Multiple-model adaptive estimation using a residual correlation Kalman filter bank

We propose a modified multiple model adaptive estimation (MMAE) algorithm that uses the time correlation of the Kalman filter residuals, in place of their scaled magnitude, to assign conditional probabilities for each of the modeled hypotheses. This modified algorithm, denoted the residual correlation Kalman filter bank (RCKFB), uses the magnitude of an estimate of the correlation of the residual with a slightly modified version of the usual MMAE hypothesis testing algorithm to assign the conditional probabilities to the various hypotheses that are modeled in the Kalman filter bank. The correlation of the residual is estimated by collecting several samples of the residual and using the periodogram algorithm. This technique can detect highly time-correlated signals at very low signal-to-noise ratios. This concept is used to detect flight control actuator failures, where the existence of a single frequency sinusoid (which is highly time-correlated) in the residual of an elemental filter within an MMAE is indicative of that filter having the wrong actuator failure status hypothesis. This technique allows a significant reduction of the amplitude of the required system dithers for exciting the various system modes to enhance identifiability, to the point where they may possibly be subliminal, so as not to be objectionable to the pilot and passengers.