THE MARGINALIZED LIKELIHOOD RATIO TEST FOR DETECTING ABRUPT CHANGES

The generalized likelihood ratio (GLR) test is a widely used method fo r detecting abrupt changes in linear systems and signals, In this pape r the marginalized likelihood ratio (MLR) test is introduced for elimi nating three shortcomings of GLR while preserving its applicability an d generality, First, the need for a user-chosen threshold is eliminate d in MLR. Second, the noise levels need not be known exactly and may e ven change over time, which means that MLR is robust. Finally, a very efficient exact implementation with linear in time complexity for batc h-wise data processing is developed, This should be compared to the qu adratic in time complexity of the exact GLR.