###### Abstract  
In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843 ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively. mmWave radar; cloud points; target tracking; target recognition +
Footnote †: journal: Applied system  
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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].