A recursive generalized likelihood ratio test algorithm for detecting sudden changes in linear, discrete systems

Chang, Chaw-Bing; Dunn, Keh-Ping

doi:10.1109/cdc.1978.268022

Cited by 4 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We formally state the result concerning the characterization of the large deviations exponent upper bounds of the probabilities of the errors pertaining to the distributed detector based on the scalar observation model in context with the proof relegated to Appendix C. 8 We define the following quantities which will be crucial in the statement of the next theorem : let c 4 and c * 4 be constants given by,…”

Section: A Illustrative Examplementioning

confidence: 99%

Recursive Distributed Detection for Composite Hypothesis Testing: Nonlinear Observation Models in Additive Gaussian Noise

Sahu

Kar

2017

IEEE Trans. Inform. Theory

View full text Add to dashboard Cite

This paper studies recursive composite hypothesis testing in a network of sparsely connected agents. The network objective is to test a simple null hypothesis against a composite alternative concerning the state of the field, modeled as a vector of (continuous) unknown parameters determining the parametric family of probability measures induced on the agents' observation spaces under the hypotheses. Specifically, under the alternative hypothesis, each agent sequentially observes an independent and identically distributed time-series consisting of a (nonlinear) function of the true but unknown parameter corrupted by Gaussian noise, whereas, under the null, they obtain noise only. Two distributed recursive generalized likelihood ratio test type algorithms of the consensus+innovations form are proposed, namely CIGLRT − L and CIGLRT − N L, in which the agents estimate the underlying parameter and in parallel also update their test decision statistics by simultaneously processing the latest local sensed information and information obtained from neighboring agents. For CIGLRT − N L, for a broad class of nonlinear observation models and under a global observability condition, algorithm parameters which ensure asymptotically decaying probabilities of errors (probability of miss and probability of false detection) are characterized. For CIGLRT − L, a linear observation model is considered and upper bounds on large deviations decay exponent for the error probabilities are obtained.

show abstract

Section: A Illustrative Examplementioning

confidence: 99%