基于特征時(shí)序性的海雜波環(huán)境小目標(biāo)檢測(cè)方法

董云龍; 羅霄; 丁昊; 王國慶; 劉寧波

doi:10.11999/JEIT240528

基于特征時(shí)序性的海雜波環(huán)境小目標(biāo)檢測(cè)方法

doi: 10.11999/JEIT240528 cstr: 32379.14.JEIT240528

海軍航空大學(xué) 煙臺(tái) 264001

基金項(xiàng)目: 國家自然科學(xué)基金(62388102, 62101583)

詳細(xì)信息

作者簡(jiǎn)介:
董云龍：男，博士，教授，研究方向?yàn)槔走_(dá)目標(biāo)組網(wǎng)檢測(cè)等

羅霄：男，碩士生，研究方向?yàn)槔走_(dá)信號(hào)處理、目標(biāo)檢測(cè)等

丁昊：男，博士，副教授，研究方向?yàn)楹ｋs波特性認(rèn)知、雷達(dá)目標(biāo)檢測(cè)等

王國慶：男，博士，副教授。研究方向?yàn)槔走_(dá)信號(hào)目標(biāo)檢測(cè)、電路系統(tǒng)設(shè)計(jì)等

劉寧波：男，博士，教授，研究方向?yàn)槔走_(dá)信號(hào)處理、海雜波抑制與目標(biāo)智能檢測(cè)

通訊作者:
丁昊　hao3431@tom.com

中圖分類號(hào): TN957.51
計(jì)量
- 文章訪問數(shù): 242
- HTML全文瀏覽量: 93
- PDF下載量: 60
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-06-25
- 修回日期: 2025-02-15
- 網(wǎng)絡(luò)出版日期: 2025-02-24
- 刊出日期: 2025-03-01

A Detection Method of Small Target in Sea Clutter Environment Based on Feature Temporal Sequence

Naval Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (62388102, 62101583)

