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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Journal Article•
TL;DR: In this paper, a sequential probability ratio test (SPRT) framework for vibration-based Structural Health Monitoring (SHM) is proposed, which employs the residual sequences obtained using a single stochastic time series model of the healthy structure.
Abstract: The goal of this study is the introduction and experimental assessment of a Sequential Probability Ratio Test (SPRT) framework for vibration based Structural Health Monitoring (SHM). This employs the residual sequences obtained using a single stochastic time series model of the healthy structure and is based on a combination of binary and multihypothesis versions of the SPRT. The framework’s performance is predetermined via the use of the Operating Characteristic (OC) and Average Sample Number (ASN) functions in combination with baseline experiments, while it requires on average a minimum number of samples in order to reach a decision compared to Fixed Sample Size (FSS) most powerful tests. The effectiveness of the proposed approach is validated and experimentally assessed via its application to a lightweight aluminum truss structure.
25 May 2016
TL;DR: A new algorithm for the sequential track formation of a radar target is suggested that combines calculations of the two LLR contributions, the kinematic and the signal one taking into account existence of false measurements.
Abstract: A new algorithm for the sequential track formation of a radar target is suggested. The algorithm is implemented and tested for both mono- and multistatic radar designs. The proposed algorithm combines calculations of the two LLR contributions, the kinematic and the signal one taking into account existence of false measurements. Moreover the algorithm considers not only one but any possible number of false measurements. Besides, unlike both above algorithms, the gating probability is additionally taken into consideration.
19 Dec 1979
TL;DR: The classifier implements a Generalized Sequential Probability Ratio Test (GSPRT) which allows for multiple classes of target signatures and forms of the classifier for both noncoherent and coherent signatures are described.
Abstract: : A linear predictive form of classifier is described for the sequential discrimination of target signatures. The classifier implements a Generalized Sequential Probability Ratio Test (GSPRT) which allows for multiple classes of target signatures. Forms of the classifier for both noncoherent and coherent signatures are described. Flow charts are included for convenience of implementation.
Patent•
02 Oct 2018
TL;DR: In this paper, a fault detection method for a redundant sensor based on an ICA-SPRT (Independent Component Analysis-Sequential Probability Ratio Test), which comprises an independent component analysis module, a signal identification and estimation module and a fault state statistical detection module, is presented.
Abstract: The invention discloses a fault detection method for a redundant sensor based on an ICA-SPRT (Independent Component Analysis-Sequential Probability Ratio Test), which comprises an ICA-SPRT fault detection system, wherein the ICA-SPRT fault detection system comprises a signal independent component analysis module, a signal identification and estimation module and a fault state statistical detectionmodule; the signal independent component analysis module separates independent signals by adopting an ICA algorithm, and finds the independent signal corresponding to an original signal for restoration through the signal identification and estimation module; the fault state statistical detection module obtains fault judgment through an SPRT algorithm. The fault detection method provided by the invention for the redundant sensor based on the ICA-SPRT can effectively eliminate influences of system noise and channel noise, and still can realize accurate fault detection under the condition of lowredundancy.