An Automatic Detection Algorithm Based on Wideband Spatial Spectrum of Passive Sonar

doi:10.12382/bgxb.2022.0170

Abstract

Abstract:

In passive sonar boardband target detection, detection based on the fixed threshold cannot adapt to the fluctuating background, and it is easy to detect sidelobes or miss weak targets when detecting strong and weak targets coexisting in the scenario. In addition, the targets occupying multiple beams in the detection results can easily generate beam selection bias for accurate direction finding. To solve these problems, the broadband spatial spectrum is used as the test statistic, and the sorted truncation average algorithm, local peak detection and beam interpolation algorithm are combined. Through theoretical and Monte Carlo experiments, the parameters affecting the estimated threshold value are analyzed, and an automatic parameter selection scheme is designed. A detection threshold adaptive method and an integrated scheme of automatic detection and accurate direction finding are developed. The simulations and sea trials have verified the effectiveness of the proposed algorithm and have strong engineering application value.

Key words: wideband target detection, automatic detection, sorted truncation average, local peak detection, beam interpolation

LIU Song, YAO Zhixiang, LU Daiqiang, YUAN Jun. An Automatic Detection Algorithm Based on Wideband Spatial Spectrum of Passive Sonar[J]. Acta Armamentarii, 2023, 44(6): 1764-1774.

