CRS-Based Optimal Radar Placement for Target Positioning

Yoo, Kyungwoo; Chun, Joohwan; Ryu, Chung-Ho

doi:10.1109/radar.2018.8557308

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Hybrid measurements Correlated noise Non-uniform noise variances Optimality criteria [28] No No No A [22], [24], [27], [29], [30] No No No D [34]- [38] No No Yes A [31]- [33] No No Yes D [40] No Yes Yes A, D and E [39], [41] Yes No No A, D [42] Yes No No D [23], [43] Yes No No A Proposed Algorithm Yes Yes Yes A, D and E optimality criteria. Section IV gives the details of numerical simulations and the results.…”

Section: Papermentioning

confidence: 99%

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Optimal Sensor Placement for Hybrid Source Localization Using Fused TOA-RSS-AOA Measurements

Panwar¹,

Fatima²,

Babu³

2022

Preprint

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Source localization techniques incorporating hybrid measurements improve the reliability and accuracy of the location estimate. Given a set of hybrid sensors that can collect combined time of arrival (TOA), received signal strength (RSS) and angle of arrival (AOA) measurements, the localization accuracy can be enhanced further by optimally designing the placements of the hybrid sensors. In this paper, we present an optimal sensor placement methodology, which is based on the principle of majorization-minimization (MM), for hybrid localization technique. We first derive the Cramer-Rao lower bound (CRLB) of the hybrid measurement model, and formulate the design problem using the A-optimal criterion. Next, we introduce an auxiliary variable to reformulate the design problem into an equivalent saddle-point problem, and then construct simple surrogate functions (having closed form solutions) over both primal and dual variables. The application of MM in this paper is distinct from the conventional MM (that is usually developed only over the primal variable), and we believe that the MM framework developed in this paper can be employed to solve many optimization problems. The main advantage of our method over most of the existing state-ofthe-art algorithms (which are mostly analytical in nature) is its ability to work for both uncorrelated and correlated noise in the measurements. We also discuss the extension of the proposed algorithm for the optimal placement designs based on D and E optimal criteria. Finally, the performance of the proposed method is studied under different noise conditions and different design parameters.

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Section: Papermentioning

confidence: 99%

“…The above mentioned problem formulation looks similar to (23) and the proposed method can be easily adapted to solve for a minimizer of (74).…”

Section: Outputmentioning

confidence: 99%

Optimal Sensor Placement for Hybrid Source Localization Using Fused TOA-RSS-AOA Measurements

Panwar¹,

Fatima²,

Babu³

2022

Preprint

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“…received-signal-strength (RSS) [6] and frequency-differenceof-arrival (FDOA) [7]. Apart from the employed estimation approaches, the localization accuracy also depends on the target-sensor geometry [8], [9]. Specifically, the mean squared error (MSE) in the estimation of the source position will be a function of the sensor geometry.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Placement for Source Localization: A Unified ADMM Approach

Sahu,

Wu,

Babu

et al. 2021

Preprint

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Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a unified optimization framework for optimal sensor placement (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework.

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“…The commonly used approaches are based on time-of-arrival (TOA) [3], angleof-arrival (AOA) [4], time-difference-of-arrival (TDOA) [5], received-signal-strength (RSS) [6] and frequency-differenceof-arrival (FDOA) [7]. Apart from the employed estimation approaches, the localization accuracy also depends on the target-sensor geometry [8], [9]. Specifically, the mean squared error (MSE) in the estimation of the source position will be a function of the sensor geometry.…”

mentioning

confidence: 99%

Optimal Sensor Placement for Source Localization: A Unified ADMM Approach

Sahu

Babu

et al. 2022

IEEE Trans. Veh. Technol.

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Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a unified optimization framework for optimal sensor placement (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorizationminimization (MM) techniques. Unlike the majority of the stateof-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework.

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CRS-Based Optimal Radar Placement for Target Positioning

Cited by 7 publications

References 10 publications

Optimal Sensor Placement for Hybrid Source Localization Using Fused TOA-RSS-AOA Measurements

Optimal Sensor Placement for Hybrid Source Localization Using Fused TOA-RSS-AOA Measurements

Optimal Sensor Placement for Source Localization: A Unified ADMM Approach

Optimal Sensor Placement for Source Localization: A Unified ADMM Approach

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