A novel hypothesis splitting method implementation for multi-hypothesis filters

Enis Bayramoglu; Ole Ravn; Nils Axel Andersen

doi:10.1109/ICCA.2013.6564951

A novel hypothesis splitting method implementation for multi-hypothesis filters

Enis Bayramoglu, Ole Ravn, Nils Axel Andersen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 2013 10th IEEE International Conference on Control and Automation
Publisher	IEEE
Publication date	2013
Pages	574-579
ISBN (Print)	9781467347075
DOIs	https://doi.org/10.1109/ICCA.2013.6564951
Publication status	Published - 2013
Event	2013 10th IEEE International Conference on Control and Automation (ICCA) - Hangzhou, China Duration: 12 Jun 2013 → 14 Jun 2013

Conference

Conference	2013 10th IEEE International Conference on Control and Automation (ICCA)
Country/Territory	China
City	Hangzhou
Period	12/06/2013 → 14/06/2013

Keywords

Approximation theory
C++ language
Filtering theory
Gaussian distribution
Mobile robots
Public domain software
Software libraries
Table lookup

Access to Document

10.1109/ICCA.2013.6564951

OpenUrl availability

Full text

Cite this

@inproceedings{816e846b6c5549f4bb61fa3839358518,

title = "A novel hypothesis splitting method implementation for multi-hypothesis filters",

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.",

keywords = "Approximation theory, C++ language, Filtering theory, Gaussian distribution, Mobile robots, Public domain software, Software libraries, Table lookup",

author = "Enis Bayramoglu and Ole Ravn and Andersen, {Nils Axel}",

year = "2013",

doi = "10.1109/ICCA.2013.6564951",

language = "English",

isbn = "9781467347075",

pages = "574--579",

booktitle = "Proceedings of the 2013 10th IEEE International Conference on Control and Automation",

publisher = "IEEE",

address = "United States",

note = "2013 10th IEEE International Conference on Control and Automation (ICCA) ; Conference date: 12-06-2013 Through 14-06-2013",

}

Bayramoglu, E, Ravn, O & Andersen, NA 2013, A novel hypothesis splitting method implementation for multi-hypothesis filters. in Proceedings of the 2013 10th IEEE International Conference on Control and Automation. IEEE, pp. 574-579, 2013 10th IEEE International Conference on Control and Automation (ICCA), Hangzhou, China, 12/06/2013. https://doi.org/10.1109/ICCA.2013.6564951

TY - GEN

T1 - A novel hypothesis splitting method implementation for multi-hypothesis filters

AU - Bayramoglu, Enis

AU - Ravn, Ole

AU - Andersen, Nils Axel

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Approximation theory

KW - C++ language

KW - Filtering theory

KW - Gaussian distribution

KW - Mobile robots

KW - Public domain software

KW - Software libraries

KW - Table lookup

U2 - 10.1109/ICCA.2013.6564951

DO - 10.1109/ICCA.2013.6564951

M3 - Article in proceedings

SN - 9781467347075

SP - 574

EP - 579

BT - Proceedings of the 2013 10th IEEE International Conference on Control and Automation

PB - IEEE

T2 - 2013 10th IEEE International Conference on Control and Automation (ICCA)

Y2 - 12 June 2013 through 14 June 2013

ER -

A novel hypothesis splitting method implementation for multi-hypothesis filters

Abstract

Conference

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this