# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### RF-Based  
RF is used for UAV remote command and control communication. RF-based detection technologies rely on real-time sensing, capturing, processing, analyzing, and retrieving data from UAV's RF-emitted signals. Acquired RF data are intended to identify, track and classify the detected UAV and localize the controller. RF-based techniques analyze the captured spectrum between the UAV and operators using circular or linear array antennas to detect both the drone and its controller in all-weather environments. As most of the communication between a drone and its controller occurs in the ISM band, around 2.4 GHz, the implementation cost of such a system is much lower compared to a radar-based solution [21; 22; 23; 24; 25; 26; 27; 28; 51]. In RF-based detection technologies, RF and WiFi-based fingerprinting techniques are major verification systems. RF-based techniques include studying and analyzing the characteristics of the captured transmitted RF signal from UAVs or UAVs' controllers. However, WiFi-based fingerprinting is related to the WiFi links and traffic between the UAV and its remote controller. The reviewed studies include the analysis of RF spectrogram (fingerprinting) [7; 9; 10; 19], angle of arrival (AOA) (MUSIC) [12], and direction of arrival (DOA) [40] methods for the identification and localization of drones using conventional as well machine learning algorithms [9; 10; 13; 14; 15]. In [7], the technique proposes a complete UAV detection and identification system framework designed to work in the 2.4 GHz frequency band. The system starts with capturing the wireless signals in the test area. Then, the captured signal is processed based on a 4-level Haar wavelet transform analysis. The standard deviation of the processed signal is calculated to define the UAV detection condition. After the detection of the UAV, the RF fingerprinting stage is activated, and three main features are extracted: (1) fractal dimension (FD), (2) square integrated bispectra (SIB), and (3) axially integrated bispectra (AIB). These features are adjusted and weighted using principal component analysis (PCA) and neighborhood component analysis (NCA) algorithms. The final RF fingerprintsare stored as the training data for a set of machine learning algorithms used to classify the UAV. Based on indoor and outdoor experimental scenarios, the average identification accuracy of UAVs is summarized with respect to three fingerprinting features. Furthermore, in [8], the WiFi network traffic is monitored, and the UAV detection method is based on WiFi fingerprint analysis. The extracted features are related to the captured traffic's duration, behavior, and distribution. Different scenarios are applied to evaluate the system's performance in UAV detection, where the average precision is about 96%. The authors of [15; 19; 20] fed the extracted time-domain characteristics (shape factor, skewness, kurtosis, and variance) of recorded RF signals to the machine learning data processing units to detect and classify UAVs. In [9], indoor experimental testing is conducted for data collection using the RF fingerprints of the transmitted signal from the micro-UAV controller to the UAV for UAV detection and classification. Different micro-UAV controllers (a total of 14) operating at the 2.4 GHz frequency band were used to create the dataset (a total of 100 RF signals) and test the proposed detection and classification technique. Each micro-UAV controller has a different transmitted signal, categorized with its unique transmitter characteristics, excluding the traditional threshold-based detection technique. The Markov model algorithm is later used for UAV detection and energy transient signal approach for feature extraction and UAV classification. The performance and accuracy of the system were found to be 96.3%. UAVs use the Industrial Scientific and Medical (ISM) frequency bands, i.e., 2.4 GHz and 5.8 GHz bands, to communicate with their remote controllers [11].