In recent years, unmanned aerial vehicles (UAVs), such as drones, have received significant attention for performing tasks in different domains. This is because of their low cost, high coverage, and vast mobility, as well as their capability to perform different operations using small-scale sensors [1]. Smartphones can now operate drones instead of traditional remote controllers, owing to technological advancements. In addition, drone technology can provide live video streaming and image capturing, as well as make autonomous decisions based on these data. Consequently, artificial intelligence techniques have been utilized in the provisioning of civilian and military services [2]. In this context, drones have been adopted for express shipping and delivery [3,4,5], natural disaster prevention [6,7], geographical mapping [8], search and rescue operations [9], aerial photography for journalism and film making [10], providing essential materials [11], border control surveillance [12], and building safety inspection [13]. Even though drone technology offers a multitude of benefits, it raises mixed concerns when it comes to how it will be used in the future. Drones pose many potential threats, including invasion of privacy, smuggling, espionage, flight disruption, human injury, and terrorist attacks. These threats compromise aviation operations and public safety. However, it has become increasingly necessary to detect, track, and recognize drone targets and make decisions in certain situations, such as detonating or jamming unwanted drone targets.

The detection of unwanted drones poses significant challenges to observation systems, especially in urban areas, as drones are tiny and move at different rates and heights compared to other moving targets [2]. For target recognition, optic-based systems that rely on cameras provide more detailed information than radio-frequency (RF)-based systems, but these require a clear frontal view, as well as ideal light and weather conditions [14,15], as shown in Figure 1A. Both residential and business environments are less accepting of the use of cameras for target recognition because of their intrusive nature [16]. Although RF-based systems are less intrusive, the signals received from RF devices are not as expressive or intuitive as those received from images. Humans are often unable to directly interpret RF signals. Thus, preprocessing RF signals is a challenging process that requires the translation of raw data into intuitive information for target recognition. It has been proven that RF-based systems such as WiFi, ultrasound sensors, and millimeter-wave (mmWave) radar can be useful for a variety of observation applications that are not affected by light or weather conditions [17]. WiFi signals require a delicate transmitter and receiver and are limited to situations where targets must move between the transmitter and receiver [18]. Because ultrasound signals are short-range, they are usually used to detect close targets and are affected by blocking or interference from other nearby transmitters [14].

The large bandwidth of mmWave allows a high distance-independent resolution, which not only facilitates the detection and tracking of moving targets, but also their recognition [18]. Furthermore, mmWave radar requires at least two antennas for transmitting and receiving signals; thus, the collected signals can be used in multiple observation operations [18]. Rather than true color image representation, mmWave signals can represent multiple targets using reflected three-dimensional (3D) cloud points, micro-Doppler signatures, RF-intensity-based spatial heat maps, or range-Doppler localizations [19].

mmWave-based systems frequently use convolutional neural networks (CNNs) to extract representative features from micro-Doppler signatures to recognize objects [18,20,21]; however, examining micro-Doppler signals is computationally complex because they deal with images, and they only distinguish moving targets based on translational motion. Employing a CNN to extract representative features from cloud points is becoming the tool of choice for developing the mathematical modeling underlying dynamic environments and leveraging spatiotemporal information processed from range, velocity, and angle information, thereby improving robustness, reliability, and detection accuracy and reducing computing complexity to achieve the simultaneous performance of mmWave radar operations [22].

2. Millimeter-Wave Radar and Convolutional Neural Network