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Proceedings Paper

Minimum-sample SPRT for sensor or operating point selection in a single- or multisensor environment
Author(s): Robert J. Pawlak
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Paper Abstract

This paper describes a technique for employing the sequential probability ratio test (SPRT) in a single or multisensor environment. The technique minimizes the number of sensor decisions required to declare the null or alternative hypothesis when there is a choice of different sensors or sensor operating points. Thus the technique will be dubbed the Minimum-Sample SPRT (MS-SPRT). The first step of the MS-SPRT requires an off-line optimization of the choice of sensors across all possible values of the alternative hypothesis probability. The second step of the technique involves the application of two Kalman filters to estimate the probability of the alternative hypothesis and to optimize a set of sensor probabilities. The sensor probabilities determine the optimal sensor choice that minimizes the expected number of samples before a decision is made. Three examples are given using simulated data. In the first example, it is shown that the MS-SPRT is not necessary. The second example shows the usefulness of the MS-SPRT when there is a step discontinuity in the null/alternative hypothesis probabilities. In the third example, the MS-SPRT facilitates the use of the proper sensor for a probabilistic variation in the hypothesis probabilities.

Paper Details

Date Published: 20 March 1998
PDF: 10 pages
Proc. SPIE 3376, Sensor Fusion: Architectures, Algorithms, and Applications II, (20 March 1998); doi: 10.1117/12.303674
Show Author Affiliations
Robert J. Pawlak, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 3376:
Sensor Fusion: Architectures, Algorithms, and Applications II
Belur V. Dasarathy, Editor(s)

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