Abstract
We address the problem of detecting changes in multivariate Gaussian random signals with an unknown mean after the change. The window-limited generalized-likelihood ratio (GLR) scheme is a well-known approach to solve this problem. However, this algorithm involves at least (log /spl gamma/)//spl rho/ likelihood-ratio computations at each stage, where /spl gamma/(/spl gamma//spl rarr//spl infin/) is the mean time before a false alarm and /spl rho/ is the Kullback-Leibler information. We establish a new suboptimal recursive approach which is based on a collection of L parallel recursive /spl chi//sup 2/ tests instead of the window-limited GLR scheme. This new approach involves only a fixed number L of likelihood-ratio computations at each stage for any combinations of /spl gamma/ and /spl rho/. By choosing an acceptable value of nonoptimality, the designer can easily find a tradeoff between the complexity of the quadratic change detection algorithm and its efficiency.
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