Figures/Tables 15

Fig.1 Angle axis sliding window

Fig.2 BSTAD algorithm flow

Fig.3 Curve of false alarm probability varying with α

Fig.4 Detection probability

Fig.5 False alarm probability curves for different K values

Fig.6 Detection probability at -20dB

Fig.7 Detection probability curves for different α values

Fig.8 False alarm probability curves for different α values

Fig.9 Azimuth history map of 17 targets

Fig.10 BSTAD detection results under different K values

Fig.11 Azimuth history map when strong and weak targets coexist

Fig.12 Comparison of test results of two different detection algorithms

Fig.13 Root mean square error of angle estimation in different cases

Fig.14 The azimuth history map of the sea trial experiment

Fig.15 Comparison of test results of two different detection algorithms

References 22

[1]	王晓宇, 杨益新, 史阳. 一种基于波束扫描特征的被动声纳背景均衡方法[C]//2010年全国声学学术会议论文集. 哈尔滨: 中国声学学会, 2010:2.
	WANG X Y, YANG Y X, SHI Y. Background equalization for passive sonar based on beam characterristics[C]//Proceedings of 2010 National Acoustic Conference. Harbin: Acoustic Society of China, 2010:2. (in Chinese)
[2]	吴国清, 陈永强, 李乐强, 等. 水声瞬态信号短时谱形态及谱相关法检测[J]. 声学学报, 2000(6): 510-515.
	WU G Q, CHEN Y Q, LI L Q, et al. The analysis of underwater transient signal and their detection by spectral correlation[J]. Acta Acustica, 2000(6): 510-515. (in Chinese)
[3]	王晓燕, 方世良, 朱志峰. 一种基于自相关估计的水声直扩信号检测方法[J]. 东南大学学报(自然科学版), 2010, 40(2): 248-252.
	WANG X Y, FANG S L, ZHU Z F. Detection method of acoustic direct-sequence spread-spectrum signal based on autocorrelation estimation[J]. Journal of Southeast University(Natural Science Edition), 2010, 40(2): 248-252. (in Chinese)
[4]	郑恩明, 田甜, 陈新华, 等. 基于波束域相位稳定性的目标检测方法研究[J]. 电子学报, 2016, 44(9): 2033-2039. doi: 10.3969/j.issn.0372-2112.2016.09.001
	ZHENG E M, TIAN T, CHEN X H, et al. Target detection method based on the stability of beam domain phase[J]. Acta Electronica Sinica, 2016, 44(9): 2033-2039. (in Chinese)
[5]	BONO M, SHAPO B, MCCARTY P. Subband energy detection in passive array processing:ADA405484[R]. Fort Belvoir, VA, US: Defense Technical Information Center, 2001: 25-30.
[6]	杨陈辉, 马远良, 杨益新. 峰值能量检测及其在被动声纳显示中的应用[J]. 应用声学, 2003(5): 31-35.
	YANG C H, MA Y L, YANG Y X. Peak energy detection with application to passive sonar display[J]. Applied Acoustics, 2003(5): 31-35. (in Chinese)
[7]	张大伟, 范文涛, 许林周, 等. 一种基于色彩处理的被动声呐宽带目标检测显示方法[J]. 声学技术, 2021, 40(4): 556-559.
	ZHANG D W, FAN W T, XU L Z, et al. A method of detecting and displaying passive sonar broadband target based on color processing[J]. Technical Acoustics, 2021, 40(4): 556-559. (in Chinese)
[8]	夏文杰, 蔡志明. 未知正弦信号的功率谱熵检测性能分析[J]. 信号处理, 2018, 34(12): 1499-1507.
	XIA W J, CAI Z M. Performance analysis of power spectrum entropy detection for unknown sinusoidal signals[J]. Journal of Signal Processing, 2018, 34(12): 1499-1507. (in Chinese)
[9]	马永峥. 基于排列熵和恒虚警检测的水下目标信号检测技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2020: 2-24.
	MA Y Z. Research on underwater target signal detection technology based on permutation entropy and constant false alarm detection[D]. Harbin: Harbin Engineering University, 2020: 2-24. (in Chinese)
[10]	BOGERT B P, OSSANNA J F. The heuristics of cepstrum analysis of a stationary complex echoed gaussian signal in stationary gaussian noise[J]. IEEE Transactions on Information Theory, 1966, 12(3): 373-380. doi: 10.1109/TIT.1966.1053903 URL
[11]	余文晶, 何琳, 徐荣武, 等. 典型瞬态声信号倒谱域时延估计方法[J]. 华中科技大学学报(自然科学版), 2018, 46(4): 68-73.
	YU W J, HE L, XU R W, et al. Time delay estimation method in cepstrum domain for typical transient signals[J]. Journal of Huazhong University of Science and Technology, 2018, 46(4): 68-73. (in Chinese)
[12]	HINICH M J. Detecting a transient signal by bispectral analysis[J]. IEEE Transactions on Acoustics Speech & Signal Processing, 1990, 38(7): 1277-1283.
[13]	严侃, 雷江涛. 基于时频分析的水声目标被动检测模型研究[J]. 鱼雷技术, 2015, 23(1): 26-29.
	YAN K, LEI J T. Passive detection models of underwater acoustic target based on time-frequency analysis[J]. Torpedo Technology, 2015, 23(1):26-29. (in Chinese)
[14]	李山, 王德俊, 王海斌. 一种基于线谱特征函数提取LOFAR图线谱的方法[J]. 声学技术, 2016, 35(4): 373-377.
	LI S, WANG D J, WANG H B. An approach to lofargram spectrum line detection based on the spectrum line feature function[J]. Technical Acoustics, 2016, 35(4): 373-377. (in Chinese)
[15]	SAMUEL J D, HAN X G. Track-before-detect using expectation maximisation, signals and communication technology[M]. Singapore: Springer Nature Press, 2018: 235-285.
[16]	NORTHARDT T. A Cramér-Rao lower bound derivation for passive sonar track-before-detect algorithms[J]. IEEE Transactions on Information Theory, 2020, 66(10): 6449-6457. doi: 10.1109/TIT.18 URL
[17]	王学敏, 张翔宇, 吴明辉, 等. 基于交叉定位和Hough变换检测前跟踪的水下目标检测方法[J/OL]. 系统工程与电子技术, 2022(2022-04-28). http://kns.cnki.net/kcms/detail/11.2422.tn.20220427.1209.002.html.
	WANG X M, ZHANG X Y, WU M H, et al. Underwater target detection method based on cross location and Hough transform track-before-detect[J/OL]. Systems Engineering and Electronics, 2022(2022-04-28). http://kns.cnki.net/kcms/detail/11.2422.tn.20220427.1209.002.html. in Chinese)
[18]	卢术平, 胡鹏, 丁烽. 复杂非均匀背景下的鲁棒声呐恒虚警检测算法[J]. 声学技术, 2020, 39(6): 744-751.
	LU S P, HU P, DING F. Robust sonar CFAR detection algorithm in complex non-homogeneous background[J]. Technical Acoustics, 2020, 39(6): 744-751. (in Chinese)
[19]	李启虎, 潘学宝, 尹力. 数字式声呐中一种新的背景均衡算法[J]. 声学学报, 2000(1):5-9.
	LI Q H, PAN X B, YIN L. A new algorithm of background equalization[J]. Acta Acustica, 2000(1):5-9. (in Chinese)
[20]	郑佼. 被动声纳显示优化技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2013.
	ZHENG J. Research on passive sonar display optimization technology[D]. Harbin: Harbin Engineering University, 2013. (in Chinese)
[21]	官林杰. 基于矢量水听器阵列的目标跟踪定位[D]. 南京: 东南大学, 2017: 8-14.
	GUAN L J. Target tracking and positioning based on vector hydrophone array[D]. Nanjing: School of Information Science and Engineering Southeast University, 2017: 8-14. (in Chinese)
[22]	姚直象, 姜可宇, 苏林, 等. 声呐被动定位技术[M]. 北京: 兵器工业出版社, 2021: 26-28.
	YAO Z X, JIANG K Y, SU L, et al. Sonar passive positioning technology[M]. Beijing: Publishing House of Ordnance Industry, 2021: 26-28. (in Chinese)