Distributed recursive testing of composite hypothesis in multi-agent networks

Abstract

This chapter considers the problem of recursive composite hypothesis testing in a network of sparsely connected agents. In classical centralized composite hypothesis testing, procedures such as the generalized likelihood ratio test (GLRT), i.e., the detection procedure which uses the underlying parameter estimate based on all the collected samples as a plug-in estimate, may exhibit poor performance until a reasonably accurate parameter estimate (typically the maximum likelihood estimate of the underlying parameter/state) is obtained. Usually in setups that employ the classical (centralized) GLRTs, the data-collection phase precedes the parameter estimation and detection statistic update phase, thus rendering the testing an essentially offline batch procedure. The motivation behind studying distributed recursive online detection algorithms in contrast to offline batch processing based detection algorithms is that in most multi-agent networked scenarios, which are typically energy constrained, the priority is to obtain reasonable inference performance by expending fewer amount of resources.