Machine Learning for UAV Classification Employing Mechanical Control Information

Abstract

<p>Range-Doppler images are widely used to classify different types of UAVs because each UAV has a unique range-doppler signature. However, a drone's range-doppler signature depends on its movement mechanism. This is why the classifier accuracy would be degraded if the effect of the mechanical control system wasn't taken into consideration, which may lead to a non-unique signature of a drone while in-flight. In this paper, a full-wave electromagnetic CAD tool is used to investigate the effect of the control systems of a quadcopter and a hexacopter UAVs on their range-doppler signatures. A Mechanical Control-Based Machine Learning (MCML) algorithm is introduced to classify the two UAVs and its accuracy is found to exceed 90%.</p>

Comprehensive Review: Effectiveness of MIMO and Beamforming Technologies in Detecting Low RCS UAVs

“…However, the fusion of MIMO and beamforming technologies transcends a simple mathematical equation, as it involves a complex interplay between MIMO's spatial diversity and beamforming's directional gain. This integration leads to a sophisticated radar system capable of superior performance in challenging environments by effectively utilizing the spatial multiplexing of MIMO for enhanced resolution and the focused energy delivery of beamforming for improved UAV detection [74,75]. To conceptualize the fusion of MIMO and beamforming in radar systems, we introduce an integrated equation:…”

Detection and Classification of Drones using Radars, AI, and Full-wave Electromagnetic CAD Tool

“…UAVs have five basic motions, which are hovering, pitching, throttling, rolling, and yawing. To perform each motion, the speeds of a UAV's rotors have to be changed, which affects its Doppler signature [23]. In the state-of-the-art literature, ML models are trained on datasets that contain a UAV's hovering and pitching motions only, and they are tested on the same datasets [36,[40][41][42][43][44][45][46][47][48][49][50].…”

“…In the state-of-the-art literature, ML models are trained on datasets that contain a UAV's hovering and pitching motions only, and they are tested on the same datasets [36,[40][41][42][43][44][45][46][47][48][49][50]. This leads to a degradation on ML accuracy if these trained models were tested using different datasets that contain other motions [23]. As radar signature of UAVs is highly dependent on the mechanical control information of them [23].…”

“…In addition, these datasets are limited to the used radars, UAV types, and the location and surroundings in which these measurements are made [20]. In this chapter, we propose to use a full-wave EM CAD tool, such as Ansys high-frequency structure simulator (HFSS) [21], to generate radar datasets the contain UAV information for the purpose of training ML algorithms [22][23][24][25]. Traditionally, full-wave EM CAD tools are used for designing and simulating high-frequency stationary electronic products.…”

“…Traditionally, full-wave EM CAD tools are used for designing and simulating high-frequency stationary electronic products. The proposed method using Ansys HFSS SBR+ solver [21][22][23][24][25] can be used to move drones and perform time-based full-wave analysis. For example, Figure 1a-c illustrate the DJI S900 hexacopter UAV, Ansys HFSS model of this hexacopter, and three-time stamps showing the rotation of its blades in Ansys HFSS, respectively.…”