ROBUST FILTER DESIGN FOR UNCERTAIN SYSTEMS DEFINED BY BOTH HARD AND SOFT BOUNDS

A new approach to robust linear filter design is described that attemp ts to combine the advantages of H-infinity robust linear synthesis wit h a probabilistic method of system and noise modeling. The signal and measurement noise model parameters are assumed to be subject to pertur bations represented by random variables with known covariances, The sy stem is represented in polynomial form, and the uncertainty can be des cribed by both soft and hard bounds. An H-infinity cost-function is mi nimized and averaged with respect to model errors in signal and noise descriptions, The polynomial solution is no more complicated than the usual H-infinity optimal filter and involves averaged spectral factori zations and linear equations, Both usual and deconvolution filtering p roblems are considered.