preprint · arXiv (Cornell University) · 2019
The universality of the Kalman filter: a conditional characteristic function perspective
Sandhya Rathore, Shambhu N. Sharma, Shaival H. Nagarsheth
Cited by 0Open access
Abstract
The universality of the celebrated Kalman filtering can be found in control theory. The Kalman filter has found its striking applications in sophisticated autonomous systems and smart products, which are attributed to its realization in a single complex chip. In this paper, we revisit the Kalman filter from the perspective of conditional characteristic function evolution and Ito calculus and develop three Kalman filtering Theorems and their formal proof. Most notably, this paper reveals the following: (i) Kalman filtering equations are a consequence of the evolution of conditional characteristic function for the linear stochastic differential system coupled with the linear discrete measurement system. (ii) The Kalman filtering is a consequence of the stochastic evolution of conditional characteristic function for the linear stochastic differential system coupled with the linear continuous measurement system. (iii) The structure of the Kalman filter remains invariant under two popular stochastic interpretations, the Ito vs Stratonovich.
Cite this paper
Rathore, S., Sharma, S. N., & Nagarsheth, S. H. (2019). The universality of the Kalman filter: a conditional characteristic function perspective. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1910.03446
Rathore, Sandhya, et al. “The universality of the Kalman filter: a conditional characteristic function perspective.” arXiv (Cornell University), 2019, https://doi.org/10.48550/arxiv.1910.03446.
Rathore, Sandhya, Shambhu N. Sharma, and Shaival H. Nagarsheth. 2019. “The universality of the Kalman filter: a conditional characteristic function perspective.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1910.03446.
Rathore, S., Sharma, S.N. and Nagarsheth, S.H. (2019) ‘The universality of the Kalman filter: a conditional characteristic function perspective’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1910.03446.
@misc{rathore2019universality,
title = {{The universality of the Kalman filter: a conditional characteristic function perspective}},
author = {Rathore, Sandhya and Sharma, Shambhu N. and Nagarsheth, Shaival H.},
journal = {arXiv (Cornell University)},
year = {2019},
doi = {10.48550/arxiv.1910.03446}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
Related papers
Kalman filtering with state constraints: a survey of linear and nonlinear algorithms
D. Simon · IET Control Theory and Applications · 2010
Cited by 910
Non-linear and robust filtering: From the Kalman filter to the particle filter
Jason Ford · QUT ePrints (Queensland University of Technology) · 2002
Cited by 6Open access
An Introduction to the Kalman Filter
Greg Welch, Gary Bishop · CiteSeer X (The Pennsylvania State University) · 1995
Cited by 4,888Open access
Solution of the Kalman filtering problem for stationary noise and finite data records
Michael John Grimble · International Journal of Systems Science · 1979
Cited by 11
Exact finite dimensional nonlinear filters
Fred E. Daum · IEEE Conference on Decision and Control (CDC) · 1985
Cited by 14
Kalman filtering with multiplicative and additive noises
Yilin Wu, Qian Zhang, Zhiping Shen · World Congress on Intelligent Control and Automation · 2016
Cited by 15
Comparison of H_∞ Robust Filter and Kalman Filter
Zhang Li-kun · Systems engineering and electronics · 2003
Cited by 3
Exact finite-dimensional nonlinear filters
Fred E. Daum · IEEE Transactions on Automatic Control · 1986
Cited by 165