A general evaluation system for optimal selection performance of radar clutter model

doi:10.23919/JSEE.2022.000122

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (6): 1520-1525.doi: 10.23919/JSEE.2022.000122

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

A general evaluation system for optimal selection performance of radar clutter model

Wei YANG(), Liang ZHANG(), Liru YANG(), Wenpeng ZHANG(), Qingmu SHEN

¹ College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Received:2022-03-14 Accepted:2022-10-13 Online:2023-12-18 Published:2023-12-29
Contact: Liang ZHANG E-mail:yw850716@sina.com;2564397757@qq.com;yangliru18@nudt.edu.cn;zhangwenpeng@hotmail.com
About author:
YANG Wei was born in 1985. He received his Ph.D. degree from National University of Defense Technology in China. He is an associate professor in National University of Defense Technology. His research interests are cognitive radar target detection and recognition. E-mail: yw850716@sina.com

ZHANG Liang was born in 1988. He received his M.S. degree from mechanical engineering at National University of Defense Technology (NUDT), in 2013. He is now working toward his Ph.D. degree in information and communication engineering at NUDT. His research interests are deep learning and their applications in radar signal processing. E-mail: 2564397757@qq.com

YANG Liru was born in 1984. He received his B.S. and M.S. degrees from Information Engineering University in 2006 and 2010, respectively. He is pursuing his Ph.D. degree at National University of Defense Technology since 2018. His research directions are radar signal processing, machine learning, and transfer learning. E-mail: yangliru18@nudt.edu.cn

ZHANG Wenpeng was born in 1989. He received his Ph.D. degree in information and communication engineering from the National University of Defense Technology (NUDT), Changsha, China, in 2018. He is an assistant professor with the College of Electronic Science, NUDT. His research interests are radar signal processing, time-frequency analysis, and signal representation. E-mail: zhangwenpeng@hotmail.com

SHEN Qinmu was born in 1981. He received his B.S. degree from Zhejiang University, Hangzhou, China, in 2005, and his M.S. and Ph.D. degree from National University of Defense Technology, Changsha, China, in 2008 and 2017. He is currently an asssociate professor at National University of Defense Technology. His research interests include intelligent target recognition, few shot learning, and multi-modal information fusion. E-mail: qinmu2004@sina.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61871384;61921001).

Abstract

Abstract:

The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.

Key words: radar clutter, clutter characterization model, model selection, performance evaluation

Wei YANG, Liang ZHANG, Liru YANG, Wenpeng ZHANG, Qingmu SHEN. A general evaluation system for optimal selection performance of radar clutter model[J]. Journal of Systems Engineering and Electronics, 2023, 34(6): 1520-1525.

