Abstract

Several types of solutions exist for multiple target tracking. These techniques are computation-intensive and in some cases very difficult to operate online. The authors report on a backpropagation neural network which has been successfully used to identify multiple moving targets using kinematic data (time, range, range-rate and azimuth angle) from sensors to train the network. Preliminary results from simulated scenarios show that neural networks are capable of learning target identification for three targets during the time period used during training and a time period shortly after. This effective classification period can be extended by the use of networks in coordination with smart logic systems.

Meeting Name

IEEE 1992 National Aerospace and Electronics Conference, 1992

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Azimuth Angle; Backpropagation; Kinematic Data; Learning; Multi-Target Classification; Multiple Moving Targets; Neural Nets; Neural Network; Numerical Analysis; Pattern Recognition; Range-Rate; Sensor Fusion; Simulation; Smart Logic Systems; Target Identification; Time; Time Varying Dynamics; Time-Varying Systems; Tracking

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1992 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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