摘要

摘要: 特征檢測(cè)作為海雜波環(huán)境下小目標(biāo)檢測(cè)的有效手段，受到了廣泛關(guān)注與深入研究。過去對(duì)特征的研究大多關(guān)注于當(dāng)前幀，近年來使用幀間時(shí)序信息融合當(dāng)前幀特征的方法也被提出并在檢測(cè)方面取得一定效果。但該方法不能很好地適應(yīng)具有時(shí)變性的海雜波數(shù)據(jù)，且僅采用靜態(tài)加權(quán)算法融合特征，對(duì)歷史幀信息的利用不夠充分。針對(duì)上述問題，該文提出基于模型穩(wěn)定的修正Burg方法進(jìn)行特征自回歸(AR)建模與一步預(yù)測(cè)，使模型能夠自適應(yīng)調(diào)整極點(diǎn)分布，提高了海雜波特征預(yù)測(cè)的準(zhǔn)確性，并基于求解多變量極值問題提出了一種動(dòng)態(tài)加權(quán)算法得到了最小方差的融合特征。該文結(jié)合IPIX數(shù)據(jù)集和海軍航空大學(xué)共享數(shù)據(jù)集進(jìn)行實(shí)驗(yàn)，利用相對(duì)平均幅度(RAA)、相對(duì)多普勒峰高(RDPH)、頻域峰均值比(FPAR)3特征構(gòu)建凸包檢測(cè)器驗(yàn)證了所提方法的有效性。
- 小目標(biāo)檢測(cè) /
- 海雜波 /
- 特征時(shí)序信息 /
- 修正Burg方法 /
- 動(dòng)態(tài)加權(quán)
Abstract: Objective Feature detection has become an effective approach for detecting small targets in sea clutter environments, attracting significant attention and research. Previous studies primarily focused on extracting differential features between targets and clutter from the current pulse frame for detection. Recent methods have integrated temporal information from multiple frames with current frame features, demonstrating improved detection performance. However, these methods rely on fixed-order Auto Regressive (AR) models, which do not effectively adapt to the time-varying nature of sea clutter. Moreover, the use of static weighting algorithms for feature fusion fails to account for clutter characteristics in the current scene, leading to suboptimal utilization of temporal information. To address these issues, this study proposes a feature AR modeling and one-step prediction method based on a model-stable modified Burg algorithm, enabling adaptive pole distribution adjustment and enhancing the accuracy of sea clutter feature prediction. Additionally, a dynamic weighting algorithm is developed by solving multivariable extreme value problems to obtain minimum variance fused features, fully leveraging historical frame temporal information and improving radar target detection performance. Methods This study employs a modified Burg method to predict sea clutter, incorporating a stability factor in the derivation of reflection coefficients to constrain the model’s poles within the unit circle. This enhances model stability, improving its adaptability to the time-varying nature of sea clutter and increasing the accuracy of feature prediction. A dynamic weighting algorithm is introduced to adaptively adjust fusion weights based on data volatility around the current frame by solving a multivariable extremum problem, thereby minimizing the local variance of fused features. Temporal fusion is performed using the features Relative Average Amplitude (RAA), Frequency Peak to Average Ratio (FPAR), and Relative Doppler Peak Height (RDPH) to generate a fused feature. The fused clutter features are then used to construct a three-dimensional convex hull decision region, where target presence is determined by assessing whether the detection unit’s feature point lies within this region. Detection results are compared with commonly used feature detection methods. Additionally, the study evaluates the boundary performance of the proposed method and contrasts it with the traditional energy-domain CFAR method, providing a comprehensive analysis of its usability and effectiveness. Results and Discussions The proposed method achieves the following results: (1) For clutter data, the temporal fusion algorithm reduces data variance by an average of 0.024 5 compared to no temporal fusion and by 0.003 5 compared to the original temporal fusion algorithm. For target data, it reduces data variance by an average of 1.126 6 compared to no temporal fusion and by 0.179 compared to the original temporal fusion algorithm. (2) The Bhattacharyya distance of the proposed temporal fusion algorithm improves by an average of 0.237 3 compared to no temporal fusion and by 0.109 3 compared to the original temporal fusion algorithm. Under VV polarization, the Bhattacharyya distance improves by an average of 0.219 9 compared to no temporal fusion and by 0.090 8 compared to the original temporal fusion algorithm. (3) The proposed method outperforms other feature detectors in detection performance by effectively utilizing temporal information from historical frames, thereby enhancing the echo information used. Compared to energy-domain CFAR methods, it maintains a strong competitive advantage. Conclusions This study presents innovative solutions to two key challenges in existing sea clutter feature modeling and fusion methods. First, to address the time-varying nature of sea clutter features, a model-stable modified Burg method is proposed for Autoregressive (AR) feature modeling. This approach enables adaptive adjustment of model pole distribution, improving the accuracy of one-step sea clutter feature predictions and simplifying model order estimation. Second, to enhance the utilization of inter-frame temporal information during feature fusion, a dynamic weighted fusion algorithm is introduced to integrate predicted and observed features. This method reduces the variance of fused features and fully exploits historical temporal information. Validation using the IPIX dataset and the shared dataset from the Naval Aeronautical University demonstrates that the fused features obtained through these methods exhibit improved separability compared to the original features, significantly enhancing detector performance.
- Small target detection /
- Sea clutter /
- Temporal feature information /
- Modified burg /
- Dynamic weighting

HTML全文

圖 1 航道浮標(biāo)圖

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圖 2 不同海況時(shí)域回波圖

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圖 3 加入穩(wěn)定因子后AR模型極點(diǎn)變化

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圖 4 特征頻譜對(duì)比

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圖 5 特征相對(duì)預(yù)測(cè)誤差隨穩(wěn)定參數(shù)變化曲線

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圖 6 修正Burg方法的AR預(yù)測(cè)曲線與觀測(cè)曲線對(duì)比

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圖 7 動(dòng)態(tài)加權(quán)算法檢測(cè)器流程

