{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:24:30Z","timestamp":1766298270209},"reference-count":19,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2023,7,1]]},"DOI":"10.1587\/transfun.2022eal2042","type":"journal-article","created":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T22:09:36Z","timestamp":1672092576000},"page":"1002-1006","source":"Crossref","is-referenced-by-count":3,"title":["Persymmetric Structured Covariance Matrix Estimation Based on Whitening for Airborne STAP"],"prefix":"10.1587","volume":"E106.A","author":[{"given":"Quanxin","family":"MA","sequence":"first","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University"}]},{"given":"Xiaolin","family":"DU","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University"}]},{"given":"Jianbo","family":"LI","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications"}]},{"given":"Yang","family":"JING","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University"}]},{"given":"Yuqing","family":"CHANG","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] J. Ward, \u201cSpace-time adaptive processing for airborne radar,\u201d Technical Report, MIT Lincoln Laboratory, 1994."},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] B. Tang, J. Liu, Z. Huang, G. Wang, and F. Fan, \u201cAdaptive target detection in Gaussian clutter edges,\u201d IEEE Trans. Aerosp. Electron. Syst., vol.56, no.2, pp.1662-1673, 2019. 10.1109\/taes.2019.2930019","DOI":"10.1109\/TAES.2019.2930019"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] Y. Wu, T. Wang, J. Wu, and J. Duan, \u201cTraining sample selection for space-time adaptive processing in heterogeneous environments,\u201d IEEE Geosci. Remote Sensing Lett., vol.12, no.4, pp.691-695, 2015. 10.1109\/lgrs.2014.2357804","DOI":"10.1109\/LGRS.2014.2357804"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] P. Wang, Z. Wang, H. Li, and B. Himed, \u201cKnowledge-aided parametric adaptive matched filter with automatic combining for covariance estimation,\u201d IEEE Trans. Signal Process., vol.62, no.18, pp.4713-4722, 2014. 10.1109\/tsp.2014.2338838","DOI":"10.1109\/TSP.2014.2338838"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] G. Sun, Z. He, J. Tong, and X. Zhang, \u201cKnowledge-aided covariance matrix estimation via Kronecker product expansions for airborne STAP,\u201d IEEE Geosci, Remote Sensing Lett., vol.15, no.4, pp.527-531, 2018. 10.1109\/lgrs.2018.2799329","DOI":"10.1109\/LGRS.2018.2799329"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Bergin, C. Teixeira, P. Techau, and J. Guerci, \u201cSTAP with knowledge-aided data pre-whitening,\u201d Proc. 2004 IEEE Radar Conference, pp.289-294, 2004. 10.1109\/nrc.2004.1316437","DOI":"10.1109\/NRC.2004.1316437"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] G. Cui, N. Li, L. Pallotta, G. Foglia, and L. Kong, \u201cGeometric barycenters for covariance estimation in compound-Gaussian clutter,\u201d IET Radar, Sonar &amp; Navigation, vol.11, no.3, pp.404-409, 2017. 10.1049\/iet-rsn.2016.0092","DOI":"10.1049\/iet-rsn.2016.0092"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] M. Tang, Y. Rong, A. De Maio, C. Chen, and J. Zhou, \u201cAdaptive radar detection in Gaussian disturbance with structured covariance matrix via invariance theory,\u201d IEEE Trans. Signal Process., vol.67, no.21, pp.5671-5685, 2019. 10.1109\/tsp.2019.2941119","DOI":"10.1109\/TSP.2019.2941119"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] J. Li, A. Aubry, A. De Maio, and J. Zhou, \u201cAn EL approach for similarity parameter selection in KA covariance matrix estimation,\u201d IEEE Signal Process. Lett., vol.26, no.8, pp.1217-1221, 2019. 10.1109\/lsp.2019.2925582","DOI":"10.1109\/LSP.2019.2925582"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] P. Stoica, J. Li, X. Zhu, and J.R. Guerci, \u201cOn using a priori knowledge in space-time adaptive processing,\u201d IEEE Trans. Signal Process., vol.56, no.6, pp.2598-2602, 2008. 10.1109\/tsp.2007.914347","DOI":"10.1109\/TSP.2007.914347"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] S. Zhang, Z. He, J. Li, and Y. Wang, \u201cA robust colored-loading factor optimization approach for knowledge-aided STAP,\u201d 2016 IEEE Radar Conference (RadarConf), pp.1-5, 2016. 10.1109\/radar.2016.7485266","DOI":"10.1109\/RADAR.2016.7485266"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] X. Du, A. Aubry, A. De Maio, and G. Cui, \u201cToeplitz structured covariance matrix estimation for radar applications,\u201d IEEE Signal Process. Lett., vol.27, pp.595-599, 2020. 10.1109\/LSP.2020.2984431","DOI":"10.1109\/LSP.2020.2984431"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] G. Foglia, C. Hao, G. Giunta, and D. Orlando, \u201cKnowledge-aided adaptive detection in partially homogeneous clutter: Joint exploitation of persymmetry and symmetric spectrum,\u201d Digital Signal Processing, vol.67, pp.131-138, 2017. 10.1016\/j.dsp.2017.04.003","DOI":"10.1016\/j.dsp.2017.04.003"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] L. Cai and H. Wang, \u201cA persymmetric multiband GLR algorithm,\u201d IEEE Trans. Aerosp. Electron. Syst., vol.28, no.3, pp.806-816, 1992. 10.1109\/7.256301","DOI":"10.1109\/7.256301"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] J. Liu, W. Zhou, A. Zaimbashi, and H. Li, \u201cPersymmetric adaptive array detection of spread spectrum signals,\u201d IEEE Trans. Inf. Theory, vol.66, no.12, pp.7828-7834, 2020. 10.1109\/tit.2020.3014782","DOI":"10.1109\/TIT.2020.3014782"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] G. Sun, Z. He, F. Jia, and R. Li, \u201cKronecker product PCA for structured covariance matrix of airborne radar STAP,\u201d 2017 IEEE Radar Conference (RadarConf), pp.1015-1019, IEEE, 2017. 10.1109\/radar.2017.7944352","DOI":"10.1109\/RADAR.2017.7944352"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] N. Kang, Z. Shang, and Q. Du, \u201cKnowledge-aided structured covariance matrix estimator applied for radar sensor signal detection,\u201d Sensors, vol.19, no.3, p.664, 2019. 10.3390\/s19030664","DOI":"10.3390\/s19030664"},{"key":"18","unstructured":"[18] R.C. DiPietro, \u201cExtended factored space-time processing for airborne radar systems,\u201d Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems &amp; Computers, pp.425-426, IEEE Computer Society, 1992. 10.1109\/acssc.1992.269236"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] J. Guerci and E. Baranoski, \u201cKnowledge-aided adaptive radar at DARPA: An overview,\u201d IEEE Signal Process. Mag., vol.23, no.1, pp.41-50, 2006. 10.1109\/msp.2006.1593336","DOI":"10.1109\/MSP.2006.1593336"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/7\/E106.A_2022EAL2042\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:44:54Z","timestamp":1728607494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/7\/E106.A_2022EAL2042\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":19,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2022eal2042","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"type":"print","value":"0916-8508"},{"type":"electronic","value":"1745-1337"}],"subject":[],"published":{"date-parts":[[2023,7,1]]},"article-number":"2022EAL2042"}}