For the effective deployment of countermeasures against drones, information on their intent is crucial. There are several indicators for a drone's intent, e.g., its size, payload, and behavior. Within the current study, the focus was on estimating subsets of the following four indicators: a drone's wing type, its number of rotors, the presence of a payload and its mean rotor rotation rate. Three Multitask Learning (MTL) approaches were analyzed for the simultaneous estimation of subsets of these indicators based on radar micro-Doppler spectrograms. MTL refers to training neural networks simultaneously for multiple related tasks. The assumption is that if tasks share features between them, an MTL model is easier to train and has improved generalization capabilities as compared to separately trained single-task neural networks. The results of this initial study show that MTL provides overall better performance than the single-task learning approach, given the available data set of measured drone spectrograms.

This paper investigates the benefits of multichannel processing for inverse synthetic aperture radar (ISAR) imaging. There are potential advantages of multichannel processing in radar systems, for example, improved motion estimation, clutter suppression, extraction of supporting information, such as sea wave direction and speed, and ship wake imaging. The current study focuses on two multichannel techniques, namely the oceanic displaced phase centre antenna (ODPCA) and the minimum variance distortionless response (MVDR) techniques, to suppress (ambiguous) clutter and enhance focused target responses even in the proximity of other target responses. These techniques are validated using experimental multichannel radar data from an airborne measurement campaign in a maritime environment.