{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T16:11:38Z","timestamp":1778861498088,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology on Near-Surface Detection Laboratory","award":["TCGZ2018A001"],"award-info":[{"award-number":["TCGZ2018A001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The classification and recognition of radar clutter is helpful to improve the efficiency of radar signal processing and target detection. In order to realize the effective classification of uniform circular array (UCA) radar clutter data, a classification method of ground clutter data based on the chaotic genetic algorithm is proposed. In this paper, the characteristics of UCA radar ground clutter data are studied, and then the statistical characteristic factors of correlation, non-stationery and range-Doppler maps are extracted, which can be used to classify ground clutter data. Based on the clustering analysis, results of characteristic factors of radar clutter data under different wave-controlled modes in multiple scenarios, we can see: in radar clutter clustering of different scenes, the chaotic genetic algorithm can save 34.61% of clustering time and improve the classification accuracy by 42.82% compared with the standard genetic algorithm. In radar clutter clustering of different wave-controlled modes, the timeliness and accuracy of the chaotic genetic algorithm are improved by 42.69% and 20.79%, respectively, compared to standard genetic algorithm clustering. The clustering experiment results show that the chaotic genetic algorithm can effectively classify UCA radar\u2019s ground clutter data.<\/jats:p>","DOI":"10.3390\/s21134596","type":"journal-article","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T22:02:04Z","timestamp":1625522524000},"page":"4596","update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on Chaotic Genetic Algorithm"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0003-3763-3664","authenticated-orcid":false,"given":"Bin","family":"Yang","sequence":"first","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mo","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-6288-8541","authenticated-orcid":false,"given":"Yao","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-9038-0278","authenticated-orcid":false,"given":"Changyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjiao","family":"Rong","sequence":"additional","affiliation":[{"name":"Science and Technology on Near Surface Detection Laboratory, Wuxi 214035, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huihui","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0002-3576-2430","authenticated-orcid":false,"given":"Tao","family":"Duan","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5481","DOI":"10.1109\/TVT.2018.2817230","article-title":"A 2-D FFT-based transceiver architecture for OAM-OFDM systems with UCA antennas","volume":"67","author":"Chen","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xie, Y., Huang, M., Zhang, Y., Duan, T., and Wang, C. (2021). Two-Stage Fast DOA Estimation Based on Directional Antennas in Conformal Uniform Circular Array. Sensors, 21.","DOI":"10.3390\/s21010276"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jiang, W., Wu, X., Wang, Y., Chen, B., Feng, W., and Jin, Y. (2021). Time\u2013Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems. Sensors, 21.","DOI":"10.3390\/s21010231"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3271","DOI":"10.1109\/TGRS.2018.2882912","article-title":"Adaptive ground clutter reduction in ground-penetrating radar data based on principal component analysis","volume":"57","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1049\/iet-rsn:20060109","article-title":"Measurements of the Doppler spectra of breaking waves","volume":"1","author":"Waseda","year":"2007","journal-title":"IET Radar Sonar Navig."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1049\/iet-rsn.2009.0072","article-title":"Electromagnetic scattering from wind blown waves and ripples modulated by longer waves under laboratory conditions","volume":"4","author":"Mitomi","year":"2010","journal-title":"IET Radar Sonar Navig."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Watts, S. (2013, January 9\u201312). The effects of covariance matrix mismatch on adaptive CFAR performance. Proceedings of the 2013 International Conference on Radar, Adelaide, SA, Australia.","DOI":"10.1109\/RADAR.2013.6652007"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1109\/JPROC.2002.1015011","article-title":"Uncovering nonlinear dynamics-the case study of sea clutter","volume":"90","author":"Haykin","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Kay, S., Cogun, F., and Raghavan, R.S. (2016). On detection of non-stationarity in radar signal processing. IEEE Radar Conf., 1\u20134.","DOI":"10.1109\/RADAR.2016.7485083"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"147094","DOI":"10.1109\/ACCESS.2019.2946465","article-title":"Clutter Suppression Approach for End-Fire Array Airborne Radar Based on Adaptive Segmentation","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/5.362751","article-title":"Detection of signals in chaos","volume":"83","author":"Haykin","year":"1995","journal-title":"Proc. IEEE."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1109\/LGRS.2013.2237750","article-title":"The fractal properties of sea clutter and their applications in maritime target detection","volume":"10","author":"Luo","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1049\/iet-rsn.2014.0473","article-title":"Fractal properties of autoregressive spectrum and its application on weak target detection in sea clutter background","volume":"9","author":"Fan","year":"2015","journal-title":"IET Radar Sonar Navig."