JOURNAL ARTICLE

Joint Node Selection and Power Allocation Optimization for Multi-Target ISAR Imaging in Radar Network

Dan WangQun ZhangLi ZhuQingwei MengJia LiangYing Luo

Year: 2022 Journal:   2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pages: 1-6

Abstract

Inverse synthetic aperture radar (ISAR) imaging technology has been widely used in military and civilian areas due to its ability of obtaining the fine structure of a target. However, the contradiction in the use of radar resources is highlighted when facing multi-target ISAR imaging problem. Compared with a single radar, the radar network which is constituted of many dispersed radars is expected to solve the multi-target imaging problem. An efficient resource allocation method plays an important role in guaranteeing the successful completion of the multi-target imaging task and improving the resource utilization of radar network. In this paper, according to the ISAR imaging principle, the relationship between the mission time and radar resources (i.e., radar node and radar power) is analyzed first. Thus the joint node selection and power allocation optimization model for multi-target ISAR imaging in radar network is constructed and the purpose is to minimize the mission time of the multi-target ISAR imaging tasks. Then the optimal radar node selection and power allocation scheme can be further obtained by the circular iterative method of relaxed convex optimization. Simulations demonstrate the effectiveness the proposed method.

Keywords:
Inverse synthetic aperture radar Computer science Radar Radar imaging Radar engineering details Artificial intelligence Real-time computing Remote sensing Telecommunications Geography

Metrics

2
Cited By
0.65
FWCI (Field Weighted Citation Impact)
8
Refs
0.62
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced SAR Imaging Techniques
Physical Sciences →  Engineering →  Aerospace Engineering
Radar Systems and Signal Processing
Physical Sciences →  Engineering →  Aerospace Engineering
Sparse and Compressive Sensing Techniques
Physical Sciences →  Engineering →  Computational Mechanics

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