{"title":"A novel hypothesis splitting method implementation for multi-hypothesis filters","authors":"Enis Bayramoglu, Ole Ravn, N. Andersen","doi":"10.1109/ICCA.2013.6564951","DOIUrl":null,"url":null,"abstract":"The paper presents a multi-hypothesis filter library featuring a novel method for splitting Gaussians into ones with smaller variances. The library is written in C++ for high performance and the source code is open and free1. The multi-hypothesis filters commonly approximate the distribution transformations better, if the covariances of the individual hypotheses are sufficiently small. We propose a look-up table based method to calculate a set of Gaussian hypotheses approximating a wider Gaussian in order to improve the filter approximation. Python bindings for the library are also provided for fast prototyping.","PeriodicalId":336534,"journal":{"name":"2013 10th IEEE International Conference on Control and Automation (ICCA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 10th IEEE International Conference on Control and Automation (ICCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCA.2013.6564951","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The paper presents a multi-hypothesis filter library featuring a novel method for splitting Gaussians into ones with smaller variances. The library is written in C++ for high performance and the source code is open and free1. The multi-hypothesis filters commonly approximate the distribution transformations better, if the covariances of the individual hypotheses are sufficiently small. We propose a look-up table based method to calculate a set of Gaussian hypotheses approximating a wider Gaussian in order to improve the filter approximation. Python bindings for the library are also provided for fast prototyping.
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