With the decrease in the cost and size of drones in recent years, their number has also increased exponentially. As such, the concerns regarding security aspects that are raised by their presence are also becoming more serious. The necessity of designing and implementing systems that are able to detect and provide defense actions against such threats has become apparent. In this paper, we perform a survey regarding the different drone detection and defense systems that were proposed in the literature, based on different types of methods (i.e., radio frequency (RF), acoustical, optical, radar, etc.), with an emphasis on RF-based systems implemented using software-defined radio (SDR) platforms. We have followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines in order to provide a concise and thorough presentation of the current status of the subject. In the final part, we also describe our own solution that was designed and implemented in the framework of the DronEnd research project. The DronEnd system is based on RF methods and uses SDR platforms as the main hardware elements.

Implementation of dynamic spectrum access (DSA) in cognitive radio (CR) systems requires the unlicensed secondary users (SU) to implement spectrum sensing to monitor the activity of the licensed primary users (PU). Energy detection (ED) is one of the most widely used methods for spectrum sensing in CR systems, and in this paper we present a novel ED algorithm with an adaptive sensing threshold. The three-event ED (3EED) algorithm for spectrum sensing is considered for which an accurate approximation of the optimal decision threshold that minimizes the decision error probability (DEP) is found using Newton’s method with forced convergence in one iteration. The proposed algorithm is analyzed and illustrated with numerical results obtained from simulations that closely match the theoretical results and show that it outperforms the conventional ED (CED) algorithm for spectrum sensing.

Spectrum sensing (SS) based on energy detection (ED) is a simple yet effective approach to detect the presence of unknown signals that are active in a specific band of frequencies. The classical ED (CED) algorithm uses the value of the energy detected in the current sensing slot as test statistic and has a fixed threshold, but improved signal detection performance is possible by modifying the test statistic and/or the detection threshold. In this paper we propose a novel ED algorithm for SS that considers a binary activity model for the signal to be detected and combines the use of an average energy test statistic with an adaptive decision threshold for improved detection performance. We present the analytical characterization of the proposed test statistic in terms of its mean and variance, and derive the expressions corresponding to the correct decision probability (CDP) and false alarm probability (FAP). Using the derived CDP and FAP expressions, we also determine the detection thresholds that yield desired values for the false alarm and missed detection probabilities. The proposed algorithm is illustrated with numerical results obtained from simulations, which confirm our theoretical findings and also show that the algorithm outperforms alternative adaptive ED algorithms.

Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in $K$ consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of $K$ . Our analysis is corroborated with numerical results in which Monte-Carlo simulations are used to determine FAP and CDP to confirm the validity of the analytical expressions obtained and to illustrate that good receiver operating characteristic (ROC) performance is reached for reasonably small values of $K$ .

In this brief, nonlinear digital filters with finite precision are analyzed as recursive systematic convolutional (RSC) encoders. An infinite-impulse-response (IIR) digital filter with finite precision (wordlength of N bits) is a rate-1 RSC encoder over a Galois field GF(2N). The Frey chaotic filter is analyzed for different wordlengths N, and it is demonstrated that the trellis performances can be enhanced by proper filter design. Therefore, a modified definition for the encoding rate is provided, and a trellis design method is proposed for the Frey filter, which consists of reducing the encoding rate from 1 to 1/2. This trellis optimization partially follows Ungerboeck's rules, i.e., increasing the performances of the encoded chaotic transmission in the presence of noise. In fact, it is demonstrated that for the same spectral efficiency, the modified Frey encoder outperforms the original Frey encoder only for N = 2. To show the potential of these nonlinear encoders, it is demonstrated that a particular nonlinear digital filter over GF(4) is equivalent to a GF(2) conventional optimum RSC encoder. The symbol error rate (SER) is estimated for all the proposed schemes, and the results show the expected coding gains as compared to their equivalent nonencoded and linear versions.