非均匀杂波背景下的自适应检测器设计

doi:10.12305/j.issn.1001-506X.2024.11.03

Abstract

Abstract:

To enhance the constant false alarm detector's robustness and anti-interference performance in a variety of detection environments, an integrated adaptive detector with multi-strategy selection capability and dynamic adjustment of detection parameters is proposed. This detector introduces the Bayesian information criteria and computes the information estimation sequence of the reference units. Then the present detection background type is identified. Additionally, this detector can estimate the quantity of interfering targets or the location of clutter edges in the inhomogeneous background. Finally, the detection strategy and the detection parameters are chosen based on the recognition and estimation results. The performance of the proposed detector that and it is compared with that of traditional detectors using Monte Carlo simulation. Results of the simulation show that the proposed detector has the ability to recognize the type of background and estimate background parameters. Simultaneously, the detector also has strong anti-interference capabilities without enough prior knowledge of background parameters.

Key words: constant false alarm rate (CFAR), adaptive detection, information criteria, multi-target, clutter edge

CLC Number:

TN957.51

Dongsheng ZHU, Xiaojing SU, Zhixun MA, Yingjie SHI. Design of adaptive detector for inhomogeneous clutter background[J]. Systems Engineering and Electronics, 2024, 46(11): 3613-3620.

Figures/Tables 17

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References 30

1	何友. 雷达目标检测与恒虚警处理[M]. 关键, 孟祥伟. 2版. 北京: 清华大学出版社, 2011.
	HE Y. Radar target detection and CFAR processing[M]. GUAN J, MENG X W. 2nd ed. Beijing: Tsinghua University Press, 2011.
2	郭辰锋. 复杂背景目标检测技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2020.
	GUO C F. Reasearch on target detection technology with complex backgroud[D]. Harbin: Harbin Institute of Technology, 2020.
3	XU Y W , HOU C H , YAN S F , et al. Fuzzy statistical norma-lization CFAR detector for non-rayleigh data[J]. IEEE Trans.on Aerospace and Electronic Systems, 2015, 51 (1): 383- 396. doi: 10.1109/TAES.2014.130683
4	CAROTENUTO V , DEMAIO A . A clustering approach for jamming environment classification[J]. IEEE Trans.on Aerospace and Electronic Systems, 2021, 57 (3): 1903- 1918. doi: 10.1109/TAES.2021.3050655
5	DEMAIO A . Invariance theory for adaptive radar detection in heterogeneous environment[J]. IEEE Signal Processing Letters, 2019, 26 (7): 996- 1000. doi: 10.1109/LSP.2019.2916749
6	LI L T, SHI J Y, LI Y Y, et al. An IOSGO-CFAR algorithm based on clutter classification and recognition[C]//Proc. of the IEEE 15th International Conference on Solid-State and Integrated Circuit Technology, 2020.
7	BAADECHE M , SOLTANI F . Performance analysis of mean level constant false alarm rate detectors with binary integration in Weibull background[J]. IET Radar Sonar and Navigation, 2015, 9 (3): 233- 240. doi: 10.1049/iet-rsn.2014.0053
8	COLUCCIA A , FASCISTA A , RICCI G . A k-nearest neighbors approach to the design of radar detectors[J]. Signal Processing, 2020, 174, 107609. doi: 10.1016/j.sigpro.2020.107609
9	SMITH M E , VARSHNEY P K . Intelligent CFAR processor based on data variability[J]. IEEE Trans.on Aerospace and Electronic Systems, 2000, 36 (3): 837- 847. doi: 10.1109/7.869503
10	CAO T T . Constant false-alarm rate algorithm based on test cell information[J]. IET Radar Sonar and Navigation, 2008, 2 (3): 200- 213. doi: 10.1049/iet-rsn:20070133
11	ADDABBO P , HAN S D , ORLANDO D , et al. Learning strategies for radar clutter classification[J]. IEEE Trans.on Signal Processing, 2021, 69, 1070- 1082. doi: 10.1109/TSP.2021.3050985
12	LIU J , BIONDI F , ORLANDO D , et al. Training data classification algorithms for radar applications[J]. IEEE Signal Processing Letters, 2019, 26 (10): 1446- 1450. doi: 10.1109/LSP.2019.2935625
13	TAO D , ANFINSEN S N , BREKKE C . Robust CFAR detector based on truncated statistics in multiple-target situations[J]. IEEE Trans.on Geoscience and Remote Sensing, 2016, 54 (1): 117- 134. doi: 10.1109/TGRS.2015.2451311
