生成式中断航迹接续关联方法

doi:10.12305/j.issn.1001-506X.2022.05.15

Abstract

Abstract:

Traditional track segment consecutive association (TSCA) methods are based on the hypothesis target motion model and need to use a lot of prior information. It has many disadvantages such as too many parameters, complicated calculation and long reasoning time. In order to solve the above problems, a generative TSCA method based on the attention mechanism is proposed. Firstly, the module of generating the track situation map is designed, and the original track data is converted into the track situation map as the input of generative adversarial network. Aiming at the problems of track noise and the difficulty in extracting the features of track motion and track interruption, based on the generative adversarial network and the attention mechanism, the track association network is designed to filter out the noise of tracks and accomplish TSCA. The simulation results show that the proposed method is effective and exceeds the existing algorithms in both precision and speed.

Key words: track segment consecutive association (TSCA), generative adversarial network, attention mechanism, track situation map

CLC Number:

Pingliang XU, Yaqi CUI, Wei XIONG, Zhenyu XIONG, Xiangqi GU. Generative track segment consecutive association method[J]. Systems Engineering and Electronics, 2022, 44(5): 1543-1552.

Figures/Tables 18

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Table 2

Fig.9

Table 3

Fig.10

Fig.11

Fig.12

Fig.13

Table 4

Fig.14

References 33

1	YEOM S W, KIRUBARAJAN T, BAR S Y. Improving track continuity using track segment association[C]//Proc. of the IEEE Aerospace Conference, 2003.
2	SUN J P, WANG N Y, ZHANG Z G. Track segment association of maneuvering target based on expectation maximization[C]//Proc. of the 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, 2018.
3	ZHANG S , BAR S Y . Track segment association for GMTI tracks of evasive move-stop-move maneuvering targets[J]. IEEE Trans.on Aerospace and Electronic Systems, 2011, 47 (3): 1899- 1914. doi: 10.1109/TAES.2011.5937272
4	DEMPSTER A P , LAIRD N M , RUBIN D B . Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1977, 39 (1): 1- 22. doi: 10.1111/j.2517-6161.1977.tb01600.x
5	ZHU H Y , HAN S Y . Track-to-track association based on structural similarity in the presence of sensor biases[J]. Journal of Applied Mathematics, 2014, 294657.
6	杜渐, 夏学知. 面向航迹中断的模糊航迹关联算法[J]. 火力与指挥控制, 2013, 38 (6): 68- 71.
	DU J , XIA X Z . A fuzzy track association algorithm in track interrupt-oriented[J]. Fire Control & Command Control, 2013, 38 (6): 68- 71.
7	刘颢, 陈世友, 汪学东, 等. 一种自适应航迹关联算法[J]. 电子学报, 2013, 41 (12): 2416- 2421. doi: 10.3969/j.issn.0372-2112.2013.12.015
	LIU H , CHEN S Y , WANG X D , et al. An adaptive track correlation algorithm[J]. Chinese Journal of Electronics, 2013, 41 (12): 2416- 2421. doi: 10.3969/j.issn.0372-2112.2013.12.015
8	齐林, 王海鹏, 熊伟, 等. 基于先验信息的多假设模型中断航迹关联算法[J]. 系统工程与电子技术, 2015, 37 (4): 732- 739.
	QI L , WANG H P , XIONG W , et al. Track segment association algorithm based on multiple-hypothesis models with priori information[J]. Journal of Systems Engineering and Electronics, 2015, 37 (4): 732- 739.
9	DONG H, YU S M, WU C, et al. Semantic image synthesis via adversarial learning[C]//Proc. of the IEEE International Conference on Computer Vision, 2017: 5706-5714.
10	KANEKO T, HIRAMATSU K, KASHINO K. Generative attribute controller with conditional filtered generative adversarial networks[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
11	KARACAN L, AKATA Z, ERDEM A, et al. Learning to generate images of outdoor scenes from attributes and semantic layouts[EB/OL]. [2021-01-25]. https://arXiv.org/pdf/1612.00215.pdf.
