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.
S. N. Balakrishnan and J. Rainwater, "Use of Time Varying Dynamics in Neural Network to Solve Multi-Target Classification," Proceedings of the IEEE 1992 National Aerospace and Electronics Conference, 1992, Institute of Electrical and Electronics Engineers (IEEE), Jan 1992.
The definitive version is available at http://dx.doi.org/10.1109/NAECON.1992.220538
IEEE 1992 National Aerospace and Electronics Conference, 1992
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
Article - Conference proceedings
© 1992 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.