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Found 9 papers, 0 papers with code
Data-Driven Kalman Filter using Maximum Likelihood Optimization
A novel data-driven Kalman filter (DDKF) that combines model identification with state estimation is developed using pre-collected input-output data and uncertain initial state information of the unknown system.
A Framework on Fully Distributed State Estimation and Cooperative Stabilization of LTI Plants
Further, the proposed method can be applied to pure fully distributed state estimation scenarios and modified for noise-bounded LTI plants.
On the Effects and Optimal Design of Redundant Sensors in Collaborative State Estimation
The paper presents two main results: a theoretical analysis of the effects of redundant sensors and an engineering-oriented optimal design of redundant sensors.
Cooperative Control of Multi-Channel Linear Systems with Self-Organizing Private Agents
Cooperative behavior design for multi-agent systems with collective tasks is a critical issue in promoting swarm intelligence.
Observation of Periodic Systems: Bridge Centralized Kalman Filtering and Consensus-Based Distributed Filtering
This paper aims to consider the observation problem of periodic systems by bridging two fundamental filtering algorithms for periodic systems with a sensor network: consensus-on-measurement-based distributed filtering (CMDF) and centralized Kalman filtering (CKF).
Harmonic-Copuled Riccati Equations and its Applications in Distributed Filtering
This paper manages to formulate and investigate a new kind of coupled Riccati equations, called harmonic-coupled Riccati equations (HCRE), from the matrix iterative law of the consensus on information-based distributed filtering (CIDF) algortihm proposed in [1], where the solutions of the equations are coupled with harmonic means.
Stochastic Event-triggered Variational Bayesian Filtering
This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances.
Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework
The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors.
Consensus-Based Distributed Filtering with Fusion Step Analysis
For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering.
