Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

Author:

Chen Yijun12ORCID,Zhang Qun23ORCID,Luo Ying34ORCID,Yeo Tat Soon4

Affiliation:

1. College of Information Engineering, Engineering University of CAPF, Xi’an 710086, China

2. Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

3. Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China

4. Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583

Abstract

The micromotion feature extraction method based on track-before-detect (TBD) can save the radar resource and improve the real-time performance of micromotion feature extraction by implementing target detecting, tracking, and micromotion feature extraction simultaneously. Usually, multitargets will exist in different areas, and the limited radar resources should be allocated for different areas to achieve the maximal performance of radar. For single-beam phased array radar, an adaptive resource allocation optimization model is established according to the processing steps of the micromotion feature extraction method based on TBD, and an adaptive resource allocation strategy is proposed. With the method, the radar efficiency can be significantly improved. The effectiveness of the proposed method is demonstrated by simulations.

Funder

Ministry of Education - Singapore

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Iterative recovery algorithm for Incomplete and Phase-Corrupted micro-Doppler signals in real-time;2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE);2022-05-27

2. Obtaining TFR From Incomplete and Phase-Corrupted m-D Signal in Real Time;IEEE Geoscience and Remote Sensing Letters;2022

3. Lightweight EEG Classification Model Based on EEG-sensor with Few Channels;2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC);2019-10

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