Multitask Learning Approaches Towards Drone Characterisation With Radar
Apostolos Pappas (Microwave Sensing, Signals & Systems)
Jacco J.M. de Wit (TNO)
Francesco Fioranelli (Microwave Sensing, Signals & Systems)
Bas Jacobs (TNO)
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Abstract
For the effective deployment of countermeasures against drones, information on their intent is crucial. There are several indicators for a drone's intent, for example, its size, payload and behaviour. In this paper, a method is proposed to estimate two or more 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. Specifically, three multitask learning (MTL) approaches are analysed for the simultaneous estimation of several 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 generalisation capabilities as compared to separately trained single-task neural networks. The proposed MTL approaches are validated with experimental data and in a variety of combined classification and regression tasks. The results show that MTL approaches can provide improvement in several tasks compared with conventional approaches.
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