{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T12:02:46Z","timestamp":1648814566234},"reference-count":34,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2015]]},"DOI":"10.1587\/transinf.2014edp7416","type":"journal-article","created":{"date-parts":[[2015,8,31]],"date-time":"2015-08-31T18:15:25Z","timestamp":1441044925000},"page":"1683-1690","source":"Crossref","is-referenced-by-count":1,"title":["Radar HRRP Target Recognition Based on the Improved Kernel Distance Fuzzy C-Means Clustering Method"],"prefix":"10.1587","volume":"E98.D","author":[{"given":"Kun","family":"CHEN","sequence":"first","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuehua","family":"LI","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingjian","family":"XU","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] M. Vespe, C.J. Baker, and H.D. Griffiths, \u201cRadar target classification using multiple perspectives,\u201d IET Radar Sonar Navig., vol.4, no.1, pp.300-307, 2007.","DOI":"10.1049\/iet-rsn:20060049"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] L. Du, H. Liu, Z. Bao, and J. Zhang, \u201cA two-distribution compounded statistical model for radar HRRP target recognition,\u201d IEEE Trans. Signal Process., vol.54, no.6, pp.2226-2238, 2006.","DOI":"10.1109\/TSP.2006.873534"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] B.R. Mahafza and A.Z. Elsherbeni, Matlab simulations for radar systems design, Chapman and Hall\/CRC, 2003.","DOI":"10.1201\/9780203502556"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] A. Zyweck and R.E. Bogner, \u201cRadar target classification of commercial aircraft,\u201d IEEE Trans. Aerosp. Electron. Syst., vol.32, no.2, pp.598-606, 1996.","DOI":"10.1109\/7.489504"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] J.P. Zwart, R. van der Heiden, S. Gelsema, and F. Groen, \u201cFast translation invariant classification of HRR range profiles in zero phase representation,\u201d Proc. Inst. Elect. Eng. Radar Sonar Navig., vol.150, no.6, pp.411-418, Feb. 2003.","DOI":"10.1049\/ip-rsn:20030428"},{"key":"6","unstructured":"[6] X. Liao, Z. Bao, and M. Xing, \u201cOn the aspect sensitivity of high resolution range profiles and its reduction methods,\u201d Conf. Rec. IEEE Int. Radar, pp.310-315, Alexandria, Egypt, May 2000."},{"key":"7","unstructured":"[7] F. Chen, W. Yu, X. Liu, and K. Wang, \u201cCombinational matching method of amplitude and time-shift for radar HRRP recognition,\u201d Proc. International Geoscience and Remote Sensing Symposium, pp.1311-1314, 2011."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J.R. Diemunsch, J. Wissinger, and E.G. Zelnio, \u201cMoving and stationary target acquisition and recognition (MSTAR) model-based automatic target recognition: Search technology for a robust ATR,\u201d Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, pp.481-492, 1998.","DOI":"10.1117\/12.321851"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] L. Du, H. Liu, Z. Bao, and M. Xing, \u201cRadar HRRP target recognition based on higher-order spectra,\u201d IEEE Trans. Singal Process., vol.53, no.7, pp.2359-2368, 2005.","DOI":"10.1109\/TSP.2005.849161"},{"key":"10","unstructured":"[10] L. Du, H.W. Liu, and Z. Bao, \u201cRadar HRRP target recognition by the higher order spectra features,\u201d Proc. IASTED Int. Conf. Appl. Informatics, pp.627-632, Innsbruck, Austria, 2004."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] X.-D. Zhang, Y. Shi, and Z. Bao, \u201cA new feature vector using selected bispectra for signal classification with application in radar target recognition,\u201d IEEE Trans. Signal Process., vol.49, no.9, pp.1875-1885, 2001.","DOI":"10.1109\/78.942617"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] L. Shi, P. Wang, H. Liu, L. Xu, and Z. Bao, \u201cRadar HRRP statistical recognition with local factor analysis by automatic Bayesian Ying-Yang harmony learning,\u201d IEEE Trans. Signal Process., vol.59, no.2, pp.610-617, 2011.","DOI":"10.1109\/TSP.2010.2088391"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] B. Feng, L. Du, C. Shao, P. Wang, and H. Liu, \u201cRadar HRRP target recognition based on robust dictionary learning with small training data size,\u201d Proc. IEEE Radar Conference, pp.1-4, 2013.","DOI":"10.1109\/RADAR.2013.6586036"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Q.Y. Hou, H.W. Liu, F. Chen, and Z. Bao, \u201cAdaptive statistical model for radar HRRP recognition,\u201d Proc. IET International Radar Conference, pp.502-505, 2009.","DOI":"10.1049\/cp.2009.0358"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] B. Chen, H. Liu, and Z. Bao, \u201cPCA and kernel PCA for radar high range resolution profiles recognition,\u201d Proc. Radar Conference, pp.528-533, 