Compressed Gaussian Estimation under Low Precision Numerical Representation.

Guivant, J; Narula, K; Kim, J; Li, X; Khan, S

Compressed Gaussian Estimation under Low Precision Numerical Representation.

Guivant, J Narula, K Kim, J

Li, X Khan, S

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2023, 23, (14), pp. 6406
Issue Date:: 2023-07-14

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Field	Value	Language
dc.contributor.author	Guivant, J
dc.contributor.author	Narula, K
dc.contributor.author	Kim, J https://orcid.org/0000-0003-4505-1103
dc.contributor.author	Li, X
dc.contributor.author	Khan, S
dc.date.accessioned	2024-01-16T03:18:06Z
dc.date.available	2023-07-07
dc.date.available	2024-01-16T03:18:06Z
dc.date.issued	2023-07-14
dc.identifier.citation	Sensors (Basel), 2023, 23, (14), pp. 6406
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/174563
dc.description.abstract	This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to implement on embedded processors. We propose a low-precision numerical representation for the global filter, such as 16-bit integer or 32-bit single-precision formats for the global covariance matrix, instead of the expensive double-precision, floating-point representation (64 bits). This truncation can inevitably cause filter instability since the truncated covariance matrix becomes overoptimistic or even turns to be an invalid covariance matrix. We introduce a Minimal Covariance Inflation (MCI) method to make the filter consistent while minimizing the truncation errors. Simulation-based experiments results show significant improvement of the proposed method with a reduction in the processing time with minimal loss of accuracy.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s23146406
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	3103 Ecology
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4104 Environmental management
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Compressed Gaussian Estimation under Low Precision Numerical Representation.
dc.type	Journal Article
utslib.citation.volume	23
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-16T03:18:04Z
pubs.issue	14
pubs.publication-status	Published online
pubs.volume	23
utslib.citation.issue	14

Abstract:

This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to implement on embedded processors. We propose a low-precision numerical representation for the global filter, such as 16-bit integer or 32-bit single-precision formats for the global covariance matrix, instead of the expensive double-precision, floating-point representation (64 bits). This truncation can inevitably cause filter instability since the truncated covariance matrix becomes overoptimistic or even turns to be an invalid covariance matrix. We introduce a Minimal Covariance Inflation (MCI) method to make the filter consistent while minimizing the truncation errors. Simulation-based experiments results show significant improvement of the proposed method with a reduction in the processing time with minimal loss of accuracy.

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http://hdl.handle.net/10453/174563