Adaptive TM-CFAR Detection Based on the Statistics ODV

doi:10.13190/jbupt.201302.64.duhm

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2013, Vol. 36 ›› Issue (2): 64-69.doi: 10.13190/jbupt.201302.64.duhm

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Adaptive TM-CFAR Detection Based on the Statistics ODV

DU Hai-ming^1,2, MA Hong¹, DU Bao-qiang³

1. Department of Electronic and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;<br>2. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China;<br>3. National Laboratory of Radar Signal Processing, Xidian University, Xian 710071, China

Received:2012-05-19 Revised:2012-11-23 Online:2013-04-30 Published:2013-03-25
Contact: du haiming E-mail:duhaiming-007@163.com.

Abstract

Abstract:

Adaptive trimmed mean constant false alarm rate (ATM-CFAR) detection based on TM-CFAR detection and statistics ordered data variability (ODV) is presented. These parameters and background estimations can be selected automatically. Simulation shows that the algorithm has good detection performance under homogeneous environment and multi-target environment, and also increases its tolerance of interfering targets. Moreover, under high clutter noise ratio at clutter edge regions, the control ability on false alarm rate is much better than that of cell average CFAR detection and ordered statistics CFAR detection. Using two-level architecture and sub-block parallel processing methods, its hardware implementation and computational complexity are less than the automatic censored cell-averaging based on the statistics ODV by on-chip implementation. Furthermore, it also has the advantages of high real-time processing and is very convenient for sequential control in practice.

Key words: ordered data variability, trimmed mean constant false alarm rate detection, adaptive detection, homogenous environment

CLC Number:

TN911.7

DU Hai-ming, MA Hong, DU Bao-qiang. Adaptive TM-CFAR Detection Based on the Statistics ODV[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2013, 36(2): 64-69.

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Adaptive TM-CFAR Detection Based on the Statistics ODV

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