# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects
### Detection and Localization  
The detection of UAVs can be achieved either by direct self-reporting or indirect discovery performed by ground BSs. Conventional detection techniques, including radar, LiDAR sensors, acoustic recognition, electro-optical sensors, and computer vision seem inappropriate in challenging overcrowded dense-urban environments with numerous physical obstacles, ambient noise and light variation, and NLoS propagation conditions. To overcome these issues, intelligent detection and identification techniques have emerged based on data driven algorithms. Among them, ML classifier models have been previously proposed as an accurate classification method. Moreover, DL methods have also been used for the UAV detection problem. In Reference [88], a complementary ML-based identification method was applied using large training data sets consisting of UAV and non-UAV data flows from real-world encrypted Wi-Fi traffic. In general, exploiting Wi-Fi traffic is not straightforward due to possible data encryption whereas applying ML methods for real-time UAV identification may not always be beneficial due to time constraints and possible processing delays. Thus, a delay-aware ML-based UAV identification framework was proposed that achieves permissible accuracy while minimizes the delay. This framework handles the encrypted data flow as a time series, exploiting information of packet size and inter-arrival time for statistical feature extraction and estimating the packet inter-arrival time using maximum likelihood estimation (MLE) method. The prediction time is significantly reduced due to l1-norm regularization and the integration of the feature selection and accuracy optimization in one objective function. Based on the results, the delay-aware ML solution achieves an accuracy of about 85-95% being capable of identifying UAVs within 0.15 to 0.35 s in four scenarios where, in each one, a data set from a different traffic class was used. In Reference [89], an air-to-ground LTE cellular network was considered, under strong LoS propagation conditions and harsh interference. The existence of the aerial users (AUs) was accurately and timely detected using ML and measured RF data from UEs as input in a rural area. More specifically, for the AUs detection, a relatively small number of training samples and a weighted distribution for the training set were used along with three ML techniques: (i) the rather simple and insensitive to the distribution or size of the training set Bayesian estimator [90]; (ii) the SVM with high computational complexity and high sensibility but adequate performance [90]; and (iii) the MLP [91], which supports real-time on-the-fly training and avoids storage costs. It is noted that the decision time and the performance of the classification algorithms are a function of the number of features. Using the proposed ML classification methods and an significant number of features, the reliability was approximately 99% whereas the accuracy approached 100%. Moreover, it was observed that,depending on the available data set, each ML solution can lead to different results, as it is the case with unevenly balanced data among aerial and ground users. More specifically, Bayesian and SVM estimators exhibit better generalization capabilities for different performance metrics, while MLP can offer improved performance if a specific metric is targeted. An ANN-based detection algorithm was proposed in Reference [92] using three features: the slope, the kurtosis, and the skewness of the signal received from an UAV. Both outdoor and indoor experimental measured data from RF signals were used to train the algorithm and to obtain feature extraction and classification of UAV or non-UAV signals. The results in terms of the error rate revealed that the recognition rate can exceed 82% within a distance of 3 km, a score that is better than other scores provided by conventional non-ML detection methods.