Recurrent Neural Networks for Radar Target Identification

摘要

A real-time recurrent learning algorithm was applied to a five class radar targetidentification problem. The wideband radar was assumed to measure both kinematic (tracking information expressed as estimated aspect angles) and high range resolution data from a single, isolated aircraft. The aspect angles (azimuth and elevation) of the aircraft relative to the radar were assumed to be constantly changing. This created temporal sequences of high range resolution radar signatures that changed as the aspect angles changed. These sequences were used as input features to a recurrent neural network for three radar target identification test cases. The first test case demonstrated the feasibility of using recurrent neural networks for radar target identification. The second test case demonstrated the relationship between sequence length and target recognition accuracy. For the third test case, the recurrent net achieved 96% test set accuracy under the following conditions: 5 aircraft classes, azimuth range between 60 deg and 90 deg, elevation range between +5 deg and -5 deg, 1 deg signature granularity, and signatures corrupted by 5 dBsm scintillation noise.... Neural networks, Recurrent neural networks, Real-time recurrent learning algorithm, Radar target identification, Wideband radar, High range resolution radar, Temporal sequences, Sequence analysis.