Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.

The use of through‐wall radar imagery for remote intelligence of building interiors is promising but challenging. Relevant information about human targets and room layout features is typically buried in clutter. In this Letter, the authors propose a methodology for automated extraction of information about stationary human targets behind walls. Based on treatment of the individual blobs found in the imagery, the method consists of thresholding, segmentation, classification and 3D visualisation. Although further optimisation of each of the steps is required, they demonstrate with two examples, including one cluttered scene, that the combination of these steps is effective at eliminating large amounts of clutter and identifying human targets behind walls.