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.

The size of the features, and their relative distance to the probe, vary a lot in the intracardiac echocardiography application thus challenging the design of the probe. Therefore it may be beneficial to design a versatile probe which can produce both a large image to provide overview for navigation, and a smaller but detailed anatomic image on the structures of interest. This could be achieved by a probe whose frequency range of operation can be tuned - on the fly - to the specific task. Our goal is to develop a forward-looking catheter which can change its imaging frequency in the range 5 MHz - 15 MHz, allowing for both high penetration and high resolution intracardiac imaging within a single device. Our design comprises a capacitive micromachined ultrasonic transducer (CMUT) array operated in collapse-mode, which allows tuning of the imaging frequency. Custom-made front-end electronics is integrated in a catheter tip close to the CMUT for improved performance. In this paper, we report on the frequency-agility of the fabricated collapse-mode 1-D CMUT array.