Improved operation of m-interval detectors by optimum signal selection
- 1 July 1978
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 24 (4) , 477-484
- https://doi.org/10.1109/tit.1978.1055908
Abstract
The theory of them-interval partition detector is extended to include the analysis and design of nonconstant signals. Using the derived locally most powerful scores, the performance of this detector is investigated. A discrete formulation is used to select the signal and correlation function of the detector to optimize a performance index that reflects the system constraints. Though the detector appears to be similar to a conventional correlation detector, it retains the robustness properties of them-interval detector. In addition, a method is presented whereby dependent samples may be used while still retaining nonparametric operation of the detector.Keywords
This publication has 10 references indexed in Scilit:
- Bivariate m-interval classifiers with application to edge detectionInformation and Control, 1977
- Robustized vector Robbins-Monro algorithm with applications to m-interval detectionInformation Sciences, 1976
- A class of nonparametric detectors for dependent input dataIEEE Transactions on Information Theory, 1975
- A class of discrete signal-design problems in burst noiseIEEE Transactions on Information Theory, 1972
- Nonparametric detectors based on m-interval partitioningIEEE Transactions on Information Theory, 1972
- Nonparametric detection using dependent samples (Corresp.)IEEE Transactions on Information Theory, 1970
- Theory of a Class of Discrete Optimal Control Systems†Journal of Electronics and Control, 1964
- On the asmptotic efficiency of locally optimum detectorsIEEE Transactions on Information Theory, 1961
- The Gradient Projection Method for Nonlinear Programming. Part I. Linear ConstraintsJournal of the Society for Industrial and Applied Mathematics, 1960
- On a Theorem of PitmanThe Annals of Mathematical Statistics, 1955