Fusion techniques using distributed Kalman filtering for detecting changes in systemsA comparison is made of the performances of two detection strategies that are based on different data fusion techniques. The strategies detect changes in a linear system. One detection strategy involves combining the estimates and error covariance matrices of distributed Kalman filters, generating a residual from the used estimates, comparing this residual to a threshold, and making a decision. The other detection strategy involves a distributed decision process in which estimates from distributed Kalman filters are used to generate distributed residuals which are compared locally to a threshold. Local decisions are made and these decisions are then fused into a global decision. The performances of each of these detection schemes are compared, and it is concluded that better performance is achieved when local decisions are made and then fused into a global decision.
Document ID
19920046684
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Belcastro, Celeste M. (NASA Langley Research Center Hampton, VA, United States)
Fischl, Robert (NASA Langley Research Center Hampton, VA, United States)
Kam, Moshe (Drexel University Philadelphia, PA, United States)