12	LEDIG C, THEIS L, HUSZAR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4681-4690.
13	PATHAK D, KRAHENBUHL P, DONAHUE J, et al. Context encoders: feature learning by inpainting[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2536-2544.
14	SANGKLOY P, LU J W, FANG C, et al. Scribbler: controlling deep image synthesis with sketch and color[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 5400-5409.
15	WANG X L, GUPTA A. Generative image modeling using style and structure adversarial networks[C]//Proc. of the European Conference on Computer Vision, 2016: 318-335.
16	ZHANG Z F, SONG Y, QI H R. Age progression/regression by conditional adversarial autoencoder[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 5810-5818.
17	GOODFELLOW I , POUGET A J , MIRZA M , et al. Generative adversarial nets[J]. Advances in Neural Information Processing Systems, 2014, 27, 2672- 2680.
18	MNIH V , HEESS N , GRAVES A . Recurrent models of visual attention[J]. Advances in Neural Information Processing Systems, 2014, 2, 2204- 2212.
19	WANG K F , GOU C , DUAN Y J , et al. Generative adversa-rial networks: the state of the art and beyond[J]. Acta Automatica Sinica, 2017, 43 (3): 321- 332.
20	VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proc. of the 25th International Conference on Machine Learning, 2008: 1096-1103.
21	ULYANOV D, VEDALDI A, LEMPITSKY V. Instance normalization: the missing ingredient for fast stylization[EB/OL]. [2021-01-25]. https//arXiv.org/abs/1607.08022v1.
22	GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C]//Proc. of the 14th International Confe-rence on Artificial Intelligence and Statistics, 2011: 315-323.
23	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
24	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2021-01-25]. https//arXiv.org/abs/1409.1556.
25	DUMOULIN V, VISIN F. A guide to convolution arithmetic for deep learning[EB/OL]. [2021-01-25]. https//arXiv.org/abs/1603.07285.
26	PENG X B, KANAZAWA A, TOYER S, et al. Variational discriminator bottleneck: improving imitation learning, inverserl, and gans by constraining information flow[EB/OL]. [2021-01-25]. https//arXiv.org/abs/1810.00821v4.
27	RATLIFF L J, BURDEN S A, SASTRY S S. Characterization and computation of local Nash equilibria in continuous games[C]//Proc. of the IEEE 51st Annual Allerton Conference on Communication, Control, and Computing, 2013: 917-924.
28	ZHAO H , GALLO O , FROSIO I , et al. Loss functions for image restoration with neural networks[J]. IEEE Trans.on Computational Imaging, 2016, 3 (1): 47- 57.
29	LI X R , JILKOV V P . Survey of maneuvering target tracking. Part I. dynamic models[J]. IEEE Trans.on Aerospace and Electronic Systems, 2003, 39 (4): 1333- 1364. doi: 10.1109/TAES.2003.1261132
30	WANG Z , BOVIK A C , SHEIKH H R , et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans.on Image Processing, 2004, 13 (4): 600- 612. doi: 10.1109/TIP.2003.819861
31	PASZKE A, GROSS S, MASSA F, et al. Pytorch: an imperative style, high-performance deep learning library[EB/OL]. [2021-01-25]. https//arXiv.org/abs/1912.01703v1.
32	YEOM S W , KIRUBARAJAN T , BAR S Y . Track segment association, fine-step IMM and initialization with Doppler for improved track performance[J]. IEEE Trans.on Aerospace and Electronic Systems, 2004, 40 (1): 293- 309. doi: 10.1109/TAES.2004.1292161
33	RAGHU J , SRIHARI P , THARMARASA R , et al. Comprehensive track segment association for improved track continuity[J]. IEEE Trans.on Aerospace and Electronic Systems, 2018, 54 (5): 2463- 2480. doi: 10.1109/TAES.2018.2820364

输出通道	AP	P@5	P@10	P@20	SSIM
8	0.40	0.80	0.60	0.20	0.863 7
16	0.63	1.00	0.70	0.50	0.891 4
32	0.68	1.00	0.80	0.55	0.893 9
64	0.71	1.00	0.90	0.55	0.904 3
128	0.00	0.00	0.00	0.00	0.414 5
256	0.00	0.00	0.00	0.00	0.365 8

