AI & ML
Optimal Linear Filtering of Markov Jump Systems
arXiv cs.SY0 views
arXiv:2610.34095v1 Announce Type: new Abstract: This paper deals with the optimal state estimation problem for discrete-time Markov jump linear systems in the sense of linear minimum mean-square error where the noises corrupting the system are mode-dependent and need not be Gaussian. An orthogonal projection extension-based equation (OPEBE) is established, which can decompose a random vector into several parts where the random vector contains conditional expectation of another random vector. Using the OPEBE and some results obtained in this paper, an optimal linear filter for the system under consideration is proposed. The computational and storage load of the proposed filter does not increase with time. Also, the proposed filter is finite-dimensionally computable and has a recursive structure. Computer simulations are provided to verify the effectiveness and performance of the proposed optimal linear filter.
Source:https://arxiv.org/abs/2610.34095PreviousWater as a Battery: Using Hydropower Flexibility to Support the GridNextEvaluating Wi-Fi HaLow for Next-Generation Capsule Endoscopy
Related posts

10 Open-Source Robotics Repositories for 3D Perception, VLA Policies, and Sensor Calibration
0dev.to
My self-modifying system tried to wire a gate-passing module into its own protection enforcer. It was refused three times.
0dev.to
Last night an AI agent performed brain surgery on a mouse
0Hacker News
The Card-Update Page Gets One Visit. Design for That Visit.
0dev.to UX
SaaS Partner Scout: A Script for Identifying GitHub SaaS Project Contributors
0dev.to GitHub
WARN Act and Retaliation — Can Your Employer Punish You for Asking About a Layoff Notice?
0dev.to Data