Target Detection Method Based on Complex-Valued Deep Neural Network

‍ ‍Traditional radar target detection method performs not good enough in the complex scene which consist of multi sidelobe jamming， non-homogeneous and non-stability clutter with strict probability of detection. The performance need to be further improved. Aiming at this problem， a radar target detection method based on multi-channel complex deep neural network is proposed in this paper. In narrow band target detection area， the constant false alarm rate target detection method of traditional pulse array radar is usually carried out with the channel. During the spatial coherent preprocessing of the echo signals， the coherent accumulation is obtained to rise the signal-noise ratio. In fact， the phase relationship between the target echo in spatial area is certain and it is different from the noise， the clutter， the side lobe jamming， etc. The coherent accumulation uses this relationship to rise the signal-noise ratio， but it may not the best way to use the phase information in spatial area， which means the traditional radar target detection method for narrow band target can be further improved. Deep learning and deep neural network have the powerful fitting ability and classification ability， and target detection can be regarded as a binary hypothesis testing， which can also regard as a binary classification. In this paper， by using the abilities of fitting and classification of neural networks， a complex-valued deep neural network is designed. This neural network can deeply mine the amplitude and phase information differences between target and background in different array elements. Thus， it can distinguish the differences between target and background better in the spatial-range-Doppler space， the more front end of traditional target detection， which means the performance of radar target detection is improved. The simulation results show that the complex-valued deep neural network proposed in this paper has better detection performance and anti-interference ability than the traditional method in the scene with a large number of side lobe interference， and also has good performance in the clutter environment.