Robust Estimation of Covariance Matrices

IEEE Transactions on Automatic Control

Volumn 35, Issue 9, 1990, Pages 1047-1051

  Reynolds, Reid G ^a  

^a NORTHROP GRUMMAN CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL TECHNIQUES--LEAST SQUARES APPROXIMATIONS; SIGNAL FILTERING AND PREDICTION--KALMAN FILTERING; SIGNAL PROCESSING--CORRELATION DETECTORS;

COVARIANCE MATRICES; COVARIANCE PARAMETERS; LEAST-SQUARES INNOVATION CORRELATION ALGORITHM; PROCESS-NOISE COVARIANCE PARAMETERS; SLOWLY VARYING SYSTEMS; SUBOPTIMAL KALMAN FILTER;

CONTROL SYSTEMS, TIME VARYING;

EID: 0025481806     PISSN: 00189286     EISSN: 15582523     Source Type: Journal    
DOI: 10.1109/9.58534     Document Type: Article

References (11)

1. P. R. Belanger, “Estimation of noise covariance matrices for a linear time-varying stochastic process,” Automatica, vol. 10, pp. 267-275, 1974.
2. C. Chen, Linear System Theory and Design. New York: Holt, Rinehart, and Winston, 1984.
3. G. C. Goodwin and K. S. Sin, Adaptive Filtering Prediction and Control. Englewood Cliffs, NJ: Prentice-Hall, 1984.
4. T. Kailath, Linear Systems. Englewood Cliffs, NJ: Prentice-Hall, 1986.
5. F. L. Lewis, Optimal Estimation. New York: Wiley, 1986.
6. L. Ljung and T. Soderstrom, Theory and Practice of Recursive Identification. Cambridge, MA: M.I.T. Press, 1987.
7. R. K. Mehra, “Approaches to adaptive filtering,” IEEE Trans. Automat. Contr., vol. AC-17, pp. 693-698, 1972.
8. R. K. Mehra, “On the identification of variances and adaptive Kalman filtering,” IEEE Trans. Automat. Contr., vol. AC-15, pp. 175-184, 1970.
9. J. M. Mendel, Lessons in Digital Estimation Theory. Englewood Cliffs, NJ: Prentice-Hall, 1987, pp. 71-80.
10. A. Papoulis, Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill, 1984, p. 186.
11. R. G. Reynolds, “Optimal filtering of FIR prefiltered data,” IEEE Trans. Automat. Contr., vol. 35, pp. 608-610, 1990.