基于对角积分双谱的海面慢速小目标检测方法

关键; 伍僖杰; 丁昊; 刘宁波; 董云龙; 张鹏飞

doi:10.11999/JEIT210408

基于对角积分双谱的海面慢速小目标检测方法

doi: 10.11999/JEIT210408

关键¹,
伍僖杰¹,
丁昊^{1, 2, ,},
刘宁波¹,
董云龙¹,
张鹏飞³

1.
海军航空大学烟台 264001
2.
军事科学院国防科技创新研究院北京 100071
3.
中国人民解放军92975部队

基金项目: 国家自然科学基金(62101583, 61871392, 61871391)

详细信息

作者简介:
关键：男，1967年生，教授，研究方向为雷达目标检测与跟踪、侦察图像处理和信息融合

伍僖杰：男，1997年生，硕士生，研究方向为海杂波中目标检测

丁昊：男，1988年生，副教授，研究方向为海杂波特性认知与抑制、海杂波中目标检测

刘宁波：男，1983年生，副教授，研究方向为雷达信号智能处理、海上目标探测技术

董云龙：男，1974年生，教授，研究方向为多传感器信息融合

张鹏飞：男，1988年生，研究方向为飞行与指挥管理工作

通讯作者:
丁昊　hao3431@tom.com

中图分类号: TN959
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- 被引次数: 0
出版历程
- 收稿日期: 2021-05-12
- 修回日期: 2021-09-28
- 网络出版日期: 2021-10-01
- 刊出日期: 2022-07-25

A Method for Detecting Small Slow Targets in Sea Surface Based on Diagonal Integrated Bispectrum

1.
Naval Aviation University, Yantai 264001, China
2.
National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100071, China
3.
Unit 92975 of the PLA

Funds: The National Natural Science Foundation of China (62101583, 61871392, 61871391)

摘要

摘要: 针对海杂波背景下雷达对海面慢速小目标探测技术难题，该文提出一种基于对角积分双谱的三特征融合检测方法。该方法首先从待检测信号的估计双谱中获得对角积分双谱，而后根据海杂波单元与目标单元之间的非线性耦合差异性，进一步从对角积分双谱中提取峰值、质心频率、谱宽3种特征。考虑到扫描模式下雷达采用的相干脉冲数通常较少，易导致特征不稳定，进而影响海杂波与目标可分性，为此，通过多帧扫描历史数据和当前帧数据的综合应用，对谱特征进行积累得到累积峰值、全变差、累积谱宽3种累积特征。最后采用凸包分类算法，在三特征空间进行融合检测。经实测CSIR数据集验证，在同等参数条件下，该文检测方法相比已有基于时频三特征的检测方法，基于幅度、多普勒三特征检测方法和分形特征检测方法具有更好的检测性能。
- 目标检测 /
- 海杂波 /
- 对角积分双谱 /
- 三特征
Abstract: Considering the technical difficulty of radar to detect small targets embedded in the sea clutter, a three-feature fusion detection method based on diagonal integrated bispectrum is proposed. Firstly, the diagonal integrated bispectrum is obtained from the estimated bispectrum of the signal to be detected. Then, according to the nonlinear coupling difference between sea clutter cell and target cell, three features consist of peak value, centroid frequency and spectrum width are extracted from the diagonal integrated bispectrum. Considering that the number of coherent pulses used by radar in scanning mode is usually small, it is easy to lead to feature instability, and then affect the separability of sea clutter and target. For this reason, through the comprehensive application of multi-frame scanning historical data and current frame data, three cumulative features including cumulative peak value, total variation, cumulative spectrum width are obtained by accumulating three spectrum features. Finally, the convex hull classification algorithm is used to perform fusion detection in three dimensional feature space. The measured CSIR dataset verifies that, under same parameters, the proposed detection method has better detection performance compared with the existing detection methods based on three time-frequency features, amplitude feature and doppler features, fractal feature.
- Target detection /
- Sea clutter /
- Diagonal integrated bispectrum /
- Three features

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图 1 海杂波和目标单元双谱估计结果

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图 2 海杂波、目标单元对角积分双谱

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图 3 两类距离单元的对角积分双谱图

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图 4 峰值、谱宽谱及直方图

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图 5 两类累积特征直方图

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图 6 质心谱及全变差特征直方图

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图 7 不同虚警率时的凸包分类结果

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图 8 检测器框图

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图 9 4种特征空间内的巴氏距离对比

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图 10 特征来源不同时本文检测器的性能对比

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图 11 变虚警时4类检测器的性能变化(N=64)

