Information Filtering and Array Algorithms for Descriptor Systems Subject to Parameter Uncertainties

• Published in: IEEE transactions on signal processing Vol. 55; no. 1; pp. 1 - 9
• Main Authors: Terra, M.H., Ishihara, J.Y., Padoan, A.C.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Array algorithm; Arrays; Covariance matrix; descriptor systems; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Filtering; Filtering algorithms; Filtration; information filter; Information filtering; Information filters; Information, signal and communications theory; Kalman filter; Mathematical models; Recursion; Recursive; Riccati equations; Robustness; Signal and communications theory; Signal processing algorithms; Signal, noise; State estimation; Telecommunications and information theory; Uncertain systems; Uncertainty; Performance evaluation; Array algorithm; Descriptor system; Filtering; information filter; Kalman filter; descriptor systems; Recursive method; Numerical simulation; Riccati equation; Algorithm
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2006.885747

This paper presents information filters in Riccati recursions and in array algorithms for descriptor systems subject to parameters uncertainties. The filters are developed in filtered and predicted forms. The inversion of the state matrix is avoided in the new information recursive formulas. Therefore, it turns out clear that the invertibility of the state matrix, usually considered in the state-space information recursions, is not necessary. A numerical example is provided to illustrate the performance of the proposed robust array algorithms