Adaptive Kalman Filter for Linear Systems with Additive and Multiplicative Noises Article Swipe
This manuscript investigates adaptive Kalman filter problem of of linear systems with multiplicative and additive noises. The main contributions are stated in two aspects. Firstly, compared with the estimation problem of linear systems with additive noises, we propose an algorithm that is applicable to the linear systems with both additive and multiplicative noises. To solve the technical issue raised by the multiplicative noise, a variational Bayesian approach is proposed. Moreover, the proposed approach is capable of estimating the multiplicative and additive measurement noise covariances as a whole, while the existing algorithms often operate in a separate way. Secondly, in contrast with existing literature, where the covariance of the multiplicative noise is assumed to be fixed and known, we focus on the situation that the covariances of both additive and multiplicative noises are time-varying and unknown. Towards this end, a novel adaptive Kalman filter is proposed to jointly estimate the covariances of multiplicative and additive noises based on projection formula and a VB approach.
Related Topics
Concepts
Multiplicative function
Multiplicative noise
Kalman filter
Covariance
Mathematics
Additive model
Filter (signal processing)
Noise (video)
Projection (relational algebra)
Algorithm
Computer science
Linear system
Control theory (sociology)
Mathematical optimization
Statistics
Artificial intelligence
Computer vision
Computer hardware
Signal transfer function
Mathematical analysis
Analog signal
Image (mathematics)
Digital signal processing
Control (management)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.36227/techrxiv.14576289
- https://www.techrxiv.org/articles/preprint/Adaptive_Kalman_Filter_for_Linear_Systems_with_Additive_and_Multiplicative_Noises/14576289/1/files/27969216.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4231605874
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4231605874Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.36227/techrxiv.14576289Digital Object Identifier
- Title
-
Adaptive Kalman Filter for Linear Systems with Additive and Multiplicative NoisesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-14Full publication date if available
- Authors
-
Xingkai YuList of authors in order
- Landing page
-
https://doi.org/10.36227/techrxiv.14576289Publisher landing page
- PDF URL
-
https://www.techrxiv.org/articles/preprint/Adaptive_Kalman_Filter_for_Linear_Systems_with_Additive_and_Multiplicative_Noises/14576289/1/files/27969216.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.techrxiv.org/articles/preprint/Adaptive_Kalman_Filter_for_Linear_Systems_with_Additive_and_Multiplicative_Noises/14576289/1/files/27969216.pdfDirect OA link when available
- Concepts
-
Multiplicative function, Multiplicative noise, Kalman filter, Covariance, Mathematics, Additive model, Filter (signal processing), Noise (video), Projection (relational algebra), Algorithm, Computer science, Linear system, Control theory (sociology), Mathematical optimization, Statistics, Artificial intelligence, Computer vision, Computer hardware, Signal transfer function, Mathematical analysis, Analog signal, Image (mathematics), Digital signal processing, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.contrast | 99 |
| abstract_inverted_index.estimate | 147 |
| abstract_inverted_index.existing | 89, 101 |
| abstract_inverted_index.proposed | 71, 144 |
| abstract_inverted_index.separate | 95 |
| abstract_inverted_index.unknown. | 134 |
| abstract_inverted_index.Moreover, | 69 |
| abstract_inverted_index.Secondly, | 97 |
| abstract_inverted_index.algorithm | 39 |
| abstract_inverted_index.approach. | 162 |
| abstract_inverted_index.proposed. | 68 |
| abstract_inverted_index.situation | 121 |
| abstract_inverted_index.technical | 56 |
| abstract_inverted_index.algorithms | 90 |
| abstract_inverted_index.applicable | 42 |
| abstract_inverted_index.covariance | 105 |
| abstract_inverted_index.estimating | 76 |
| abstract_inverted_index.estimation | 28 |
| abstract_inverted_index.manuscript | 1 |
| abstract_inverted_index.projection | 157 |
| abstract_inverted_index.covariances | 83, 124, 149 |
| abstract_inverted_index.literature, | 102 |
| abstract_inverted_index.measurement | 81 |
| abstract_inverted_index.variational | 64 |
| abstract_inverted_index.investigates | 2 |
| abstract_inverted_index.time-varying | 132 |
| abstract_inverted_index.contributions | 18 |
| abstract_inverted_index.multiplicative | 12, 51, 61, 78, 108, 129, 151 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100608388 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.59031223 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |