Linear model

Diagonal

Algorithm

Linguistics

Philosophy

Statistics

Covariance matrix

Covariance intersection

Extended Kalman filter

Diagonal matrix

Computer science

Mathematical optimization

Filter (signal processing)

Sensor fusion

Covariance

Fusion

Mathematics

Estimator

Artificial intelligence

Kalman filter

Geometry

Minimum-variance unbiased estimator

Machine learning

Optimal estimation

Computer vision

Information Fusion

This paper is concerned with the distributed information fusion estimation problem in multi-sensor environments. A universal distributed optimal linear fusion estimation (DOLFE) algorithm, which has a Kalman-type structure with matrix gains, is presented under the linear unbiased minimum variance criterion. To reduce the computational burden, two suboptimal linear fusion estimation algorithms with diagonal-matrix gains and scalar gains are also presented. Based on the proposed DOLFE algorithm, a distributed optimal linear fusion filter (DOLFF) is presented for multi-sensor linear discrete-time stochastic systems. It has better accuracy than that based on the matrix-weighted fusion of local estimators but worse accuracy than the centralized fusion filter (CFF). The stability and steady-state property of the proposed DOLFF are analyzed. Then, the corresponding multi-step predictor and smoother are also developed based on DOLFF. To obtain the distributed fusion estimators, some estimation error cross-covariance matrices that are used to compute the gains are derived. At last, distributed optimal linear fusion estimators with feedback are also presented. Furthermore, it is strictly proved that the proposed distributed optimal linear fusion filter with feedback (DOLFFWF) has the same accuracy as the CFF. Two simulation examples show the effectiveness of the proposed algorithms.

Distributed optimal linear fusion estimators