A closed-form solution to bearings-only target motion analysis

Nardone, S.; Graham, M.L.

doi:10.1109/48.557551

Cited by 84 publications

(42 citation statements)

References 23 publications

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“…This is not difficult, because the position vector p is a function of direction. Once the DOAs relative to all arrays have been estimated, the initial source position can be easily determined using the closed-form method [4]. In addition, s and β can be initialized from (28) and (29), respectively.…”

Section: Summary Of the Alternating Minimization Algorithmmentioning

confidence: 99%

“…Generally, each base station is equipped with an antenna array that can be used to estimate the angle of arrival of the transmitted signals. With multiple DOA estimates from different observer locations, it is possible to locate the source [4][5][6]. This emitter localization procedure is called two-step location.…”

Section: Introductionmentioning

confidence: 99%

“…This emitter localization procedure is called two-step location. In the first step, the signal parameters (e.g., DOA [1][2][3][4][5][6], time difference of arrival (TDOA) [7,8], time of arrival (TOA) [9,10], frequency difference of arrival (FDOA) [11,12], frequency of arrival (FOA) [13], received signal strength (RSS) [14], gain ratios of arrival (GROA) [15]) are obtained independently from the intercepted signals by spatially separated sensors. In the second step, the measurements of all sensors are transferred to a central unit and the transmitter location is estimated.…”

Section: Introductionmentioning

confidence: 99%

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Robust direct position determination methods in the presence of array model errors

Wang

Yin

Liu

et al. 2018

EURASIP J. Adv. Signal Process.

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Direct position determination (DPD) methods are known to have many advantages over the traditional two-step localization method, especially for low signal-to-noise ratios (SNR) and/or short data records. However, similar to conventional direction-of-arrival (DOA) estimation methods, the performance of DPD estimators can be dramatically degraded by inaccuracies in the array model. In this paper, we present robust DPD methods that can mitigate the effects of these uncertainties in the array manifold. The proposed technique is related to the classical auto-calibration procedure under the assumption that prior knowledge of the array response errors is available. Localization is considered for the cases of both unknown and a priori known transmitted signals. The corresponding maximum a posteriori (MAP) estimators for these two cases are formulated, and two alternating minimization algorithms are derived to determine the source location directly from the observed signals. The Cramér-Rao bounds (CRBs) for position estimation are derived for both unknown and known signal waveforms. Simulation results demonstrate that the proposed algorithms are asymptotically efficient and very robust to array model errors.

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Section: Summary Of the Alternating Minimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust direct position determination methods in the presence of array model errors

Wang

Yin

Liu

et al. 2018

EURASIP J. Adv. Signal Process.

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“…A large variety of range-based localization methods have been proposed with respect to a set of sensors with known positions [3,4]. Several range-based geolocation techniques have been used to estimate the target position including the time-of-arrival (TOA) [5][6][7], the time-difference-of-arrival (TDOA) [8,9], the angle-of-arrival (AOA) [10][11][12], the received signal strength (RSS) [13][14][15][16] based techniques, or hybrid location techniques [17,18]. Among these location techniques, TOA and AOA have attracted much attention because of their high positioning accuracy [16,18].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Estimator for Target Localization in a Multistation Redundancy System without Matrix Inversion

et al. 2018

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This paper focuses on target localization problem in a multistation redundancy system which finds broad applications in sonar, radar, and location-based service. Previous solutions can only be applied to the minimum system, such as the TOA method with three sensors and the AOA method with two sensors or need matrix inversion. To solve this problem, we propose a simple closed-form solution for a multistation redundancy localization system by using the estimation variance as the weighting coefficient to compute an average of each group's localization result. The proposed method, with simple algebraic solution, requires no matrix inversion and can be used for low-cost hardware devices. We derive the method in TOA solution and AOA solution, respectively. The proposed method can also be extended to other locating technologies. Numerical examples are provided to illustrate the performance of the proposed method in root-mean-square error. The positioning accuracy of the proposed method is close to Cramér-Rao low bound both in TOA solution and AOA solution.

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“…In this setup, it has been recognized that the problem is fundamentally one of nonlinear regression, for which batch algorithms are a convenient framework; see [28] for a seminal contribution. A vast and rich literature has been devoted to this subject [28,6,31]. However, inclusion of constraints in the estimation process for BOT TMA is rarely considered [14,19,29].…”

Section: Introductionmentioning

confidence: 99%

Constrained bearings-only target motion analysis via Markov chain Monte Carlo methods

Bavencoff

Vanpeperstraete

Cadre

2006

IEEE Trans. Aerosp. Electron. Syst.

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A closed-form solution to bearings-only target motion analysis

Cited by 84 publications

References 23 publications

Robust direct position determination methods in the presence of array model errors

Robust direct position determination methods in the presence of array model errors

An Efficient Estimator for Target Localization in a Multistation Redundancy System without Matrix Inversion

Constrained bearings-only target motion analysis via Markov chain Monte Carlo methods

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