Closed-Form Localization for Distributed MIMO Radar Systems Using Time Delay Measurements

Park, Chee‐Hyun; Chang, Joon‐Hyuk

doi:10.1109/twc.2015.2490677

Cited by 98 publications

(46 citation statements)

References 37 publications

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“…In general, there are basically two kinds of target localization methods. One is based on the time of arrival (TOA) or angle of arrival (AOA) information from the received signals, which are used to calculate the position via triangulation [11], [12], [18]. Such an algorithm is categorized as an indirect localization approach.…”

Section: Introductionmentioning

confidence: 99%

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Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas

Zhou

et al. 2020

IEEE Trans. Signal Process.

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In this paper, the problems of simultaneously detecting and localizing multiple targets are considered for noncoherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming a prior knowledge of target number, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching parameters of interest in a high-dimensional space. However, the complexity of this method increases exponentially with the number G of targets.Besides, without the prior information of the number of targets, a multi-hypothesis testing strategy to determine the number of targets is required, which further complicates this method. Therefore, we split the joint maximization into G disjoint optimization problems by clearing the interference from previously declared targets. In this way, we derive two fast and robust suboptimal solutions which allow trading performance for a much lower implementation complexity which is almost independent of the number of targets. In addition, the multi-hypothesis testing is no longer required when target number is unknown.Simulation results show the proposed algorithms can correctly detect and accurately localize multiple targets even when targets share common range bins in some paths.

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Section: Introductionmentioning

confidence: 99%

“…if F g (θ g ) < λ g (θ g ) then 12 Ω D = Ω D /θ g . 20 Output the set Ω D containing the locations of the detected target, and the number of elements of the set Ω D is the number of targets.…”

mentioning

confidence: 99%

Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas

Zhou

et al. 2020

IEEE Trans. Signal Process.

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“…Localization methods [6][7][8][9][10][11][12][13] in MIMO systems can be attributed to use different measurements, including time delay (TD), bistatic range (BR), angle of arrival (AOA), and their combinations. The doppler shift (DS) and bistatic range rate (BRR) can be also utilized when the source and/or receivers are moving [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the results from different groups are combined to form a composite solution. In [9] a different WLS algorithm has been proposed which solves the problem in a centralized manner. By introducing nuisance parameters, a pseudo-linear set of BR equations is established in [10].…”

Section: Introductionmentioning

confidence: 99%

An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

Liu

Gao

Zhao

et al. 2020

Preprint

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By utilizing time delay (TD) and angle of arrival (AOA) measurements, three dimensional (3D) target localization based on multi-input and multi-output (MIMO) radar systems is studied in this letter. For such a positioning problem, the typical closed-form solution firstly establishes pseudolinear equation and then applies the weighted least squares (WLS) to determine the final position.However, the WLS solution often suffers performance degradation when the noise is large. To alleviate this problem, the cost function of the WLS is re-represented and a quadratic constraint is imposed such that the expectation of the cost function can attain the minimum value at the true position. Moreover, simulation experiments show that the proposed method performs better than the state-of art algorithms.

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“…The range measurements can be estimated from received signal strength (RSS) [7], [8], time of arrival(TOA) [9], time difference of arrival (TDOA) [7], [10], and so on [7]. One can also use RSS, TOA, etc.…”

Section: Introductionmentioning

confidence: 99%

Robust Localization Using Range Measurements With Unknown and Bounded Errors

Shi

Mao

Anderson

et al. 2017

IEEE Trans. Wireless Commun.

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Cooperative geolocation has attracted significant research interests in recent years. A large number of localization algorithms rely on the availability of statistical knowledge of measurement errors, which is often difficult to obtain in practice. Compared with the statistical knowledge of measurement errors, it can often be easier to obtain the measurement error bound. This work investigates a localization problem assuming unknown measurement error distribution except for a bound on the error. We first formulate this localization problem as an optimization problem to minimize the worst-case estimation error, which is shown to be a non-convex optimization problem. Then, relaxation is applied to transform it into a convex one. Furthermore, we propose a distributed algorithm to solve the problem, which will converge in a few iterations. Simulation results show that the proposed algorithms are more robust to large measurement errors than existing algorithms in the literature. Geometrical analysis providing additional insights is also provided.

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Closed-Form Localization for Distributed MIMO Radar Systems Using Time Delay Measurements

Cited by 98 publications

References 37 publications

Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas

Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas

An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

Robust Localization Using Range Measurements With Unknown and Bounded Errors

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