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Use of Time Varying Dynamics in Neural Network to Solve Multi-Target Classification
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- text
- 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
- Aerospace Engineering
- Mechanical Engineering