2005.","DOI":"10.1109\/ICR.2006.343412"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. Copsey and A.R. Webb, \u201cBayesian gamma mixture model approach to radar target recognition,\u201d IEEE Trans. Aerosp. Electron. Syst., vol.39, no.4, pp.1201-1217, Oct. 2003.","DOI":"10.1109\/TAES.2003.1261122"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] M. Pan, L. Du, P. Wang, H. Liu, and Z. Bao, \u201cNoise-robust modification method for Gaussian-based models with application to radar recognition,\u201d IEEE Geosci. Remote Sens. Lett., vol.10, no.3, pp.558-562, 2013.","DOI":"10.1109\/LGRS.2012.2213234"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] R. Williams, J. Westerkamp, D. Gross, and A. Palomino, \u201cAutomatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar,\u201d IEEE Aerosp. Electron. Syst. Mag., vol.15, no.4, pp.37-43, 2000.","DOI":"10.1109\/62.839633"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] K. Ni, Y. Qi, and L. Carin, \u201cMulti-aspect target classification and detection via the infinite hidden Markov model,\u201d IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP&apos;07, pp.II-433-II-436, 2007.","DOI":"10.1109\/ICASSP.2007.366265"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] J.C. Dunn, \u201cA fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,\u201d Journal of Cybernetics, vol.3, no.3, pp.32-57, 1973.","DOI":"10.1080\/01969727308546046"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, 1981.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] R.A. Krishnapuram and J.M. Keller, \u201cA possibilistic approach to clustering,\u201d IEEE Trans. Fuzzy Syst., vol.1, no.2, pp.98-110, 1993.","DOI":"10.1109\/91.227387"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] N.R. Pal, K. Pal, J.M. Keller, and J.C. Bezdek, \u201cA possibilistic fuzzy c-means clustering algorithm,\u201d IEEE Trans. Fuzzy Syst., vol.13, no.4, pp.517-530, 2005.","DOI":"10.1109\/TFUZZ.2004.840099"},{"key":"24","unstructured":"[24] X.-H. Wu and J.-J. Zhou, \u201cPossibilistic fuzzy c-means clustering model using kernel methods,\u201d International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC&apos;06), pp.465-470, 2005."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] D.-Q. Zhang and S.-C. Chen, \u201cA novel kernelized fuzzy c-means algorithm with application in medical image segmentation,\u201d Artif. Intell. Med., vol.32, no.1, pp.37-50, 2004.","DOI":"10.1016\/j.artmed.2004.01.012"},{"key":"26","unstructured":"[26] S. Theodoridis and K. Koutroumbas, Pattern Recognition 3rd ed., Tsinghua University Press, Beijing, 2006."},{"key":"27","unstructured":"[27] M. Sugeno and Y. Fukuyama, \u201cA new method of choosing the number of clusters for the fuzzy c-means method,\u201d Proc. Fifth Fuzzy Systems Symposium, pp.247-250, 1989."},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] M.-S. Yang and K.-L. Wu, \u201cUnsupervised possibilistic clustering,\u201d Pattern Recognit., vol.39, no.1, pp.5-21, 2006.","DOI":"10.1016\/j.patcog.2005.07.005"},{"key":"29","unstructured":"[29] R. Inokuchi and S. Miyamoto, \u201cLVQ clustering and SOM using a kernel function,\u201d Proc. IEEE Interational Conference on Fuzzy Systems, pp.1497-1500, 2004."},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] F. Camastra and A. Verri, \u201cA novel kernel method for clustering,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.25, no.5, pp.801-805, 2005.","DOI":"10.1109\/TPAMI.2005.88"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] J.-H. Chiang and P.-Y. Hao, \u201cA new kernel-based fuzzy clustering approach: Support vector clustering with cell growing,\u201d IEEE Trans. Fuzzy Syst., vol.11, no.4, pp.518-527, 2003.","DOI":"10.1109\/TFUZZ.2003.814839"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] U. von Luxburg, \u201cA tutorial on spectral clustering,\u201d Statistics and Computing, vol.17, no.4, pp.395-416, 2007.","DOI":"10.1007\/s11222-007-9033-z"},{"key":"33","unstructured":"[33] T.D. Ross, S.W. Worrell, V.J. Velten, J.C. Mossing, and M.L. Bryant, \u201cStandard SAR ATR evaluation experiments using the MSTAR public release data set,\u201d Proc. SPIE Conf. Algorithms Synthetic Aperture Radar Imagery V, vol.3370, pp.566-573, 1998."},{"key":"34","unstructured":"[34] http:\/\/archive.ics.uci.eud\/ml\/"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E98.D\/9\/E98.D_2014EDP7416\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T21:34:04Z","timestamp":1567114444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E98.D\/9\/E98.D_2014EDP7416\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":34,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2015]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2014edp7416","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015]]}}}