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圖 8 不同滑窗寬度對(duì)應(yīng)的數(shù)據(jù)方差曲線

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圖 9 3種算法計(jì)算后數(shù)據(jù)方差對(duì)比

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圖 10 凸包檢測(cè)器圖示

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圖 11 不同極化3種算法計(jì)算后特征巴氏距離對(duì)比

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圖 12 融合特征后平均檢測(cè)概率隨AR階數(shù)增加的變化曲線

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圖 13 不同極化本文所提方法與其他特征組合的檢測(cè)效果對(duì)比

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圖 14 不同海況背景下本文所提方法與其他檢測(cè)方法的邊界性能對(duì)比

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表 1 IPIX數(shù)據(jù)集信息

文件序號(hào)	采樣數(shù)	浪高		風(fēng)力		目標(biāo)影響單元	目標(biāo)所在單元	總距離
文件序號(hào)	采樣數(shù)	最大(m)	一般(m)	風(fēng)向(°)	風(fēng)速(m/s)	目標(biāo)影響單元	目標(biāo)所在單元	單元個(gè)數(shù)
17	131 072	3.1	2.1	301	10	8:11	9	14
26	131 072	1.56	1.03	211	9	6:9	7	14
30	131 072	1.25	0.89	210	19	6:8	7	14
31	131 072	1.28	0.89	206	15	6:9	7	14
54	131 072	0.97	0.66	308	20	7:10	8	14
280	131 072	2.4	1.44	216	11	7:10	8	14
310	131 072	1.38	0.9	313	33	6:9	7	14
311	131 072	1.38	0.9	310	33	6:9	7	14
320	131 072	1.34	0.91	317	27	6:9	7	14

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表 2 LSTM訓(xùn)練參數(shù)設(shè)置

預(yù)設(shè)參數(shù)	設(shè)置值
最大訓(xùn)練次數(shù)	150
梯度閾值	1
使用歷史幀數(shù)	20
初始學(xué)習(xí)率	0.01
調(diào)整學(xué)習(xí)率節(jié)點(diǎn)	60次以后
學(xué)習(xí)率調(diào)整因子	0.2

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表 3 不同預(yù)測(cè)方法下雜波特征的平均相對(duì)預(yù)測(cè)誤差

AR預(yù)測(cè)方法	不同特征的平均相對(duì)預(yù)測(cè)誤差
AR預(yù)測(cè)方法	RAA	FPAR	RDPH
本文所提方法	0.128 3	0.204 7	0.319 4
未修正Burg方法	0.153 0	0.225 6	0.345 0
文獻(xiàn)[10]方法	0.198 3	0.260 3	0.338 4
LSTM	0.263 2	0.198 5	0.296 0

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表 4 不同預(yù)測(cè)方法下目標(biāo)特征的平均相對(duì)預(yù)測(cè)誤差

AR預(yù)測(cè)方法	不同特征的平均相對(duì)預(yù)測(cè)誤差
AR預(yù)測(cè)方法	RAA	FPAR	RDPH
本文方法	0.112 6	0.183 1	0.285 8
未修正Burg方法	0.126 9	0.199 9	0.315 8
文獻(xiàn)[10]方法	0.198 3	0.260 3	0.338 4
LSTM	0.152 7	0.183 2	0.310 7

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表 5 IPIX數(shù)據(jù)檢測(cè)結(jié)果

檢測(cè)器	使用脈沖數(shù)	不同極化方式下的平均檢測(cè)概率				平均檢測(cè)概率
檢測(cè)器	使用脈沖數(shù)	HH	HV	VH	VV	平均檢測(cè)概率
原3特征檢測(cè)器	64	0.245 9	0.349 1	0.339 3	0.177 2	0.277 9
	128	0.437 2	0.574 3	0.569 9	0.368 7	0.487 5
	256	0.569 4	0.699 6	0.696 7	0.496 2	0.615 5
文獻(xiàn)[10]檢測(cè)器	64	0.348 1	0.472 8	0.456 1	0.276 4	0.388 4
	128	0.629 2	0.743 9	0.735 4	0.535 6	0.661 0
	256	0.709 9	0.813 9	0.809 3	0.642 0	0.743 8
本文檢測(cè)器	64	0.417 7	0.543 2	0.537 3	0.347 4	0.461 4
	128	0.692 1	0.794 1	0.792 6	0.630 0	0.727 2
	256	0.776 3	0.856 3	0.854 4	0.732 1	0.804 8

