Limited Memory Prediction for Linear Systems with Different types of Observation

TL;DR

This paper develops a distributed limited memory prediction algorithm for continuous-time linear stochastic systems with multiple sensors, enabling reliable estimation even with sensor faults through parallel processing and optimal fusion.

Contribution

It introduces a novel distributed prediction algorithm with parallel structure and error covariance derivation, improving robustness and efficiency in sensor fusion for linear systems.

Findings

Algorithm achieves effective distributed prediction with fault tolerance.

Parallel processing enhances computational efficiency.

Example confirms the algorithm's effectiveness.

Abstract

This paper is concerned with distributed limited memory prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local limited memory predictors. The distributed prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The algorithm has the parallel structure and allows parallel processing of observations making it reliable since the rest faultless sensors can continue to the fusion estimation if some sensors occur faulty. The derivation of equations for error cross-covariances between the local predictors is the key of this paper. Example demonstrates effectiveness of the distributed limited memory predictor.