We consider how to protect Unmanned Aerial Vehicles (UAVs) from Global Positioning System (GPS) spoofing attacks to provide safe navigation. The Global Navigation Satellite System (GNSS) is widely used for locating drones and is by far the most popular navigation solution. This is because of the simplicity and relatively low cost of this technology, as well as the accuracy of the transmitted coordinates. Nevertheless, there are many security threats to GPS navigation. These are primarily related to the nature of the GPS signal, as an intruder can jam and spoof the GPS signal. We discuss methods of protection against this type of attack and have developed an experimental stand and conducted scenarios of attacks on a drone’s GPS system. Data from the UAV’s flight log were collected and analyzed in order to see the attack’s impact on sensor readings. From this we identify a new method for detecting UAV anomalies by analyzing changes in internal parameters of the UAV. This self-diagnosis method allows a UAV to independently assess the presence of changes in its own subsystems indicative of cyber attacks.

This article describes the design and implementation of a low-profile sinuous slot antenna, intended for ultra-wideband (UWB) sensor networks, which can be produced on one conductive layer. The article explains the design and optimization of the sinuous slot antenna and its modifications, including its sinusoidal curve shape. Other modifications were aimed at optimizing the antenna feeding. Desirable properties of the designed and implemented antenna modifications were verified both by simulation and empirically. Experimental measurements of the antenna’s properties were carried out using a vector network analyzer in an anechoic chamber and also by a pulsed UWB radar in the frequency range from 0.1 to 6 GHz. The low-profile antennas were implemented on a Rogers RO3206 substrate.

The authors of this paper study the synthesis of new models and methods for signal detection in additive correlated non-Gaussian noise. A new moment quality criterion decision making is proposed based on a random process description using moments and a formation of polynomial decision rules. Taking into account parameters of non-Gaussian distribution of random variables (such as the moments of third and higher orders and joint cumulants), it is shown that nonlinear processing of samples can increase the signal processing efficiency. A synthesis of effective methods and algorithms of data processing in non-Gaussian noise is also presented in this work.

In this paper, imaging method for moving target tracking by a multistatic ultra-wideband radar system is described. The task of moving target tracking consists in estimation of a target trajectory based on processing of raw radar data obtained from all receiving channels of a radar system. Then, the imaging method applied for target tracking consists of such signal processing phases as raw radar data pre-processing, background subtraction, fusion of the data obtained by receiving antennas of a radar, detection, localization and tracking itself. The performance of the imaging method described in this paper is demonstrated by signal processing obtained by M-sequence UWB radar for a scenario represented by through wall tracking of a single moving target. The obtained results confirm the excellent performance of the discussed method.

Creation and operation of sensor systems is a complex challenge not only for industrial and military purposes but also for consumer services (“smart city”, “smart home”) and other applications such as agriculture (“smart farm”, “smart greenhouse”). The use of such systems gives a positive economic effect and provides additional benefits from various points of view. At the same time, due to a large number of threats and challenges to cyber security, it is necessary to detect attacks on sensor systems in a timely manner. Here we present an anomaly detection method in which sensor nodes observe their neighbors and detect obvious deviations in their behavior. In this way, the community of neighboring nodes works collectively to protect one another. The nodes record only those parameters and attributes that are inherent in any node. Regardless of the node’s functionality, such parameters include the amount of traffic passing through the node, its Central Processing Unit (CPU) load, as well as the presence and number of packets dropped by the node. Our method’s main goal is to implement protection against the active influence of an internal attacker on the whole sensor network. We present the anomaly detection method, a dataset collection strategy, and experimental results that show how different types of attacks can be distinguished in the data produced by the nodes.