Adaptive Block Sequential Detection of Abrupt Signal Changes

摘要

Estimation of the time of change of a parameter governing an observed sequence ofindependent multivariate random data samples is considered when the magnitude of the change is unknown and there exist unknown nuisance parameters that may be nonstationary. The particular application considered is the detection of the onset of a narrowband signal with unknown amplitude and phase at an array of sensors for radar or sonar processing in the presence of unknown, nonstationary, spatially distinct interferences hindering detection. The solution proposed in this dissertation entails segmenting the multivariate data into nonoverlapping blocks, from which univariate statistics are formed that are invariant to the unknown interference parameters. The log likelihood ratio for a specific signal-to-interference ratio (SIR) and the locally optimal nonlinearities are applied to two level statistics formed by generalized likelihood ratio (GLR) methods for data compression prior to submission to Page's test for rapid detection of the change. Once Page's test has determined that a change has occurred at the block level, the estimate of the change time is improved by post block processing in the form of a maximum likelihood estimator for the change time. Application of the locally optimal nonlinearity to the block level statistic formed from a GLR for the unknown signal and interference parameters achieved the best asymptotic performance. (MM).