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This paper considers the sequential composite hypothesis test with multiple sensors. The sensors observe random samples in parallel and communicate with a fusion center, who makes the global decision based on the sensor inputs. On the one hand, in the centralized scenario, where local samples are precisely transmitted to the fusion center, the generalized sequential likelihood ratio test (GSPRT) is...
This work considers the sequential hypothesis testing problem in the fully distributed sensor network. In specific, each sensor can observe samples over time, exchange information with adjacent sensors, and perform testing based on its own locally available decision statistic. Under such setting, we study the sequential probability ratio test based on the statistic that is obtained by running consensus...
This paper investigates the generalized sequential probability ratio test (GSPRT) with multiple sensors. Focusing on the communication-constrained scenario, where sensors transmit one-bit messages to the fusion center, we propose a decentralized GSRPT based on level-triggered sampling scheme (LTS-GSPRT). The proposed LTS-GSPRT amounts to the algorithm where each sensor successively reports the decisions...
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