Analysis of the Information Content of Recognition Features of Small-Sized Unmanned Aerial Vehicles Based on Real S-Band Pulse Radar Recordings

An approach to analyzing classification features for radar recognition of small unmanned aerial vehic­ les based on spectral radar portraits generated from real S-band pulsed radar recordings is developed and stu­ died. The problem of extracting and assessing the information content of signal features available for practical implementation in detection and tracking radars is considered. Experimental data were processed, and a set of physically interpretable features is generated, reflecting the properties of the airframe component and secondary micro-Doppler modulation of the reflected signal. To quantitatively evaluate the separating ability of features, the Fisher criterion, the amount of information and normalized interclass distances were used. It is established that micro-Doppler features possess the greatest information content, ensuring stable separation of multicopter and hybrid types of unmanned aerial vehicles.