Figures/Tables 6

Fig 1

Fig 2

Fig 3

Fig 4

Table 1

Fig 5

References 30

1	STEHWIEN W, HAYKIN S A statistical radar clutter classifier. Proc. of the IEEE National Radar Conference, 1989, 164- 169.
2	HAYKIN S, KESLER S, CURRIE B An experimental classification of radar clutter. Proceedings of the IEEE, 1979, 67 (2): 332- 333. doi: 10.1109/PROC.1979.11246
3	HAYKIN S, DENG C Classification of radar clutter using neural networks. IEEE Trans. on neural networks, 1991, 2 (6): 589- 600. doi: 10.1109/72.97936
4	KERBAA T H, MEZACHE A, GINI F. CNN-LSTM based approach for parameter estimation of K-clutter plus noise. Proc. of the IEEE Radar Conference, 2020. DOI: 10.1109/RadarConf2043947 2020.9266571.
5	JIN Z L, PAN Q, LIANG Y, et al SVM-based land/sea clutter identification with multi-features. Proc. of the 31st Chinese Control Conference, 2012, 3903- 3908.
6	LI Y, ZENG W J, ZHANG N, et al Gabor feature based ionospheric clutter region extraction in range-Doppler map. Proc. of the IEEE Antennas and Propagation Society International Symposium, 2014, 269- 270.
7	SHANG S, ZHANG N, LI Y Extraction of ionospheric clutter in HFSWR. Proc. of the 2nd International Conference on Industrial Mechatronics and Automation, 2010, 296- 299.
8	SILVEIRA R B, HOLT A R An automatic identification of clutter and anomalous propagation in polarization-diversity weather radar data using neural networks. IEEE Trans. on geoscience and remote sensing, 2001, 39 (8): 1777- 1788. doi: 10.1109/36.942556
9	DENG G Y, ZHANG Y H, HE S Y, et al Ultrabroadband RCS reduction design by exploiting characteristic complementary polarization conversion metasurfaces. IEEE Trans. on Antennas and Propagation, 2022, 70 (4): 2904- 2914. doi: 10.1109/TAP.2021.3137228
10	MENG T Y, YANG X F, CHEN K S, et al. Radar backscattering over sea surface oil emulsions: simulation and observation. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60. DOI: 10.1109/TGRS.2021.3073369
11	LIAO T H, KIM S B, HANDWERGER A L, et al High-resolution soil-moisture maps over landslide regions in northern california grassland derived from SAR backscattering coefficients. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 4574- 4560.
12	SAIBUN T A scattering model for inhomogeneous layer with vertical profile: application to soil scattering. Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2017, 1422- 1425.
13	FERNANDEZ J R M, VIDAL J C B Improved shape parameter estimation in K clutter with neural networks and deep learning. International Journal of Interactive Multimedia and Artificial Intelligence, 2016, 3 (7): 96- 103. doi: 10.9781/ijimai.2016.3714
14	OUYANG W, HE Y, JIN Y Simulation of temporal-spatial correlated sea clutter based on statistic model. Journal of System Simulation, 2006, 18 (2): 467- 471.
15	LI Z Y, SHUI P L, ZOU P J Order-adaptive fractional-ORDER moment-based estimation of shape parameter of k-distribution. Proc. of the IEEE 4th International Conference on Signal and Image Processing, 2019, 597- 600.
16	ROSENBERG L Parametric modeling of sea clutter Doppler spectra. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5105409.
17	YOSHIKAWA E, TAKIZAWA N, KIKUCHI H, et al An estimator for weather radar Doppler power spectrum via minimum mean square error. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5100516.
18	LIU S, CAO Y H, YEO T S, et al Adaptive clutter suppression in randomized stepped-frequency radar. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (2): 1317- 1333. doi: 10.1109/TAES.2020.3040530
19	KARIMI V, MOHSENI R, SAMADI S Adaptive OFDM waveform design for cognitive radar in signal-dependent clutter. IEEE Systems Journal, 2020, 14 (3): 3630- 3640. doi: 10.1109/JSYST.2019.2943809
20	AI J Q, LUO Q W, YANG X Z, et al Outliers-robust CFAR detector of gaussian clutter based on the truncated-maximum-likelihood- estimator in SAR imagery. IEEE Trans. on Intelligent Transportation Systems, 2020, 21 (5): 2039- 2049. doi: 10.1109/TITS.2019.2911692
21	CHEN T, YANG P, PENG H Multi-target tracking algorithm based on PHD filter against multi-range-false target jamming. Journal of Systems Engineering and Electronics, 2020, 31 (5): 859- 870. doi: 10.23919/JSEE.2020.000066
22	FAN Y F, TAO M L, SU J, et al Weak target detection based on joint fractal characteristics of autoregressive spectrum in sea clutter background. IEEE Geoscience and Remote Sensing Letters, 2019, 16 (2): 1824- 1828.
23	ISMAIL N N, RASHID N E A, KHAN Z I, et al Measurement, processing and modeling of atropical foliage clutter using forward scatter radar micro-sensor network with VHF and UHF bands. Proc. of the International Conference on Radar, Antenna, Microwave, Electronics and Telecommunications, 2015, 76- 81.
24	MA X Y, FANG X L, ZHANG R H, et al An approach of radar clutter recognition based on higher-order statistics combination. Proc. of the 5th International Conference on Signal Processing, 2000, 3, 1933- 1937.
25	NEINAVAIE M, DERAKHTIAN M, SHEIKHI A, et al. Clutter classification in heterogeneous environments. Proc. of the 23rd Canadian Conference on Electrical and Compute Engineering, 2010. DOI: 10.1109/CCECE.2010.5575261.
26	ZHAN W J, YI J X, WAN X R Recognition and mitigation of micro-Doppler clutter in radar systems via support vector machine. IEEE Sensors Journal, 2020, 20 (2): 918- 930. doi: 10.1109/JSEN.2019.2943152
27	MOSER G, ZERUBIA J, SERPICO S B Dictionary-based stochastic expectation-maximization for SAR amplitude probability density function estimation. IEEE Trans. on Geoscience and Remote Sensing, 2006, 44 (1): 188- 200. doi: 10.1109/TGRS.2005.859349
28	GAO G A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 2011, 8 (3): 557- 561. doi: 10.1109/LGRS.2010.2090492
29	FERRO-FAMIL L, REIGBER A, POTTIER E, et al Scene characterization using sub-aperture polarimetric SAR data analysis. Proc. of the IEEE International Symposium on Geoscience and Remote Sensing, 2002, 1, 417- 419.
30	NIE C X Research on multi-target intelligent detection algorithm based on SAR image of complex scene. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017.

Third-party task	Typical performance index
Target detection	Target detection probability and false alarm probability
Target recognition	Target recognition rate
Target tracking	Root mean square error of target state estimation
Waveform design	Echo signal to clutter ratio, ambiguity function response

[1]	Lin TANG, Leilei SUN, Chonghui GUO, Zhen ZHANG. Adaptive spectral affinity propagation clustering [J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 647-664.
[2]	Xu Cheng, Longfei Shi, Yuliang Chang, Yongzhen Li, and Xuesong Wang. Novel polarimetric detector for target detection in heterogeneous clutter [J]. Journal of Systems Engineering and Electronics, 2016, 27(6): 1135-1141.
[3]	Jun He and Qiang Fu. Posterior probability calculation procedure for recognition rate comparison [J]. Systems Engineering and Electronics, 2016, 27(3): 700-711.
[4]	Hamidreza Hosseinzadeh. Tracking performance of large margin classifier in automatic modulation classification with a software radio environment [J]. Journal of Systems Engineering and Electronics, 2014, 25(5): 735-741.

A general evaluation system for optimal selection performance of radar clutter model

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 6

References 30

Related Articles 4

Recommended Articles

Metrics

Comments