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图 12 4类检测器的性能比较

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图 13 4类检测器的检测性能曲线

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表 1 CSIR数据库中17个数据集的环境参数

编号	数据集名称	截取时间(s)	平均风速(m/s)	有效波高(m)	夹角(°)	目标单元	信杂比(dB)
1	TFA17_001	4.45	5.40	2.26	253.70	27	10.10
2	TFA17_004	13.25	5.41	2.26	253.68	27～29	3.72
3	TFA17_005	12.17	5.42	2.26	253.68	31	12.44
4	TFA17_006	13.47	5.42	2.26	253.68	29, 30	7.57
5	TFA17_007	13.47	5.44	2.26	253.69	24	11.57
6	TFA17_008	13.47	5.44	2.26	253.70	23, 24	7.88
7	TFA17_009	4.00	5.45	2.26	253.71	23	7.25
8	TFA17_010	26.73	5.47	2.27	253.73	23, 24	9.14
9	TFA17_011	4.60	5.50	2.28	253.77	25	10.49
10	TFA17_012	39.68	6.12	2.30	254.50	14～17	9.82
11	TFA17_013	39.68	6.13	2.30	254.48	18～20	7.78
12	TFA17_014	26.73	6.26	2.35	254.12	18～20	2.61
13	TFC17_001	13.47	5.34	2.27	253.68	27, 28	9.90
14	TFC17_002	13.47	5.36	2.26	253.67	26～28	4.11
15	TFC17_004	20.00	6.10	2.28	254.55	11	13.93
16	TFC17_005	15.14	6.11	2.28	254.53	12, 13	11.84
17	TFC17_006	26.73	6.28	2.35	254.05	24～26	5.07

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表 2 特征来源不同时本文检测器的检测概率(%)

L	DIB, N=128	DSS, N=128	DIB, N=64	DSS, N=64	DIB, N=32	DSS, N=32
10	85.75	81.87	73.75	72.20	71.15	66.83
20	93.70	89.76	81.35	77.85	76.53	75.56
30	95.21	93.09	88.66	85.92	78.39	78.55
40	98.38	92.99	92.26	90.42	84.63	84.30
50	98.36	93.17	94.72	93.00	88.96	88.80
60	99.17	96.12	95.08	94.41	91.04	90.88
70	99.44	97.75	95.85	94.24	92.81	92.81
80	100.00	98.86	97.71	96.50	94.26	93.44
90	100.00	100.00	99.19	97.56	95.56	94.74
100	100.00	100.00	99.86	97.27	96.86	96.37

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表 3 4类检测器的检测概率(N=64)(%)

	本文所提检测器	时频三特征检测器	幅度、多普勒峰高和多普勒商三特征检测器	分形检测器
虚警率0.001	92.26	27.53	32.39	4.87
虚警率0.01	92.78	45.13	32.46	14.21
虚警率0.1	100.00	83.67	83.35	46.41

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表 4 相干脉冲数N降低时检测器的性能变化(%)

编号	本文所提检测器			时频三特征检测器			幅度、多普勒峰高和多普勒商三特征检测器			分形检测器
编号	N=128	N=64	降低量	N=128	N=64	降低量	N=128	N=64	降低量	N=128	N=64	降低量
1	97.01	89.61	7.40	63.58	1.44	62.14	34.10	29.39	4.71	0.00	0.29	–0.29
2	52.72	49.30	3.42	27.66	7.64	20.02	8.90	4.35	4.55	0.00	0.00	0.00
3	67.43	56.58	10.85	30.32	15.47	14.84	25.26	19.58	5.68	2.74	3.26	–0.53
4	50.62	37.28	13.34	21.90	5.99	15.92	9.14	7.51	1.63	0.00	0.29	–0.29
5	72.13	82.05	–9.92	18.06	10.27	7.79	17.87	13.31	4.56	5.51	1.62	3.90
6	38.11	38.46	–0.35	12.36	6.75	5.61	19.96	6.46	13.50	0.00	0.38	–0.38
7	50.85	40.15	10.70	27.56	18.27	9.29	19.87	12.82	7.05	0.64	0.00	0.64
8	41.75	49.17	–7.42	17.93	7.85	10.07	24.26	16.19	8.07	3.07	0.86	2.21
9	47.14	71.25	–24.11	37.99	20.61	17.38	27.37	20.61	6.76	2.79	1.11	1.68
10	88.62	89.93	–1.31	46.96	14.97	32.00	51.10	31.16	19.94	5.36	5.52	–0.15
11	72.49	70.80	1.68	30.73	12.74	17.99	19.24	11.48	7.75	0.90	0.55	0.36
12	49.10	40.82	8.28	21.26	4.84	16.42	2.78	2.64	0.14	0.38	0.91	–0.53
13	68.72	64.89	3.83	12.74	5.32	7.41	23.76	15.78	7.98	5.13	2.19	2.95
14	50.62	39.25	11.37	27.00	15.49	11.50	12.55	7.70	4.85	3.80	0.86	2.95
15	98.38	92.26	6.13	61.72	27.53	34.19	36.88	32.39	4.48	8.19	4.87	3.33
16	98.55	88.99	9.56	60.41	33.25	27.16	56.18	44.42	11.76	8.29	3.47	4.82
17	66.14	56.29	9.84	26.15	9.58	16.57	17.05	12.70	4.35	3.16	2.40	0.77

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