs) for transmitting paths from a target to different closely spaced sensors are identical. In this paper, we further verify the assumption by testing the identity of the IVHs based on TDOA measurements of a known-position target. To realize this goal, the identity-test problem is converted into a coefficient retrieval problem. The current state-of-the-art method has to make an approximation to select the nearest grid-point (NGP) of coefficient vector, which induces a larger recovery error with the increase of dimension. To alleviate this, an improved grid-search method is proposed by training weights, which can build the proportional relationship between the weighed norm-2 cost functions and the norm-2 distances of grid-points and coefficients. Thus, the NGP can be selected freely from the aforementioned approximation error; and the coefficient recovery accuracy is improved. Additionally, the training phase of the proposed method is guaranteed to be feasible while that of the conventional method is not. The simulation results verify the superiority of the proposed method over the current state-of-the-art in terms of recovery accuracy and computational complexity. INDEX TERMSNonlinear optimization, time-difference-of-arrival, sky-wave passive radar, ionosphere-layer virtual height.

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Section: Relationship Between the Proposed Methods And Taylor Seriesmentioning

confidence: 99%

An Improved Grid-Search Method for the Identity-Test of Ionosphere-Layer Virtual Heights via TDOA Measurements

et al. 2019

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“…Their techniques are based on the Maximum Likelihood estimation of the TDOA, and one of them also estimates the frequency error of the oscillators. Then, similarly to this paper, the authors of [17], [18] focused on the localization of passive sources in systems of moving sensors that suffer from sensor position errors and from clock-synchronization bias between sensors. Their model, however, assumes that sensors are divided into groups within which sensors are timesynchronized, and that timing offsets are present only among different groups.…”

Section: Related Workmentioning

confidence: 97%

TDOA Source-Localization Technique Robust to Timing Attacks

Delcourt,

Boudec

2019

Preprint

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In this paper, we focus on the localization of a passive source from time difference of arrival (TDOA) measurements. TDOA values are computed with respect to pairs of fixed sensors that are required to be accurately time-synchronized. This constitutes a weakness as all synchronization techniques are vulnerable to delay injections. Attackers are able either to spoof the signal or to inject asymmetric delays in the communication channel. By nature, TDOA measurements are highly sensitive to time-synchronization offsets between sensors. Our first contribution is to show that timing attacks can severely affect the localization process. With a delay of a few microseconds injected on one sensor, the resulting estimate might be several kilometers away from the true location of the unknown source. We also show that residual analysis does not enable the detection and identification of timing attacks. Our second contribution is to propose a two-step TDOA-localization technique that is robust against timing attacks. It uses a known source to define a weight for each pair of sensors, reflecting the confidence in their time synchronization. Our solution then uses the weighted leastsquares estimator with the newly created weights and the TDOA measurements received from the unknown source. As a result, our method either identifies the network as being too corrupt to localize, or gives a corrected estimate of the unknown position along with a confidence metric. Numerical results illustrate the performance of our technique.

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“…In this subsection, we deduce the MSE of the source location and velocity estimates through the Taylor-series method at low TDOA and FDOA measurement noise levels. If the iteration procedure in the localisation algorithm is terminated, then we can obtain the one-order Taylor-series expansion 21 at approximately θ = [ u T , u · T ] T by combing equations (17) and (25). The equation can be obtained as shown in…”

Section: Crlb and Performance Analysismentioning

confidence: 99%

Interval analysis-based Bi-iterative algorithm for robust TDOA-FDOA moving source localisation

Qin

Wang

Shan

et al. 2021

International Journal of Distributed Sensor Networks

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In this study, an interval extension method of a bi-iterative is proposed to determine a moving source. This method is developed by utilising the time difference of arrival and frequency difference of arrival measurements of a signals received from several receivers. Unlike the standard Gaussian noise model, the time difference of arrival - frequency difference of arrival measurements are obtained by interval enclosing, which avoids convergence and initialisation problems in the conventional Taylor-series method. Using the bi-iterative strategy, the algorithm can alternately calculate the position and velocity of the moving source in interval vector form. Simulation results indicate that the proposed scheme significantly outperforms other methods, and approaches the Cramer-Rao lower bound at a sufficiently high noise level before the threshold effect occurs. Moreover, the interval widths of the results provide the confidence degree of the estimate.

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Performance Analysis and Dimension-Reduction Taylor Series Algorithms for Locating Multiple Disjoint Sources Based on TDOA Under Synchronization Clock Bias

Cited by 14 publications

References 33 publications

An Improved Grid-Search Method for the Identity-Test of Ionosphere-Layer Virtual Heights via TDOA Measurements

An Improved Grid-Search Method for the Identity-Test of Ionosphere-Layer Virtual Heights via TDOA Measurements

TDOA Source-Localization Technique Robust to Timing Attacks

Interval analysis-based Bi-iterative algorithm for robust TDOA-FDOA moving source localisation

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