# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Acoustic
The study characterizes the emitted noise of UAVs of different levels in an anechoic chamber at the airborne time. The microphone array was arranged within two circular tiers, each 1-m in radius, and separated vertically by 1.6 m to collect data from the local airports. The generalized cross-correlation (GCC)-based algorithm is used to find direction by fusing the time difference of both arrivals and steered power response with phase transform (SRP-PHAT). The smallest UAV with a 294 m detection distance was tested and demonstrated. Differential Doppler is used to overcome the decorrelation effect for better accuracy, as stated in [35]. In [32], the authors used classical detection and direction-finding methods using an array of microphones. There had been a physical investigation of the UAVs through experiments on acoustic emission with two signal models presented in harmonic signal and broadband signal for open area and indoor environments, respectively. The spectral signs are used for detecting and recognizing the UAVs in a noisy environment by incorporating the effect of noises in urban transport, speech signals, and environment noises. The result gives the same quality as the MFCC method, where acoustic portraits are unnecessary. The cross-correlation function is efficient in the direction-finding of the UAV. The study of [32] concludes with the following points: (1) high-pass filters are effective in the processing stage of UAV acoustic emission; (2) taking a noisy environment as a background experiment while detecting and recognizing UAVs by spectral signs performs similarly to the MFCC method, excluding acoustic portraits; (3) it is suggested to improve the efficiency of the CCFM algorithm in acoustic signals to filter out low-frequency noise; (4) MFCC and CCFM can be used to create an effective counter-action system against UAVs. [PERSON] et al. [37] researched the utilization of acoustic nodes in the UAV detection system. The proposed system finds the best configuration of the node for deploying the UAV acoustic detection system using machine learning models. The study was designed to investigate the best combination of acoustic features, STFT and MFCC, machine learning algorithms, SVM and CNN, for node optimization. After integrating the sensing nodes in four different configurations among the test sets, the one that maximizes the detection range without blind spots is selected. A semi-circle by the STFT-SVM model with a 75-m distance between the protected area and node has the best performance for configuration optimization. Demonstrating machine learning in the audio signal domain with different learning algorithms was used for detection module development. The study [37] focused on event sound detection using binary classification with MFCC features in an urban area. A drone acoustic detection system (DADS) is proposed and demonstrated experimentally to detect, classify, and track airborne objects in [33]. They used a Phantom 4 UAV for testing, which reached 350 m with four degrees as an average precision to track a maneuvering UAV with compact acoustic nodes. This test also implemented the classification algorithm to detect a multirotor UAV based on a specific sound inherent in the flight control mechanism. The Steven Institute of Technology has developed the DADS to detect, track, and classify anonymous UAVs by propeller noise. The proposed system has three or more microphone nodes in a tetrahedron configuration. The communication between the microphone nodes and the central computer is done through WiFi for processing. The orientation calibration for the DADS system is performed by emitting white noise from a speaker and tracking the GPS position for several minutes. Based on the difference between the detected direction and computed ones from the surveyed GPS, the orientation can easily be corrected in the case of detection and tracking. Establishing a tracking process can be predicted using collected data and parameters.