Accession Number : ADA333408
Title : Feasibility Studies of Nearest Neighbor Residual Vector Quantizer Classifiers for a Collection of Signal and Sensor Waveforms: Automatic Target Recognition in SAR Images
Descriptive Note : Interim rept. 1 Jan 97-1 Jan 98
Corporate Author : GEORGIA TECH RESEARCH INST ATLANTA
Personal Author(s) : Barnes, Christopher F. ; Keel, Byron M.
PDF Url : ADA333408
Report Date : 01 JAN 1998
Pagination or Media Count : 80
Abstract : This executive summary contains a concise overview of the grant purpose, problem statement and proposed solution, the research objective, and the technical approach used to achieve this objective. Experimental setups, performance results, and conclusions are also summarized. The purpose of this ONE grant is to support the evaluation of the performance of a particular joint compression/classification algorithm called nearest neighbor residual vector quantizer (NN-RVQ) classification on data obtained from a variety of sensor types and for a variety of applications. NN-RVQ is based on a recent mathematical development called direct sum successive approximations (DSSA). DSSA can be used as a technical foundation for data compression or pattern recognition algorithms, or for a single algorithm that does both. DSSA uses an unconventional mathematical data analysis/synthesis process to construct structured pattern dictionaries that can be efficiently searched (in terms of computation and memory). These patterns can be used as codevectors in vector quantizers (VQs) used for data compression, and as templates in nearest neighbor classifiers used for data classification. The purpose of this grant is to assess the performance of NN-RVQs when they are used for classification, compression, or joint classification and compression of various types of sensor data.
Descriptors : *TARGET RECOGNITION, *SYNTHETIC APERTURE RADAR, ALGORITHMS, COMPUTATIONS, DETECTORS, DATA MANAGEMENT, WAVEFORMS, FEASIBILITY STUDIES, APPROXIMATION(MATHEMATICS), SIGNALS, TEMPLATES, VECTOR ANALYSIS, QUANTIZATION, PATTERN RECOGNITION, DATA COMPRESSION.
Subject Categories : Active & Passive Radar Detection & Equipment
Distribution Statement : APPROVED FOR PUBLIC RELEASE