Classification of air targets based on range-Doppler diagrams

In urban and littoral environments, a wide variety of moving objects such as vehicles, birds, ultralights, drones, and small aircraft may lead to a large number of closely spaced radar detections that can cause severe problems in the tracking process such as false tracks, merged tracks and track loss. By extracting non-kinematic features such as micro-Doppler signatures or 1-D or 2-D spatial target images from the radar signal, the radar detections can be classified in different target classes to mitigate the problems in the tracking process and improve situation awareness. However, micro-Doppler signature analysis and high resolution imaging techniques need a long time-on-target that may be incompatible with the large number of targets in urban or littoral environments. Furthermore, micro-Doppler features are not always observable and target images can be distorted by moving parts and multipath on the target body. In this paper, range-Doppler imaging for air target classification is assessed.