# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects
### Detection and Localization
The detection and classification of UAVs using Markov-based naive Bayes ML techniques was also realized in Reference [93] for multiple RF raw signal fingerprints from several UAV controllers and different SNR levels. In particular, the classification was based on the energy transient signal and statistical processing in order to avoid noise sensitivity and to adapt to modulation techniques. This method is characterized by low computational complexity, does not exploit the time-domain, and hence averts possible delays for the detection of the transient of the signal, especially in low SNR scenarios. The feature sets were used to train different ML algorithms, including the KNN classification [90], the discriminant analysis (DA), the SVM, and the NN. The results showed that the KNN classifier has the best performance, obtaining an accuracy of above 80% for 15 dB SNR and 14 controllers, whereas the NN has the the worst performance when SNR is low. Further, increasing the number of controllers leads to an average accuracy of 96.3%. Overall, RF fingerprinting is a promising technique, but it requires specific equipment, e.g., expensive SDRs at high frequencies. In Reference [94], an acoustic ML-based UAV detection approach with multiple listening nodes, i.e., computing units capable of detecting the UAV sounds, and a control center was presented, relying on features such as Mel-frequency cepstral coefficients (MFCC) [95] and short-time Fourier transform (STFT) for training. STFT provides more signal information but contains more noise compared to MFCC, and recently, it has been considered promising for training using acoustic signals, as DNNs and DL are capable of processing more complicated data. SVM and CNNs were considered in order to estimate in real-time the flying path of an UAV in noisy environments. Overall, the STFT-SVM model succeeded in achieving the best performance, as SVM shows improved classification performance in binary tasks with reduced training resources compared to CNN. The current flying status (i.e., whether it is flying or it is static on the ground) of a powered-on UAV that is remotely controlled by a malicious operator was detected in real-time in Reference [96] by eavesdropping the communication traffic exchanged between the UAV and its controller and by applying classification algorithms. In particular, the Weka platform [97] was adopted that contains various standard ML algorithms for data mining tasks. Among them, the Trees-J48 (J48) [98], RandF [99], and NN were used. This cost-effective passive method does not require special equipment or signal transmission and intends to statistically analyze features, such as packet size and inter-arrival time analysis. The results showed that the delay is less than 4 s with 93% detection accuracy. In Reference [100], a system that autonomously, instantaneously, and accurately locates the controllers of UAVs from the transmitted RF control signals was described. This system uses an RF sensor array to monitor the signal spectrum, whereas a CNN is trained in order to predict the bearing of the drone controller relative to the sensor and, then, estimates the position of the controllers. Using the proposed configuration, an operator can be apprehended at distances of up to 500 m with a mean absolute error of 3.67\({}^{\circ}\) in bearing calculation, which in turns corresponds to a fair positional error of 40 m. Then, in Reference [101], ML tools have been used for estimating the locations of drone-UEs based on sparse information. In particular, employing the kernel density estimation, the UAV spatial distribution has been estimated and then used for obtaining the optimal cell association. Moreover, tools from the optimal transport theory have been also adopted. The presented results confirm that the proposed approach significantly reduces the UAVs-UEs latency as compared to conventional UE-cell association based on SINR while also improves the spectral efficiency.