# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### RF-Based
Multiple passive RF sensors support these frequency bands and are used for non-invasive surveillance operations, including UAV monitoring, detection, localization, and tracking. In [11], the UAV detection system consists of a sensor node, Keysight RF sensor N6841A, operating in the range of 20 MHz-6 GHz, broadband antenna, and GPS tracker linked with geolocation software, N6854A. The RF signals are detected and collected within a radius of 2 km from the sensor node. A GPS antenna also records the time stamps for these collected signals. The localization of the UAV is performed using a detection algorithm and time difference of arrival (TDOA) measurements. Extended Kalman filter (EKF) framework and fitting motion models (MM) address these errors and improve localization performance. Furthermore, the research work in [10] illustrates a system model and architecture followed by experimental validation of the proposed direction finding (DF) method of sparse de-noising auto-encoder (SDAE) for UAV surveillance. This method consists of a single channel for a receiver and a directional phased array antenna. The mechanism of the system works as follows. First, the transmitted signal from the drone to its ground controller gets processed using an RF switching mechanism to measure the received signals output power at each directional phased array antenna. Next, the acquired output power values from the N-antennas of the phased directional array are input to the proposed SDAE-based deep neural network (DNN). The first network layer extracts received wattage values. Then the remaining network utilizes sparse representation to categorize UAVs' signal directions. The system diagram of the proposed method in [10] is depicted in Figure 2. To summarize, the wattage power values are passed to the proposed deep network, followed by the DF method, which exploits both the sparsity parameter of the transmitted UAV signal and the gain variation parameters of the directional antenna array. In [16], the authors discuss UAV detection using RF-transmitted signals between UAVs and their remote controllers. Power spectrum cancellation and multi-hop autocorrelation are developed to achieve RF passive detection of UAVs and controllers to detect emitted signals. The multi-hop autocorrelation method can detect the cross-correlation signal if the Signal-to-Noise (SNR) ratio is small by applying an emitted remote-control signal. A limitation of the multi-hop autocorrelation is the low accuracy in the case of fixed frequency in remote-control signals. The calculated parameters significantly depend on the autocorrelation function, leading to false positives. Hence, the study of [16] used the power spectrum cancellation technique to eliminate the effect of fixed frequency signals. Power spectrum cancellation works by first finding the differences between the control signal power spectrum and fixed frequency signals over time. Once the differences are identified, the fixed frequency signal is eliminated, and the remote control signal is applied to multi-hop auto-correlation to finalize the parameters for UAV detection. Furthermore, [17] stated that the RF passive detection method has the advantage of low cost, license-free, long-range distance coverage, and early warning capability. They also illustrated an RF passive system architecture, which analyzes the electromagnetic RF spectrum emitted from exchanged signals between the UAV and its controller. The passive RF detection algorithms analyze these signals to sense alternations in the frequency and time domain RF spectrum. Various studies have reported promising results utilizing different algorithms and techniques for RF-based UAV detection. However, the presence of noise affects the accuracy and detection range. Table 2 summarizes the reviewed papers and tabulates the features and accuracy of the undertaken methodology for RF-based UAV detection. Figure 2: Diagram of the system model in [10].