Sequential probability ratio test

About: Sequential probability ratio test is a research topic. Over the lifetime, 1248 publications have been published within this topic receiving 22355 citations.

Tracking-Aided Classification of Targets Using Multihypothesis Sequential Probability Ratio Test

Abstract: This paper deals with target classification by using both feature data and kinematic measurements. The problem is tackled by multihypothesis sequential testing with embedded target tracking. We implement an Armitage sequential test for nonmaneuvering and maneuvering targets. Both (centralized and distributed) fusion architectures are used for the embedded tracking. The contributions of the kinematic measurements to classification are analyzed, and classification performance improvement is shown analytically for a special case. Numerical results are provided to demonstrate the performance of our algorithms.

Order-2 Asymptotic Optimality of the Fully Distributed Sequential Hypothesis Test.

Abstract: This work analyzes the asymptotic performances of fully distributed sequential hypothesis testing procedures as the type-I and type-II error rates approach zero, in the context of a sensor network without a fusion center. In particular, the sensor network is defined by an undirected graph, where each sensor can observe samples over time, access the information from the adjacent sensors, and perform the sequential test based on its own decision statistic. Different from most literature, the sampling process and the information exchange process in our framework take place simultaneously (or, at least in comparable time-scales), thus cannot be decoupled from one another. Two message-passing schemes are considered, based on which the distributed sequential probability ratio test (DSPRT) is carried out respectively. The first scheme features the dissemination of the raw samples. Although the sample propagation based DSPRT is shown to yield the asymptotically optimal performance at each sensor, it incurs excessive inter-sensor communication overhead due to the exchange of raw samples with index information. The second scheme adopts the consensus algorithm, where the local decision statistic is exchanged between sensors instead of the raw samples, thus significantly lowering the communication requirement compared to the first scheme. In particular, the decision statistic for DSPRT at each sensor is updated by the weighted average of the decision statistics in the neighbourhood at every message-passing step. We show that, under certain regularity conditions, the consensus algorithm based DSPRT also yields the order-2 asymptotically optimal performance at all sensors.

SEQUENTIAL ANALYSIS OF ENVIRONMENTAL MONITORING DATA: OPTIMAL SPRTs

Abstract: Data provided by an environmental monitoring system are sampled successively. We propose to analyse such data by means of the sequential probability ratio test (SPRT) which is especially designed to analyse data which are sampled consecutively. We present a method, the minimax method, that can be used to select an SPRT which is optimal in testing the null hypothesis θ = θ0 against the composite alternative hypothesis θ ≠ θ0 for three monitoring systems, namely a system consisting of one sampling location with known mean and variance, a system consisting of one sampling location with unknown mean and variance and a system consisting of two sampling locations with unknown mean and covariance matrix. The latter test is applied to field data of the mallard. © 1997 by John Wiley & Sons, Ltd.