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表 6 海軍航空大學(xué)共享數(shù)據(jù)集檢測(cè)結(jié)果

檢測(cè)器	使用脈沖數(shù)	檢測(cè)概率				平均檢測(cè)概率
檢測(cè)器	使用脈沖數(shù)	4級(jí)HH	4級(jí)VV	5級(jí)HH	5級(jí)VV	平均檢測(cè)概率
原3特征檢測(cè)器	64	0.305 5	0.682 0	0.218 2	0.155 8	0.340 4
	128	0.456 7	0.808 4	0.453 1	0.334 8	0.513 2
	256	0.632 7	0.907 1	0.647 5	0.459 4	0.661 7
文獻(xiàn)[10]檢測(cè)器	64	0.400 7	0.718 6	0.334 7	0.208 9	0.415 7
	128	0.592 3	0.886 5	0.644 5	0.443 3	0.641 7
	256	0.753 3	0.959 1	0.820 8	0.534 6	0.767 0
本文檢測(cè)器	64	0.442 4	0.778 5	0.434 1	0.255 6	0.477 7
	128	0.647 1	0.922 5	0.727 6	0.497 5	0.698 7
	256	0.803 1	0.976 6	0.866 4	0.597 9	0.811 0

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參考文獻(xiàn)(17)

[1]	關(guān)鍵. 雷達(dá)海上目標(biāo)特性綜述[J]. 雷達(dá)學(xué)報(bào), 2020, 9(4): 674–683. doi: 10.12000/JR20114. GUAN Jian. Summary of marine radar target characteristics[J]. Journal of Radars, 2020, 9(4): 674–683. doi: 10.12000/JR20114.
[2]	BI Xiaowen, GUO Shenglong, YANG Yunxiu, et al. Adaptive target extraction method in sea clutter based on fractional Fourier filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5115609. doi: 10.1109/TGRS.2022.3192893.
[3]	SHI Sainan and SHUI Penglang. Sea-surface floating small target detection by one-class classifier in time-frequency feature space[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6395–6411. doi: 10.1109/ TGRS.2018.2838260. doi: 10.1109/TGRS.2018.2838260.
[4]	XU Shuwen, ZHENG Jibin, PU Jia, et al. Sea-surface floating small target detection based on polarization features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(10): 1505–1509. doi: 10.1109/LGRS.2018.2852560.
[5]	陳世超, 高鶴婷, 羅豐. 基于極化聯(lián)合特征的海面目標(biāo)檢測(cè)方法[J]. 雷達(dá)學(xué)報(bào), 2020, 9(4): 664–673. doi: 10.12000/ JR20072. doi: 10.12000/JR20072. CHEN Shichao, GAO Heting, and LUO Feng. Target detection in sea clutter based on combined characteristics of polarization[J]. Journal of Radars, 2020, 9(4): 664–673. doi: 10.12000/JR20072.
[6]	LO T, LEUNG H, LITVA J, et al. Fractal characterisation of sea-scattered signals and detection of sea-surface targets[J]. IEE Proceedings F: Radar and Signal Processing, 1993, 140(4): 243–250. doi: 10.1049/ip-f-2.1993.0034.
[7]	FAN Yifei, TAO Mingliang, and SU Jia. Multifractal correlation analysis of autoregressive spectrum-based feature learning for target detection within sea clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5108811. doi: 10.1109/TGRS.2021.3137466.
[8]	關(guān)鍵, 伍僖杰, 丁昊, 等. 基于對(duì)角積分雙譜的海面慢速小目標(biāo)檢測(cè)方法[J]. 電子與信息學(xué)報(bào), 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408. GUAN Jian, WU Xijie, DING Hao, et al. A method for detecting small slow targets in sea surface based on diagonal integrated bispectrum[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408.
