{"title":"利用多样化扫描和迭代加强参数检测","authors":"","doi":"10.1016/j.jfranklin.2024.107221","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, an enhanced parametric detection method employing diversified scan and iteration is proposed for heterogeneous environment. A diversified scan, encompassing both rough scan and intensive scan, is first conducted in the normalized space–time frequency field to acquire the interference’s frequency components, which is conducive to obtaining the outline of interference in the normalized space–time two-dimensional (2-D) frequency field. The intensities of these components are subsequently determined through iteration to reduce the impact brought by the randomness of training samples. Meanwhile, the environment is commonly sparse in most cases, which is fully utilized in the proposed method. Then the cross-correlation matrix and autoregressive (AR) covariance matrix are reconstructed to form the corresponding detector. In the end, the availability and superiority of the proposed method are validated by numerical results. It is shown from numerical results that the proposed method performs well in detection performance compared to several existing typical methods in heterogeneous environment.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhanced parametric detection employing diversified scan and iteration\",\"authors\":\"\",\"doi\":\"10.1016/j.jfranklin.2024.107221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, an enhanced parametric detection method employing diversified scan and iteration is proposed for heterogeneous environment. A diversified scan, encompassing both rough scan and intensive scan, is first conducted in the normalized space–time frequency field to acquire the interference’s frequency components, which is conducive to obtaining the outline of interference in the normalized space–time two-dimensional (2-D) frequency field. The intensities of these components are subsequently determined through iteration to reduce the impact brought by the randomness of training samples. Meanwhile, the environment is commonly sparse in most cases, which is fully utilized in the proposed method. Then the cross-correlation matrix and autoregressive (AR) covariance matrix are reconstructed to form the corresponding detector. In the end, the availability and superiority of the proposed method are validated by numerical results. It is shown from numerical results that the proposed method performs well in detection performance compared to several existing typical methods in heterogeneous environment.</p></div>\",\"PeriodicalId\":17283,\"journal\":{\"name\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0016003224006422\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016003224006422","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Enhanced parametric detection employing diversified scan and iteration
In this paper, an enhanced parametric detection method employing diversified scan and iteration is proposed for heterogeneous environment. A diversified scan, encompassing both rough scan and intensive scan, is first conducted in the normalized space–time frequency field to acquire the interference’s frequency components, which is conducive to obtaining the outline of interference in the normalized space–time two-dimensional (2-D) frequency field. The intensities of these components are subsequently determined through iteration to reduce the impact brought by the randomness of training samples. Meanwhile, the environment is commonly sparse in most cases, which is fully utilized in the proposed method. Then the cross-correlation matrix and autoregressive (AR) covariance matrix are reconstructed to form the corresponding detector. In the end, the availability and superiority of the proposed method are validated by numerical results. It is shown from numerical results that the proposed method performs well in detection performance compared to several existing typical methods in heterogeneous environment.
期刊介绍:
The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.