Topic
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.
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01 Sep 2014
TL;DR: An ordered sequential detection scheme which jointly integrates a reliability-based splitting algorithm, an ordered-transmission strategy, and a sequential probability ratio test (SPRT) which significantly outperforms a conventional SPRT scheme.
Abstract: We consider a distributed detection problem in a large, single-hop, wireless sensor network. Because of limited collection time and bandwidth, the fusion center (FC) is not able to collect the local observations from all sensor nodes. A distributed detection scheme with a selection strategy and a capability to operate in a finite bandwidth is required. We propose an ordered sequential detection scheme which jointly integrates a reliability-based splitting algorithm, an ordered-transmission strategy, and a sequential probability ratio test (SPRT). The proposed scheme allows the FC to collect the local observations in descending order of their reliabilities by using a reliability-based splitting algorithm. As it receives successfully transmitted observations, the FC sequentially decides whether to make a global decision or to continue collecting more local observations. The numerical results show that the proposed scheme significantly outperforms a conventional SPRT scheme. The improvement increases as the number of sensor nodes in the network increases.
01 Nov 2017
TL;DR: This work considers the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility's ventilation off-gas stack, and utilizes the measurements to estimate the intensity at the stack and uses it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor.
Abstract: We consider the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility's ventilation off-gas stack. The intensity of stack emissions decays with distance, and the sensor counts or measurements are inherently random with parameters determined by the intensity at the sensor's location. We utilize the measurements to estimate the intensity at the stack, and use it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor. We demonstrate the superior performance of this method over conventional majority fusers and individual sensors using (i) test measurements from a network of 21 NaI detectors, and (ii) effluence measurements collected at the stack of a reactor facility. We also analytically establish the superior detection performance of the network over individual sensors with fixed and adaptive thresholds by utilizing the Poisson distribution of the counts. We quantify the performance improvements of the network detection over individual sensors using the packing number of the intensity space.
TL;DR: Three extensions of the sequential probability ratio test (SPRT) algorithm (Fu (1968), originally developed as a two-class classifier, to the multi-class classification problem are described.
Posted Content•
TL;DR: In this paper, an energy efficient detector with low detection delay using sequential hypothesis testing (SPRT) was developed for cooperative spectrum sensing for cognitive radios, which is used at both the local nodes and the fusion center.
Abstract: We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.
Proceedings Article•
01 Oct 2008TL;DR: The proposed method is effective in applying the SPRT regardless of the utterance length of the test data because it is a method to generate the new test data through the sample shift of starting frame.
Abstract: In this paper, we propose a method that can generate new test data using the sample shift of starting frame for the sequential probability ratio test (SPRT) in automatic speaker verification (ASV). The SPRT is an effective algorithm that can reduce the computational complexity of test, but itpsilas not suitable method to apply for the short utterance. The proposed method is effective in applying the SPRT regardless of the utterance length of the test data because it is a method to generate the new test data through the sample shift of starting frame. Further, it shows a dramatic result in decision of the threshold process. Experimental results show that the proposed method has 0.5% higher performance than a conventional method in equal error rate (EER), but introduce a little increase to the computational complexity.