Joint Optimization of Time and Aperture Resource Allocation Strategy for Multi-Target ISAR Imaging in Radar Sensor Network

Wang, Dan; Zhang, Qun; Luo, Ying; Liu, Xiao-Wen; Ni, Jiacheng; Su, Linghua

doi:10.1109/jsen.2021.3090053

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…In principle, the resource-aware scheduling problem is built as a mathematical problem of optimizing the objective utility function while meeting some resource constraints, which can be divided into two typical categories according to the type of task purpose. The first type is to maximize the system performance for various applications in the context of satisfying the available system conditions [3][4][5][6][7][8][9][10][11][12]. As an example, in [3], the authors propose a joint node selection and power allocation algorithm for MTT in decentralized radar networks, which utilizes the feedback information in the tracking recursion to implement the appropriate resource allocation, aiming to improve the worst-case tracking accuracy.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

“…The authors of [7] focus on composed resource management for MTT in active and passive radar networks, the purpose of which is to collaboratively optimize the illumination resources of active radars and receive processing resources from passive radars to enhance the MTT performance. The studies in [8,9] apply the idea of time and aperture allocation to the inverse synthetic aperture radar imaging area. Other related studies can be found in [10][11][12].…”

Section: Literature Review and Motivationmentioning

confidence: 99%

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Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

et al. 2023

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This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks.

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Section: Literature Review and Motivationmentioning

confidence: 99%

Section: Literature Review and Motivationmentioning

confidence: 99%

Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

et al. 2023

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“…In practice, the long observation distance in a low-elevation angle environment may generate low SNR. Additionally, in complex electromagnetic environments with active interference and passive interference, the non-cooperative nature of target and radar resource scheduling [4] may result in incomplete echoes, and the available imaging algorithms based on Fourier analysis cannot obtain satisfactory results. Considering the sparse nature of the scattering centers in the image domain, high-resolution ISAR imaging based on sparse signal reconstruction has received intensive attention in recent years [5,6].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net

Bai

Zhou

et al. 2022

Remote Sensing

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We propose a deep learning architecture, dubbed Plug-and-play 2D ADMM-Net (PAN), by combining model-driven deep networks and data-driven deep networks for effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging with various signal-to-noise ratios (SNR) and incomplete data scenarios. First, a sparse observation model of 2D ISAR imaging is established, and a 2D ADMM algorithm is presented. On this basis, using the plug and play (PnP) technique, PnP 2D ADMM is proposed, by combining the 2D ADMM algorithm and the deep denoising network DnCNN. Then, we unroll and generalize the PnP 2D ADMM to the PAN architecture, in which all adjustable parameters in the reconstruction layers, denoising layers, and multiplier update layers are learned by end-to-end training through back-propagation. Experimental results showed that the PAN with a single parameter set can achieve noise-robust ISAR imaging with superior reconstruction performance on incomplete simulated and measured data under different SNRs.

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“…Note that, the current work mainly focuses on searching task, tracking task and anti-jamming task, etc., while few work involves the imaging task. In our previous work, we have proposed several game-theoretic based task allocation methods for ISAR imaging in radar network [20][21]. However, the task scheduling problem for multi-target 3-D imaging is much more complicated and the previous work cannot be directly applied to the problem.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Task Scheduling Algorithm for 3-D Target Imaging in Radar Network

Qun

Wang

Luo

et al. 2022

Preprint

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An effective task scheduling method is the premise and guarantee for cooperative imaging in radar network. In this article, an adaptive task scheduling algorithm for three- dimensional (3-D) target imaging in radar network is investigated. The aim of our strategy is to achieve the multiple 3-D target imaging tasks with the minimal task time. Firstly, the 3-D target image can be reconstructed by high-resolution inverse imaging aperture radar (ISAR) images from three views, and the sparse imaging algorithm based on compressed sensing (CS) is adopted to acquire the ISAR images of the targets. Then, the adaptive task scheduling optimization model is constructed. Through the steps of target Information perception, radar selection and adjustment of imaging terminal time, the optimal task scheduling strategy is obtained and the resource utilization of radar network is significantly improved. Finally, the experiments highlight the effectiveness of our proposed task scheduling method.

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Joint Optimization of Time and Aperture Resource Allocation Strategy for Multi-Target ISAR Imaging in Radar Sensor Network

Cited by 13 publications

References 30 publications

Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net

An Adaptive Task Scheduling Algorithm for 3-D Target Imaging in Radar Network

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