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Proceedings Paper

Algorithm for statistical classification of radar clutter into one of several categories
Author(s): Nicholas A. Nechval
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Paper Abstract

In this paper, an algorithm is described which successfully classifies radar clutter into one of several major categories, including bird, weather, and target classes. Statistical non-Bayesian classification of objects is based on the data samples (each sample being drawn from a different class). It is applied to a set of features derived from the reflection coefficients that contain all spectral information about the observed object and are computed using the multi-segment version of Burgts formula. These coefficients are then transformed and grouped to meet the requirements for inultivariate gaussian behaviour. The proposed algorithm is based on a new approach to solving the problem of testing whether a given sample of multivariate observations could have come from a multivariate normal population with an unknown mean vector and dispersion matrix. By using a series of transformations it is shown that the problem of testing for multi-variate normality can be reduced to that of testing for univariate uniformity (U(O,i)). In this case, the problem of classification, that is of assigning an observed object to its proper group, admits a simple solution.

Paper Details

Date Published: 1 August 1991
PDF: 12 pages
Proc. SPIE 1470, Data Structures and Target Classification, (1 August 1991); doi: 10.1117/12.44859
Show Author Affiliations
Nicholas A. Nechval, Civil Aviation Engineers Institute (Latvia)

Published in SPIE Proceedings Vol. 1470:
Data Structures and Target Classification
Vibeke Libby, Editor(s)

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