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Decomposition optimization algorithms for distributed multiple radar systems

Decomposition optimization algorithms for distributed multiple radar systems
Decomposition optimization algorithms for distributed multiple radar systems
Distributed radar systems are capable of enhancing the detection performance by using multiple widely spaced distributed antennas. With prior statistic information of targets, resource allocation is of critical importance for further improving the system’s achievable performance. In this paper, the total transmitted power is minimized at a given mean-square target-estimation error.We derive two iterative decomposition algorithms for solving this nonconvex constrained optimization problem, namely, the optimality condition decomposition (OCD)-based and the alternating direction method of multipliers (ADMM)-based algorithms. Both the convergence performance and the computational complexity of our algorithms are analyzed theoretically, which are then confirmed by our simulation results. The OCD method imposes a much lower computational burden per iteration, while the ADMM method exhibits a higher per-iteration complexity, but as a benefit of its significantly faster convergence speed, it requires less iterations. Therefore, theADMMimposes a lower total complexity than the OCD. The results also show that both of our schemes outperform the state-of-the-art benchmark scheme for the multiple target case, in terms of the total power allocated, at the cost of some degradation in localization accuracy. For the single-target case, all the three algorithms achieve similar performance. Our ADMM algorithm has similar total computational complexity per iteration and convergence speed to those of the benchmark.
1053-587X
6443-6458
Ma, Ying
317488f2-71ec-4557-b42c-482870d74371
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Xing, Chengwen
2477f24d-3711-47b1-b6b4-80e2672a48d1
Bu, Xiangyuan
e3f1fd25-0ca6-4aa9-968e-4fb03a3189a0
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Ma, Ying
317488f2-71ec-4557-b42c-482870d74371
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Xing, Chengwen
2477f24d-3711-47b1-b6b4-80e2672a48d1
Bu, Xiangyuan
e3f1fd25-0ca6-4aa9-968e-4fb03a3189a0
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Ma, Ying, Chen, Sheng, Xing, Chengwen, Bu, Xiangyuan and Hanzo, Lajos (2016) Decomposition optimization algorithms for distributed multiple radar systems. IEEE Transactions on Signal Processing, 64 (24), 6443-6458. (doi:10.1109/TSP.2016.2602801).

Record type: Article

Abstract

Distributed radar systems are capable of enhancing the detection performance by using multiple widely spaced distributed antennas. With prior statistic information of targets, resource allocation is of critical importance for further improving the system’s achievable performance. In this paper, the total transmitted power is minimized at a given mean-square target-estimation error.We derive two iterative decomposition algorithms for solving this nonconvex constrained optimization problem, namely, the optimality condition decomposition (OCD)-based and the alternating direction method of multipliers (ADMM)-based algorithms. Both the convergence performance and the computational complexity of our algorithms are analyzed theoretically, which are then confirmed by our simulation results. The OCD method imposes a much lower computational burden per iteration, while the ADMM method exhibits a higher per-iteration complexity, but as a benefit of its significantly faster convergence speed, it requires less iterations. Therefore, theADMMimposes a lower total complexity than the OCD. The results also show that both of our schemes outperform the state-of-the-art benchmark scheme for the multiple target case, in terms of the total power allocated, at the cost of some degradation in localization accuracy. For the single-target case, all the three algorithms achieve similar performance. Our ADMM algorithm has similar total computational complexity per iteration and convergence speed to those of the benchmark.

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More information

Accepted/In Press date: 9 August 2016
e-pub ahead of print date: 25 August 2016
Published date: 15 December 2016
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 401898
URI: http://eprints.soton.ac.uk/id/eprint/401898
ISSN: 1053-587X
PURE UUID: 822647bf-a8e0-4acb-a43d-8b0cd4106efe
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 24 Oct 2016 15:20
Last modified: 07 Oct 2020 01:33

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