A Long-Term Recurrent Convolutional Network for SNR Estimation of Cone-Shaped Target

Signal-to-noise ratio (SNR) is an important prior information for many micro-motion echo processing approaches and techniques. Estimating SNR in advance can effectively enhance the performance of such technologies. This letter proposes a long-term recurrent convolutional network (LRCN)-based SNR estimation method for cone-shaped target. First, mathematical expression of the echo is derived by studying the cone-shaped target micro-motion model, and hence the characteristics of the echo and its short-time fourier transform are analyzed. Second, the motivation for the proposed are analyzed, and the echo is converted as an time-frequency graph with short-time Fourier transform (STFT), so that an estimation technique based on LRCN (a hybrid of convolutional neural network and recurrent neural networks) is designed to estimate the SNR. Experiments indicate that the proposed method outperforms other already existing methods with better accuracy and robustness.