A suboptimal quadratic change detection scheme

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 23 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.