Optimal Deployment Analysis of Airborne Radars Detecting Stealth Target Using UAV Swarms

Radar deployment is of great importance for airborne radar network performance. An efficient deployment required a good detection performance and energy consumption. This paper focuses on the detection of directional stealth target using UAV swarms equipped with active and passive radars. Based on c...

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Bibliographic Details
Published in2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC) pp. 1 - 8
Main Authors Wang, Weijia, Bai, Peng, Liang, Xiaolong, Zhang, Jiaqiang, Hu, Liping
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
bistatic radars
multi-objective optimization
Passive radar
Radar cross-sections
radar deployment
Radar detection
Receivers
Sensors
stealth target
Transmitters
wolf pack algorithm
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Summary:Radar deployment is of great importance for airborne radar network performance. An efficient deployment required a good detection performance and energy consumption. This paper focuses on the detection of directional stealth target using UAV swarms equipped with active and passive radars. Based on calculated bistatic radar cross section (RCS) of stealth targets and Cassini oval sensing region, the optimum geometrical structures are analyzed with bistatic radars (BRs) using multiple mobile adaptive forward receivers to extend the dynamic detection range and coverage. Multi-objective optimization model is introduced and many critical constraints have been identified. Grid partition method and wolf pack algorithm (WPA) are applied to search the optimal deployments. In particular, we consider the problem of deploying an airborne network of BRs in the surveillance region to maximize the worst-case intrusion detectability as well as the vulnerability of active radar. Our findings provide valuable and practical insights into deployment of UAVs for stealth target tracking and the efficiency of the proposed algorithm is also validated by our simulation results.
DOI:10.1109/GNCC42960.2018.9019101