Deep Learning Approach for Fixed and Rotary-Wing Target Detection and Classification in Radars

Recent advancements demonstrate the application of machine learning techniques to radar signal processing, particularly the use of neural networks in one-dimensional (1-D) radar data to assist constant false alarm rate (CFAR) based target detection. However, the efficacy of using neural networks alone on 2-D data for target extraction remains to be explored. This research aims to develop a novel approach of designing a convolution neural network (CNN) based target detection system for real radar data. We propose a dual neural network detection scheme in which the first neural net filters out noisy images. Thereafter, we employ a second CNN-based target detector that extracts information from both radar data axes (fast-time and slow-time), thus utilizing additional information for better detection of targets even with low SNR. Moreover, the same CNN, in conjunction with a region-based convolutional network, exploits micro-Doppler features of rotary-wing aircraft in radar returns and uses them to further classify detected targets as fixed-wing or rotary-wing aircraft. To the best of our knowledge, no generalized methodology exists to address these two issues together. The proposed solution replaces the critical CFAR and peak detection algorithms from the radar signal processing chain. This article also introduces a labeled range-Doppler images dataset obtained from real-world air traffic control radar data. Experimental results demonstrate the superior performance of the proposed technique over 1-D and 2-D CFAR and peak detection algorithms.