主干层	AP	P@5	P@10	P@20	SSIM
深度卷积网络	0.46	0.6	0.5	0.4	0.893 5
残差网络	0.71	1.00	0.9	0.55	0.904 3

指标	算法
指标	传统TSA	多假设TSA	multi-frame S-D TSA	本文方法
AP/%	42.8	94.0	97.5	100.0
t/s	0.35	0.38	1.25	0.12

噪声等级	AP	P@5	P@10	P@20	SSIM
2	0.71	1.00	0.9	0.55	0.904 3
4	0.69	1.00	0.8	0.45	0.873 6
6	0.58	1.00	0.7	0.4	0.834 2

[1]	Xiao HAN, Shiwen CHEN, Meng CHEN, Jincheng YANG. Open-set recognition of LPI radar signal based on reciprocal point learning [J]. Systems Engineering and Electronics, 2022, 44(9): 2752-2759.
[2]	Rongxiang LIU, Lin WU, Zhige XIE, Honglin LIU. Auxiliary situation analysis for air defense system based on generative adversarial network [J]. Systems Engineering and Electronics, 2022, 44(8): 2522-2529.
[3]	Bowei QIN, Lei JIANG, Hua XU, Zisen QI. Modulation recognition algorithm based on residual generation adversarial network [J]. Systems Engineering and Electronics, 2022, 44(6): 2019-2026.
[4]	Kai SHAO, Miaomiao ZHU, Guangyu WANG. Modulation recognition method based on generative adversarial andconvolutional neural network [J]. Systems Engineering and Electronics, 2022, 44(3): 1036-1043.
[5]	Tao WU, Lunwen WANG, Jingcheng ZHU. Camouflage image segmentation based on transfer learning and attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(2): 376-384.
[6]	Tao JIN, Xiaofeng WANG, Runlan TIAN, Xindong ZHANG. Rapid recognition method of radar emitter based on improved 1DCNN+TCN [J]. Systems Engineering and Electronics, 2022, 44(2): 463-469.
[7]	Yutang MA, Peng SUN, Jieyong ZHANG, Peng WANG, Yunfei YAN, Liang ZHAO. Air group intention recognition method under imbalance samples [J]. Systems Engineering and Electronics, 2022, 44(12): 3747-3755.
[8]	Yiqiang TANG, Xiaopeng YANG, Shengming ZHU. Low-orbit satellite channel prediction algorithm based on the hybrid CNN-BiLSTM using attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(12): 3863-3870.
[9]	Lingzhi QU, Junan YANG, Hui LIU, Keju HUANG. Method for individual identification of communication radiation source embedded in attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(1): 20-27.
[10]	Ziyan LIU, Shanshan MA, Jing LIANG, Mingcheng ZHU, Lei YUAN. Attention mechanism based CNN channel estimation algorithm in millimeter-wave massive MIMO system [J]. Systems Engineering and Electronics, 2022, 44(1): 307-312.
[11]	Pengyu CAO, Chengzhi YANG, Limeng SHI, Hongchao WU. LPI radar signal enhancement based on DAE-GAN network [J]. Systems Engineering and Electronics, 2021, 43(9): 2493-2500.
[12]	Bangyan CUI, Runlan TIAN, Dongfeng WANG, Gang CUI, Jingyuan SHI. Radar emitter identification based on attention mechanism and improved CLDNN [J]. Systems Engineering and Electronics, 2021, 43(5): 1224-1231.
[13]	Fan ZHAO, Hu JIN. Communication jamming waveform generation technology based on GAN [J]. Systems Engineering and Electronics, 2021, 43(4): 1080-1088.
[14]	Shiyang GAO, Huixu DONG, Runlan TIAN, Xindong ZHANG. Radar emitter signal recognition method based on SRNN+Attention+CNN [J]. Systems Engineering and Electronics, 2021, 43(12): 3502-3509.
[15]	Ruochen ZHAO, Jingdong WANG, Siyu LIN, Dongze GU. Small building detection algorithm based on convolutional neural network [J]. Systems Engineering and Electronics, 2021, 43(11): 3098-3106.

Generative track segment consecutive association method

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 18

References 33

Related Articles 15

Recommended Articles

Metrics

Comments