Anti-Corner Reflector Array Method Based on Pauli Polarization Decomposition and BP Neural Network

Abstract

The radar echoes of the corner reflector array and the ship target are very similar, and the existing algorithms are difficult to identify them effectively in time, frequency and spatial domain. Aiming at the problem that the terminal guidance radar of anti-ship missile can’t detect and track the real target effectively under the deception jamming of corner reflector array, this paper designs a countermeasure method based on Pauli polarization decomposition and BP neural network. Firstly, the Pauli polarization decomposition of the full polarization scattering matrix of the target measured in the fixed angle window is used to obtain four normalized coefficients and form the eigenvector, and the differences between the ship target and the corner reflector are analyzed. Then, the BP neural network model is trained and optimized as the training sample. The simulation and test results show that the feature vectors can distinguish the two kinds of targets, and the trained network can identify the ship and the corner reflector Array effectively, and the overall success rate is close to 97%.