{"title":"Neural net based variable structure multiple model reducing mode set jump delay","authors":"D. Choi, B. Ahn, Hanseok Ko","doi":"10.1109/SSP.2001.955242","DOIUrl":null,"url":null,"abstract":"Variable structure multiple model (VSMM) is one of the most powerful algorithms for effectively tracking a single maneuvering target. Although VSMM is developed specifically to improve the interactive multiple model (MM) method focused to reducing computational cost and improving tracking performance, it presents an inherent limitation in the form of the presence of mode set jump delay (MJD). MJD as an undesirable phenomenon in VSMM is described and analyzed. In order to eliminate the MJD, a neural network based VSMM that automatically selects the optimal mode set as achieved by supervised training is proposed. Through representative simulations we show the proposed algorithm outperforming over the conventional digraph switching VSMM in terms of tracking error.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"79 1","pages":"142-145"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"信号处理","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/SSP.2001.955242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Variable structure multiple model (VSMM) is one of the most powerful algorithms for effectively tracking a single maneuvering target. Although VSMM is developed specifically to improve the interactive multiple model (MM) method focused to reducing computational cost and improving tracking performance, it presents an inherent limitation in the form of the presence of mode set jump delay (MJD). MJD as an undesirable phenomenon in VSMM is described and analyzed. In order to eliminate the MJD, a neural network based VSMM that automatically selects the optimal mode set as achieved by supervised training is proposed. Through representative simulations we show the proposed algorithm outperforming over the conventional digraph switching VSMM in terms of tracking error.
期刊介绍:
Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.