Robust detection and estimation of soft failures in linear systems

Algorithms for the robust detection and estimation of soft failures which have very modest computational requirements are presented. Soft failures are characterized by deviations in the statistical parameters of system inputs and measurement errors. The on-line detection scheme utilizes a single Kalman filter and requires the implementation of Wald Sequential Detectors (WSD). The algorithm consists of repeated application of the Sequential Probability Ratio Test (SPRT) to the filter innovations and use of the sequence of Sample Numbers (SN) for each SPRT termination for the detection of a failure. The proposed scheme for the estimation of a failed parameter consists of a bank of detectors and utilizes the properties of the Average Sample Number (ASN). The capabilities of the scheme, including its robustness for the isolation of single parameter failures, are illustrated via simulation results for a maneuvering target problem.