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ai, J., Yang, X., Dong, Z., Zhou, F., Jia, L., and Hou, L. (2017). A new two parameter CFAR ship detector in Log-Normal clutter. IEEE Radar Conf. Radar Conf., 0195\u20130199.","DOI":"10.1109\/RADAR.2017.7944196"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1109\/TIM.2010.2047579","article-title":"Coherent detection of Swerling 0 targets in sea-ice Weibull-distributed clutter using neural networks","volume":"59","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rosenberg, L., and Bocquet, S. (2013). The Pareto distribution for high grazing angle sea-clutter. IEEE Int. Geosci. Remote Sens. Symp. IGARSS, 4209\u20134212.","DOI":"10.1109\/IGARSS.2013.6723762"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1109\/TAES.2017.2705498","article-title":"Noncoherent radar detection in correlated Pareto distributed clutter","volume":"53","author":"Weinberg","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TAES.2004.1337463","article-title":"Statistical analysis of real clutter at different range resolutions","volume":"40","author":"Conte","year":"2004","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1049\/iet-rsn.2009.0096","article-title":"Analysis of the KK-distribution with medium grazing angle sea-clutter","volume":"4","author":"Rosenberg","year":"2010","journal-title":"IET Radar Sonar Navig."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chung, Y.J., Chen, Y.R., Chuang, L.Z., Yang, Y.J., and Leu, L.G. (2017, January 19\u201322). The correlation analysis of ionospheric clutter and noise using SeaSonde HF radar. Proceedings of the OCEANS 2017 Aberdeen, Aberdeen, UK.","DOI":"10.1109\/OCEANSE.2017.8084968"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lu, X., Azevedo Coste, C., Nierat, M.-C., Renaux, S., Similowski, T., and Guiraud, D. (2021). Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration. Sensors, 21.","DOI":"10.3390\/s21010099"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.comcom.2019.08.016","article-title":"High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network","volume":"147","author":"Ningbo","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cabanes, Y., Barbaresco, F., Arnaudon, M., and Bigot, J. (2019, January 26\u201328). Non-supervised Machine Learning Algorithms for Radar Clutter High-Resolution Doppler Segmentation and Pathological Clutter Analysis. Proceedings of the 2019 20th International Radar Symposium (IRS), Ulm, Germany.","DOI":"10.23919\/IRS.2019.8768140"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nagel, D., and Smith, S. (2012, January 4\u20136). Creating a likelihood vector for ground moving targets in the exo-clutter region of airborne radar signals. Proceedings of the 2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany.","DOI":"10.1109\/SDF.2012.6327907"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, Z., and Huang, J. (2021). X-Band Radar Cross-Section of Tandem Helicopter Based on Dynamic Analysis Approach. Sensors, 21.","DOI":"10.3390\/s21010271"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3824","DOI":"10.1109\/ACCESS.2017.2783878","article-title":"Non-coherent radar detection probability for correlated gamma fluctuating targets in k distributed clutter","volume":"6","author":"Yang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1109\/JSTARS.2020.2981046","article-title":"Adaptive clutter suppression and detection algorithm for radar maneuvering target with high-order motions via sparse fractional ambiguity function","volume":"13","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.21629\/JSEE.2016.06.01","article-title":"Novel polarimetric detector for target detection in heterogeneous clutter","volume":"27","author":"Cheng","year":"2016","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Haykin, S. (1983). Classification of Radar Clutter using the Maximum-Entropy Method. Nonlinear Stochastic Problems, Springer.","DOI":"10.1007\/978-94-009-7142-4_4"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Darzikolaei, M.A., Ebrahimzade, A., and Gholami, E. (2015, January 5\u20136). Classification of radar clutters with artificial neural network. Proceedings of the 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran.","DOI":"10.1109\/KBEI.2015.7436109"},{"key":"ref_31","first-page":"589","article-title":"Intelligent suppression method for ionospheric clutter based on clutter cluster and greedy strategy","volume":"9","author":"Wei","year":"2020","journal-title":"J. Radars China"},{"key":"ref_32","first-page":"119124","article-title":"Hierarchical Fuzzy-clustering Classification of Overvoltages in Power Systems Based on the Genetic Algorithm","volume":"30","author":"Du","year":"2010","journal-title":"Proc. CSEE"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gonzalez, S., Stegall, P., Edwards, H., Stirling, L., and Siu, H.C. (2021). Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running. Sensors, 21.","DOI":"10.3390\/s21010194"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zarra, T., Galang, M.G.K., Ballesteros, F.C., Belgiorno, V., and Naddeo, V. (2021). Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques. Sensors, 21.","DOI":"10.3390\/s21010114"},{"key":"ref_35","first-page":"22","article-title":"A Method of Semi-supervised Classification for Hyperspectral Images Based on Spatial Information and Genetic Optimization","volume":"10","author":"Dongcui","year":"2015","journal-title":"Bull. Surv. Mapp."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fu, Z., Wei, C., and Yang, Y. (2010). Force identification by using SVM and CPSO technique. International Conference in Swarm Intelligence, Springer.","DOI":"10.1007\/978-3-642-13498-2_19"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chai, S., Liu, X., Wu, X., and Xiong, Y. (2021). Separation of the Sound Power Spectrum of Multiple Sources by Three-Dimensional Sound Intensity Decomposition. Sensors, 21.","DOI":"10.3390\/s21010279"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1002\/cyto.a.20896","article-title":"Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander\u2019s overlap coefficient","volume":"77","author":"Adler","year":"2010","journal-title":"Cytom. Part A"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Feng, C., Cheng, J., and Zhou, L. (2013, January 23\u201325). Analysis of real sea clutter based on meta recurrence plot. Proceedings of the 2013 Ninth International Conference on Natural Computation (ICNC), Shenyang, China.","DOI":"10.1109\/ICNC.2013.6818143"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/21\/13\/4596\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:26:06Z","timestamp":1760163966000},"score":1,"resource":{"primary":{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/21\/13\/4596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":39,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134596"],"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/s21134596","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,5]]}}}