14	METCALF J, BLUNT S D, HIMED B. A machine learning approach to cognitive radar detection[C]//Proc. of the IEEE Radar Conference, 2015: 1405-1411.
15	曲超, 郝程鹏, 杨树元. 基于自动删除算法的恒虚警检测器[J]. 数据采集与处理, 2008, 23 (5): 14- 18.
	QU C , HAO C P , YANG S Y . CFAR detector based on automatic censoring algorithm[J]. Journal of Data Acquisition and Processing, 2008, 23 (5): 14- 18.
16	张永光, 荣锋. 一种改进的自适应恒虚警检测器[J]. 电子测量技术, 2022, 45 (11): 83- 89.
	ZHANG Y G , RONG F . An improved adaptive CFAR detector[J]. Electronic Measurement Technology, 2022, 45 (11): 83- 89.
17	MAGAZ B , BELOUCHRANI A , HAMADOUCHE M H . Automatic threshold selection in OS-CFAR radar detection using information theoretic criteria[J]. Progress in Electromagnetics Research B, 2011, 30, 157- 175. doi: 10.2528/PIERB10122502
18	ZHU D S, SU X J, MA Z X, et al. Intelligent CFAR detector against interference of multiple targets[C]//Proc. of the 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, 2022: 72-76.
19	WANG L O , WAND D H , HAO C P . Intelligent CFAR detector based on support vector machine[J]. IEEE Access, 2017, 5, 26965- 26972. doi: 10.1109/ACCESS.2017.2774262
20	刘恒燕, 宋杰, 熊伟, 等. 大入射余角CFAR检测器[J]. 系统工程与电子技术, 2019, 41 (6): 1218- 1223. doi: 10.3969/j.issn.1001-506X.2019.06.07
	LIU H Y , SONG J , XIONG W , et al. Large-grazing-angle CFAR detector[J]. Systems Engineering and Electronics, 2019, 41 (6): 1218- 1223. doi: 10.3969/j.issn.1001-506X.2019.06.07
21	芮义斌, 魏知寒, KHOASANGN, 等. 一种非均匀背景下的多策略CFAR检测器[J]. 南京理工大学学报, 2016, 40 (2): 199- 203.
	RUI Y B , WEI Z H , KHOASANG N , et al. Multiple strategy CFAR detector in nonhomogeneous environment[J]. Journal of Nanjing University of Science and Technology, 2016, 40 (2): 199- 203.
22	QI Q, HU W. One efficient target detection based on neural network under homogeneous and non-homogeneous background[C]//Proc. of the IEEE 17th International Conference on Communication Technology, 2017: 1503-1507.
23	YIN C R, YAN L J, HAO C P, et al. Novel classification schemes for radar scenarios: design and comparison[C]//Proc. of the 30th European Signal Processing Conference, 2022: 1861-1865.
24	HAN S D , YAN L J , ZHANG Y X , et al. Adaptive radar detection and classification algorithms for multiple coherent signals[J]. IEEE Trans.on Signal Processing, 2021, 69, 560- 572. doi: 10.1109/TSP.2020.3047523
25	STOICA P , SELEN Y . Model-order selection: a review of information criterion rules[J]. IEEE Signal Processing Magazine, 2004, 21 (4): 36- 47. doi: 10.1109/MSP.2004.1311138
26	MARIANI A , GIORGETTI A , CHIANI M . Model order selection based on information theoretic criteria: design of the penalty[J]. IEEE Trans.on Signal Processing, 2015, 63 (11): 2779- 2789. doi: 10.1109/TSP.2015.2414900
27	CAROTENUTO V , DEMAIO A , ORLANDO D , et al. Model order selection rules for covariance structure classification in radar[J]. IEEE Trans.on Signal Processing, 2017, 65 (20): 5305- 5317. doi: 10.1109/TSP.2017.2728523
28	DRIDI N , HADZAGIC M . Akaike and bayesian information criteria for hidden markov models[J]. IEEE Signal Processing Letters, 2018, 26 (2): 302- 306.
29	魏嘉, 徐达, 闫晟, 等. OSSO和OSGO恒虚警检测器在Pareto分布混响背景下的性能分析[J]. 信号处理, 2019, 35 (9): 8.
	WEI J , XU D , YAN S , et al. Performance analysis of OSSO-and OSGO-CFAR in Pareto distribution reverberation[J]. Journal of Signal Processing, 2019, 35 (9): 8.
30	朱东升, 宿晓静, 刘晋伟, 等. 均匀混响背景下抗多目标干扰自适应检测器设计[J]. 水下无人系统学报, 2022, 30 (4): 422- 428.
	ZHU D S , SU X J , LIU J W , et al. Design of a multi-target interference resistant adaptive detector under Homogeneous reverberation backgrounds[J]. Journal of Unmanned Undersea Systems, 2022, 30 (4): 422- 428.

相关性	递增	背景	背景功率方法
>80%	是	均匀背景	$\sum\limits_{i=1}^N x_i $
>80%	否	杂波边缘	$\max \left(\sum\limits_{i=1}^m x_i, \sum\limits_{i=m+1}^N x_i\right) $
≤80%	-	目标干扰	$\sum\limits_{i=N-k}^N \lambda_i $

检测器	信噪比/dB
检测器	5	15	25
CA	0.004 6	0.007 4	0.007 5
OS	0.008 1	0.009 2	0.009 3
VI	1.83e-4	3.47e-4	3.64e-4
ACCA	0.007 9	0.064 4	0.065 9
OSGO	1.0e-4	1.04e-4	1.07e-4
TB	0.016 0	0.060 9	0.060 0
BIC	0.011 4	0.032 5	9.2e-5

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Design of adaptive detector for inhomogeneous clutter background

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