Decentralized Multihypothesis Sequential Detection

TL;DR

This paper develops asymptotically optimal decentralized sequential tests for multiple hypotheses in sensor networks, combining tandem, unambiguous likelihood, and randomized quantizers to improve decision accuracy.

Contribution

It introduces a novel approach that integrates three existing quantization methodologies for multihypothesis detection in decentralized sensor networks.

Findings

Asymptotically Bayes optimal tests are derived for multihypothesis detection.

The proposed method effectively utilizes limited sensor communication.

The approach improves detection performance over existing methods.

Abstract

This article is concerned with decentralized sequential testing of multiple hypotheses. In a sensor network system with limited local memory, raw observations are observed at the local sensors, and quantized into binary sensor messages that are sent to a fusion center, which makes a final decision. It is assumed that the raw sensor observations are distributed according to a set of M>=2 specified distributions, and the fusion center has to utilize quantized sensor messages to decide which one is the true distribution. Asymptotically Bayes tests are offered for decentralized multihypothesis sequential detection by combining three existing methodologies together: tandem quantizers, unambiguous likelihood quantizers, and randomized quantizers.