Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

In recent years, Internet of Things technologies gained momentum in various application areas, including the Smart Home field. In this view, the smart objects available in the house can communicate with each other and with the outside world by adopting solutions already proposed for Internet of Things. In fact, among the challenges to face during the design and implementation of an Internet of Things-based Smart Home infrastructure, battery usage represents a key point for the realization of an efficient solution. In this context, the communication technology chosen plays a fundamental role, since transmission is generally the most energy demanding task, and Internet of Things communication technologies are designed to reduce as much as possible the power consumption. This article describes an Internet of Thingsoriented architecture for the Smart Home, based on the long-range and low-power technology LoRa. Moreover, in order to enable the devices to communicate with each other and the outside world, the Message Queue Telemetry Transfer protocol is used as a domotic middleware. We show that LoRa, designed by having in mind the typical requirements of Internet of Things (i.e. low power consumption, sporadic transmission, and robustness to interference), is well-suited to also meet the need of more established home automation systems, specifically the low latency in message delivery. Interoperability among different devices may also be obtained through the Message Queue Telemetry Transfer midlleware.

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The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers’ rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers’ speed to evaluate the potential benefits of the proposed approach.

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