[9]	關(guān)鍵, 姜星宇, 劉寧波, 等. 海雜波背景下的雙極化最大特征值目標(biāo)檢測(cè)[J]. 系統(tǒng)工程與電子技術(shù), 2024, 46(11): 3715–3725. doi: 10.12305/j.issn.1001-506X.2024.11.13. GUAN Jian, JIANG Xingyu, LIU Ningbo, et al. Target detection using dual-polarization maximum eigenvalue in sea clutter background[J]. Systems Engineering and Electronics, 2024, 46(11): 3715–3725. doi: 10.12305/j.issn.1001-506X.2024.11.13.
[10]	董云龍, 張兆祥, 丁昊, 等. 基于三特征預(yù)測(cè)的海雜波中小目標(biāo)檢測(cè)方法[J]. 雷達(dá)學(xué)報(bào), 2023, 12(4): 762–775. doi: 10.12000/JR23037. DONG Yunlong, ZHANG Zhaoxiang, DING Hao, et al. Target detection in sea clutter using a three-feature prediction-based method[J]. Journal of Radars, 2023, 12(4): 762–775. doi: 10.12000/JR23037.
[11]	SHUI Penglang, LI Dongchen, and XU Shuwen. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416–1430. doi: 10.1109/TAES.2014.120657.
[12]	IPIX Radar. The IPIX radar database[EB/OL]. http://soma.ece.mcmaster.ca/ipix/, 2021.
[13]	關(guān)鍵, 劉寧波, 王國慶, 等. 雷達(dá)對(duì)海探測(cè)試驗(yàn)與目標(biāo)特性數(shù)據(jù)獲取——海上目標(biāo)雙極化多海況散射特性數(shù)據(jù)集[J]. 雷達(dá)學(xué)報(bào), 2023, 12(2): 456–469. doi: 10.12000/JR23029. GUAN Jian, LIU Ningbo, WANG Guoqing, et al. Sea-detecting radar experiment and target feature data acquisition for dual polarization multistate scattering dataset of marine targets[J]. Journal of Radars, 2023, 12(2): 456–469. doi: 10.12000/JR23029.
[14]	BARBARESCO F. Algorithme de burg regularise fsds (fonctionnelle stabilisatrice de douceur spectrale) Comparaison avec l'algorithme de burg mfe (Minimum free energy)[C]. Quinzieme Colloque Gretsi - Juan-Les-Pins, 1995: 29–32.
[15]	LI Yuzhou, XIE Pengcheng, TANG Zeshen, et al. SVM-based sea-surface small target detection: A false-alarm-rate-controllable approach[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(8): 1225–1229. doi: 10.1109/LGRS.2019.2894385.
[16]	王鑫, 吳際, 劉超, 等. 基于LSTM循環(huán)神經(jīng)網(wǎng)絡(luò)的故障時(shí)間序列預(yù)測(cè)[J]. 北京航空航天大學(xué)學(xué)報(bào), 2018, 44(4): 772–784. doi: 10.13700/j.bh.1001-5965.2017.0285. WANG Xin, WU Ji, LIU Chao, et al. Exploring LSTM based recurrent neural network for failure time series prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772–784. doi: 10.13700/j.bh.1001-5965.2017.0285.
[17]	胡學(xué)駿, 羅中良. 基于統(tǒng)計(jì)理論的多傳感器信息融合方法[J]. 傳感器技術(shù), 2002(8): 38–39,43. doi: 10.13873/j.1000-97872002.08.013. HU Xuejun and LUO Zhongliang. Method of multi-sensor information fusion based on statistics theory[J]. Transducer and Microsystem Technologies, 2002(8): 38–39,43. doi: 10.13873/j.1000-97872002.08.013.