A Weak Signal Detection Method Based on Spatial Spectrum-LSTM Neural Network
Yaning Dong, Chuanzhang Wu, Huizhu Zhu, Feng Xu, Xin Ren · 2022
In this paper, we propose a weak signal detection method based on spatial spectrum-long short-term memory (LSTM) neural network to address the problem that the traditional blind detection method of weak signals is not effective in the condition of low signal-to-noise ratios. We firstly exploit the difference between the spatial spectrum transformed signal and noise to determine whether there is a weak signal. Then, the LSTM neural network is used for feature learning to classify different samples. It can avoid the influence of the detection threshold on the detection performance of the system. Numerical results show that the detection performance of our method outperforms LSTM neural network, radial basis function neural network, traditional maximum-minimum eigenvalue, and energy detection methods.