{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:05:46Z","timestamp":1760241946024,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T00:00:00Z","timestamp":1542672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61371172"],"award-info":[{"award-number":["61371172"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.<\/jats:p>","DOI":"10.3390\/sym10110659","type":"journal-article","created":{"date-parts":[[2018,11,23]],"date-time":"2018-11-23T03:41:31Z","timestamp":1542944491000},"page":"659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA"],"prefix":"10.3390","volume":"10","author":[{"given":"Jian","family":"Wan","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6746-7471","authenticated-orcid":false,"given":"Guoqing","family":"Ruan","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fan, X., Li, T., and Su, S. (2017). Intrapulse modulation type recognition for pulse compression radar signal. J. Appl. Remote Sens., 11.","DOI":"10.1117\/1.JRS.11.035018"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TAES.2018.2799758","article-title":"Deep Convolutional Autoencoder for Radar-Based Classification of Similar Aided and Unaided Human Activities","volume":"54","author":"Gurbuz","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Xie, J. (2016, January 10\u201313). Robust intra-pulse modulation recognition via sparse representation. Proceedings of the 2016 CIE International Conference on Radar, Guangzhou, China.","DOI":"10.1109\/RADAR.2016.8059203"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"015006","DOI":"10.1117\/1.JRS.11.015006","article-title":"Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery","volume":"11","author":"Lang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1080\/00207217.2018.1426121","article-title":"Radar Wideband Digital Beamforming Based on Time Delay and Phase Compensation","volume":"105","author":"Fu","year":"2018","journal-title":"Int. J. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7389","DOI":"10.1080\/01431161.2017.1375615","article-title":"Time\u2013frequency analysis for moving ship targets in GEO spaceborne\/airborne bistatic SAR imaging based on a GEO satellite transmitter","volume":"38","author":"Lian","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, Q., Li, L., Xu, Q., Yang, S., Shi, X., and Liu, X. (2017). Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach. Remote Sens., 9.","DOI":"10.3390\/rs9060570"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4154","DOI":"10.1109\/JSEN.2018.2820905","article-title":"Enhanced three-dimensional joint domain localized stap for airborne fda-mimo radar under dense false-target jamming scenario","volume":"18","author":"Wen","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_9","unstructured":"Cai, T., Wang, C., Cui, G., and Wang, W. (September, January 30). Constellation-wavelet transform automatic modulation identifier for M-ary QAM signals. Proceedings of the IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Hong Kong, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/78.258082","article-title":"Matching pursuit with time-frequeney dictionaries","volume":"41","author":"Mallat","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal recovery from random measurements via orthogonal matching pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1109\/TIT.2011.2173241","article-title":"Sparse solution of underdetermined linear equations by stage-wise rthogonal matching pursuit","volume":"58","author":"Donoho","year":"2012","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/JSTSP.2010.2042412","article-title":"Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit","volume":"4","author":"Needell","year":"2010","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.acha.2008.07.002","article-title":"CoSaMP: Iterative signal recovery from incomplete and inaccurate samples","volume":"26","author":"Needell","year":"2009","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"38179","DOI":"10.1109\/ACCESS.2018.2853158","article-title":"An Optimal Condition for the Block Orthogonal Matching Pursuit Algorithm","volume":"6","author":"Wen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/sym9090178","article-title":"A sparse signal reconstruction method based on improved double chains quantum genetic algorithm","volume":"9","author":"Guo","year":"2017","journal-title":"Symmetry"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"405","DOI":"10.12989\/sem.2014.52.2.405","article-title":"A new hybrid meta-heuristic for structural design: Ranked particles optimization","volume":"52","author":"Nasrollahi","year":"2014","journal-title":"Struct. Eng. Mech."},{"key":"ref_18","first-page":"1","article-title":"A new probabilistic particle swarm optimization algorithm for size optimization of spatial truss structures","volume":"12","author":"Kaveh","year":"2014","journal-title":"Int. J. Civ. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","article-title":"Grasshopper optimisation algorithm: Theory and application","volume":"105","author":"Saremi","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1177\/1077546313501535","article-title":"Single-channel bearing vibration signal blind source separation method based on morphological filter and optimal matching pursuit (MP) algorithm","volume":"21","author":"Chen","year":"2015","journal-title":"J. Vib. Control"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, L., and Wang, Y. (2012, January 21\u201325). Seismic signal fast decomposition by multichannel matching pursuit with genetic algorithm. Proceedings of the IEEE International Conference on Signal Processing, Beijing, China.","DOI":"10.1109\/ICoSP.2012.6491836"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.swevo.2016.06.003","article-title":"A quantum-inspired genetic algorithm for solving the antenna positioning problem","volume":"31","author":"Dahi","year":"2016","journal-title":"Swarm Evolut. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1631\/FITEE.1500494","article-title":"A virtual service placement approach based on improved quantum genetic algorithm","volume":"17","author":"Xiong","year":"2016","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.neucom.2015.12.131","article-title":"An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis","volume":"211","author":"Chen","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.cja.2014.12.010","article-title":"Adaptive double chain quantum genetic algorithm for constrained optimization problems","volume":"28","author":"Kong","year":"2015","journal-title":"Chin. J. Aeronaut."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1109\/TAES.2017.2667142","article-title":"Automatic intrapulse modulation classification of advanced LPI radar waveforms","volume":"53","author":"Kishore","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, M., Liu, L., and Diao, M. (2016). LPI Radar Waveform Recognition Based on Time-Frequency Distribution. Sensors, 16.","DOI":"10.3390\/s16101682"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/JSTSP.2018.2797798","article-title":"SV-Means: A Fast SVM-based Level Set Estimator for Phase-Modulated Radar Waveform Classification","volume":"12","author":"Pavy","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, F., Liu, Y., Huo, K., Zhang, S., and Zhang, Z. (2018). Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine. Sensors, 18.","DOI":"10.3390\/s18010173"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans Syst Man Cybern. Part B Cybern."},{"key":"ref_31","unstructured":"Cai, Y.L. (1986). Pattern Recognition, Northwest Telecommunication Engineering College Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11074","DOI":"10.1109\/ACCESS.2017.2716191","article-title":"Convolutional neural networks for automatic cognitive radio waveform recognition","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_33","unstructured":"Dudczyk, J., and Wnuk, M. (2004, January 12\u201314). The utilization of unintentional radiation for identification of the radiation sources. Proceedings of the 34th European Microwave Conference, Amsterdam, Netherlands."},{"key":"ref_34","unstructured":"Dudczyk, J., Kawalec, A., and Owczarek, R. (2008, January 19\u201321). An application of iterated function system attractor for specific radar source identification. Proceedings of the 17th International Conference on Microwaves, Radar and Wireless Communications, Wroclaw, Poland."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dudczyk, J., Kawalec, A., and Jacek, C. (2008, January 21\u201323). Applying the distance and similarity functions to radar signals identification. Proceedings of the 9th International Radar Symposium (IRS), Wroclaw, Poland.","DOI":"10.1109\/IRS.2008.4585771"},{"key":"ref_36","first-page":"113","article-title":"A method of feature selection in the aspect of specific identification of radar signals","volume":"65","author":"Dudczyk","year":"2017","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/11\/659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:31:00Z","timestamp":1760196660000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/11\/659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,20]]},"references-count":36,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["sym10110659"],"URL":"https:\/\/doi.org\/10.3390\/sym10110659","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2018,11,20]]}}}