HRR Signature Classification using Syntactic Pattern Recognition

Abstract

An Automatic Target Classification system contains a classifier that maps a vector of real numbered features characteristic to a specific target onto a class label. Other features can be a string of symbols or alphabets that may not involve real numbers at all. There are certain orderings of the symbols in the strings governed by syntax rules, thus, generating a language, (that is, a collection of strings). Thus, a classifier would map a string to a class label. Such a classifier is called a syntactical classifier and varies greatly from its vector space counter part. This paper will give an overview of the construction of a grammar that generates a language then shows how they fit into a syntactical classification system. The performances of two syntactical classification systems with two and ten labels respectively are presented via confusion matrices. Experiments performed on public release DCS database indicate this approach has sufficient power to perform target detection using HRR signatures. 12

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

“…Extracting low-dimensional and high-divisible features that can represent the essential characteristics of targets from the HRRP can not only reduce the dimensionality of highdimensional HRRP data, thus reducing the storage requirements of the algorithm, but can also increase the accuracy and speed of the target recognition algorithm. In addition to the common Fourier transform, bispectrum transform, and other methods, various transformation solutions have been adopted to reduce the dimension of the HRRP to obtain features with good intra-class aggregation and strong inter-class separability [43].…”