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Target Discrimination with Neural Networks

dc.contributor.authorLin, Daw-Tungen_US
dc.contributor.authorDayhoff, Judith E.en_US
dc.contributor.authorResch, C.L.en_US
dc.description.abstractThe feasibility of distinguishing multiple type components of exo-atmospheric targets is demonstrated by applying the Time Delay Neural Network (TDNN) and the Adaptive Time-Delay Neural Network (ATNN). Exo-atmospheric targets are especially difficult to distinguish using currently available techniques because all target parts follow the same spatial trajectory. Thus classification must be based on light sensors that record 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.en_US
dc.format.extent1050804 bytes
dc.relation.ispartofseriesISR; TR 1995-54en_US
dc.subjectartificial intelligenceen_US
dc.subjectneural networksen_US
dc.subjectdistributed information processingen_US
dc.subjectimage processingen_US
dc.subjectneural systemsen_US
dc.subjectrobust information processingen_US
dc.subjectsignal processingen_US
dc.subjectIntelligent Control Systemsen_US
dc.titleTarget Discrimination with Neural Networksen_US
dc.typeTechnical Reporten_US

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