Measurement Fusion Algorithms Based on Left-Coprime Factorization

Abstract

Using the modern time series analysis method, by the left-coprime factorization, the autoregressive moving average (ARMA) innovation model is constructed, by which two measurement fusion steady-state Kalman filtering algorithms are presented. They have asymptotically global optimality. A numerical simulation example for threesensor tracking system verifies their functional equivalence to the centralized fusion steady-state Kalman filtering algorithms based on the ARMA innovation model and based on the Riccati equation by the classical Kalman filtering method.