# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
## 1 Introduction  
Unmanned Aerial Vehicles (UAVs) are an emerging technology designed to flight with no human pilot. The UAV use was initially motivated by military applications, including reconnaissance, surveillance, and tracking [1, 2]. This is due to the fact that the UAVs can be readily equipped with sensors, cameras, radars, and more technologies for military purposes [3, 4]. Recently, the use of UAV has unprecedentedly increased including wide range of applications, such as, public safety, police, transportation, package delivery, and environmental monitoring. UAVs offer crucial help in rescue and recovery for disaster relief operations, when public communication networks get crippled [5], since they can form a salable and dynamic networks [6]. The UAV ability of hovering over a targeted area is desirable in many applications, for example, a UAV can help in localization when the Global position System (GPS) unavailable or inaccurate [7, 8]. Jamming attacks in wireless communication can be classified into two main levels [9; 10]. First, the elementary level that includes constant, deceptive, and random jammers. The constant jammer simply transmits a random bit continually to make the available channel busy [10]. Hence, any node within the jammer transmission range will be unable to access the channel and keep sensing until its battery depleted [9]. While the deceptive jammer, as the name suggested, tries to mimic the original transmission by sending regular packets towards the target node. In this case, all of the nodes located around the jammer will switch to receive mode and process the received corrupted data. The random jammer switches to active mode and transmitting jamming signals towards the target for a certain time and then return to sleep mode [11; 12]. Second, the advanced level that might include the large and the spot jammers. Here, the jammers sense all available channels; once it detects the channel used by legitimate one, it immediately transmits its signal to jam it. The jammers change their frequencies from carrier to another aiming to block all sub-channels [13]. Detecting the UAV jamming location is the first step towards preventing such an attack. By finding the UAV location, any appropriate action can be taken against the enemy UAV; e.g., physically destroying it or jam the Jammer by another jamming source [14; 15]. Tracking and localization jammers in wireless communication networks are still challenging topics. The used algorithms can be categorized into two main groups: range-free and range-based schemes [16]. The accuracy of the range-free techniques is based on nodes locations and the change of network topology. In this context, the Centroid Localization (CL) algorithm [17] and Weighted Centroid (WCL) algorithm [18] are examples of range-free techniques. They are both sensitive to node locations and the number of nodes deployed. The location accuracy increases when the number of nodes affected by the jamming signal increases [18; 19]. Therefore, the range free method is inaccurate when the affected nodes are fewer and are located closer to each other. However, the range-based method utilizes the so-called Jammer Received Signal Strength (JRSS) in order to detect and predict jammers' physical locations. This method tries to estimate the JRSS of the original signal, hence more reliable results can be achieved when compared to its range-free counterpart. Centralized Extended Kalman Filtering (EKF-Centr) was proposed in [20], where the algorithm is based on the received jammer's power from the boundary nodes at each time step. It was found [20; 21] that the tracking efficiency increases when the number of boundary nodes increases. A method for detecting jammer's location utilizing the Packet Delivery Ratio (PDR) rate at each node was reported in [22].