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Journal of Sensors
Volume 2017 (2017), Article ID 8723042, 14 pages
Research Article

Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

Yijun Chen,1,2 Qun Zhang,1,2,3 Ying Luo,1,2,3 and Tat Soon Yeo4

1Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China
3Key Laboratory for Information Science of Electromagnetic Waves, Ministry of Education, Fudan University, Shanghai 200433, China
4Department of Electrical and Computer Engineering, National University of Singapore, 11576, Singapore

Correspondence should be addressed to Qun Zhang

Received 29 May 2017; Accepted 16 July 2017; Published 22 August 2017

Academic Editor: Hyung-Sup Jung

Copyright © 2017 Yijun Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The micromotion feature of space target provides an effective approach for target recognition. The existing micromotion feature extraction is implemented after target detection and tracking; thus the radar resources need to be allocated for target detection, tracking, and feature extraction, successively. If the feature extraction can be implemented by utilizing the target detecting and tracking pulses, the radar efficiency can be improved. In this paper, by establishing a feedback loop between micromotion feature extraction and track-before-detect (TBD) of target, a novel feature extraction method for space target is proposed. The TBD technology is utilized to obtain the range-slow-time curves of target scatterers. Then, micromotion feature parameters are estimated from the acquired curve information. In return, the state transition set of TBD is updated adaptively according to these extracted feature parameters. As a result, the micromotion feature parameters of space target can be extracted concurrently with implementing the target detecting and tracking. Simulation results show the effectiveness of the proposed method.