Accession Number : ADA319959

Title :   Target Discrimination with Neural Networks,

Corporate Author : MARYLAND UNIV COLLEGE PARK

Personal Author(s) : Lin, Daw-Tung ; Dayhoff, Judith ; Resh, Cheryl

PDF Url : ADA319959

Report Date : SEP 1996

Pagination or Media Count : 12

Abstract : The feasibility of discriminating the warhead from an intentionally segmented exoatmospheric threat missile is demonstrated by applying the time delay neural network (TDNN) and the adaptive time delay neural network (ATNN). Exoatmospheric threats are especially difficult to distinguish using currently available techniques because all threat segments follow the same trajectory. Thus, classification must be done using infrared sensors that record the signal over time. Results have demonstrated that the trained neural networks were able to successfully identify warheads from other missile parts on a variety of simulated scenarios, including differing angles and tumbling. The network with adaptive time delays (the ATNN) performs highly complex mapping on a limited set of training data and achieves better generalization to overall trends of situations compared to the TDNN, which includes time delays but adapts only its weights. The ATNN was trained on additive noisy data, and it is shown that the ATNN possesses robustness to environment variations.

Descriptors :   *NEURAL NETS, *TARGET DISCRIMINATION, *INFRARED DETECTORS, *GUIDED MISSILE DETECTION, SCENARIOS, SIMULATION, TIME INTERVALS, GUIDED MISSILE TRAJECTORIES, GUIDED MISSILE WARHEADS, TARGET SIGNATURES, TRAINING, THREATS, FEASIBILITY STUDIES, SIGNALS, ADAPTIVE SYSTEMS, DELAY, PATTERNS, MAPPING, EXOSPHERE, SEGMENTED.

Subject Categories : Cybernetics
      Antimissile Defense Systems
      Infrared Detection and Detectors

Distribution Statement : APPROVED FOR PUBLIC RELEASE