UAV Detection Research Articles

Peculiarities of small unmanned aerial vehicles detection and recognition

Review and analysis of methods for detection and recognition of unmanned aerial vehicles (UAVs) are conducted. The channels for the detection of UAVs - acoustic, optical, radar, infrared, radio channel are considered. The advantages and disadvantages of the channels used are compared and appreciated. In the case of small UAVs, there are a number of significant difficulties and limitations. One of the directions in the UAVs detection is acoustic observation. The noise generated by the UAV propulsion system and the air propeller is a significant demasking feature. Creating and improving methods for detecting, guiding and recognizing small UAVs by the reception and processing their sound signals is an urgent task. When using such a method of detecting UAVs, frequency spectra, spectrograms, normalized autocorrelation functions, and phase portraits of acoustic signals are used. Estimates of spectral coefficients, determined by a discrete realization containing a predetermined number of samples, as well as parameters of autoregression models can serve as information signs of the UAVs sound image.

A Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications.

This paper presents a visual-based approach that allows an Unmanned Aerial Vehicle (UAV) to detect and track a cooperative flying vehicle autonomously using a monocular camera. The algorithms are based on template matching and morphological filtering, thus being able to operate within a wide range of relative distances (i.e., from a few meters up to several tens of meters), while ensuring robustness against variations of illumination conditions, target scale and background. Furthermore, the image processing chain takes full advantage of navigation hints (i.e., relative positioning and own-ship attitude estimates) to improve the computational efficiency and optimize the trade-off between correct detections, false alarms and missed detections. Clearly, the required exchange of information is enabled by the cooperative nature of the formation through a reliable inter-vehicle data-link. Performance assessment is carried out by exploiting flight data collected during an ad hoc experimental campaign. The proposed approach is a key building block of cooperative architectures designed to improve UAV navigation performance either under nominal GNSS coverage or in GNSS-challenging environments.

Countering UAVs – the Mover of Research in Military Technology

Unmanned aerial vehicles (UAVs) are massively seeping into a wide range of human activities. Along with other remotely controlled or automatic devices, they have entered many aspects of human activities and industry. While the majority of researchers have been working on the construction, deployment and non-military use of UAVs, the protection against UAVs remained on the edge of their interest. Nowadays, the situation is rapidly changing. The risk of misuse of UAVs by criminals, guerrillas or terrorists has compelled authorities, scientists and defence industry to face this threat. Organisations have launched crucial infrastructure defence programs to cope with UAV threat. To solve this problem, it is necessary to develop disciplines improving the air space surveillance and UAVs elimination techniques. The substantial aspects of the UAVs detection and elimination were analysed, being supported by a number of conferences, workshops and journals articles. The contribution of the study in the Counter–UAV area consists particularly in generalisation and evaluation of the main technical issues. The aim of this paper is to emphasise the importance of developing new scientific fields for countering UAVs, and hence it is directed firstly on the scientific audience.

Cyclostationary Phase Analysis on Micro-Doppler Parameters for Radar-Based Small UAVs Detection

Robust radar detection for small unmanned aerial vehicles (UAVs) is a challenging problem, as UAVs tend to fly at slow speed and low altitude, with a small radar cross section. Those properties may lead to difficulties in separating echoes from a significant clutter response. In this case, micro-Doppler (m-D) effect induced by the rotation of the rotor blades would be another preferable signature to enhance the discrimination between ground clutter and UAVs returns, as well as associated correct labels to small UAVs among others (such as birds). However, the detection presents two stubborn problems. First, the m-D signal always consists of multiple components, one of which is induced by the vibration of the platform. It is inevitable that the estimation precision would be affected by the m-D interference. Second, the m-D components would be rather weak. It could not always be observed due to the shelter of Doppler signal induced by the translation of the platform. In this paper, we propose the cyclostationary phase analysis (CPA) to estimate the m-D parameters for radar detection of small UAVs. This method utilizes the phase term of the returned signal as the input of the cyclostationary analysis. It could simplify the procedure of the parameter estimation, as well as reducing the m-D interference. Moreover, since the phase term of the Doppler components is not cyclostationary, the CPA could eliminate the impact of Doppler signal. Simulations and filed experiments are provided to showing an outranking performance than the existing methods in estimation precision.

Neural Network based Real-time UAV Detection and Analysis by Sound

In this paper, we present a real-time artificial intelligence system for drone detection on multiple locations. With ensemble machine learning on multiple regional clients and a central neural network server, the users can easily monitor the drone`s appearance based on its motor sound data. The clients perform FFT on the sampled real-time data and detect drones using Plotted Image Machine Learning (PIL) with sending the detected audio sample to the server. The PIL uses image data from the visualized FFT graph to detect robust points, and compares the average image similarity with a reference FFT template associated with a target of interest. The server visualizes each client`s detection status including machine learning result, and trains Artificial Neural Network (ANN) with extensive regional samples from clients. Afterwards, the server tests its ANN model whenever the client reports drone detection. The accuracy rate of client`s PIL test is 83% and server`s ANN test accuracy rate is 86%. The major deliverables of this work are a software package framework one may use to train its ANN model with various sound samples from different places to make a generalized drone detection model.

Detection, Tracking, and Interdiction for Amateur Drones

Unmanned aerial vehicles, also known as drones, are expected to play major roles in future smart cities, for example, by delivering goods and merchandise, serving as mobile hotspots for broadband wireless access, and maintaining surveillance and security. The goal of this survey article is to study various potential cyber and physical threats that may arise from the use of UAVs, and subsequently review various ways to detect, track, and interdict malicious drones. In particular, we review techniques that rely on ambient radio frequency signals (emitted from UAVs), radars, acoustic sensors, and computer vision techniques for detection of malicious UAVs. We present some early experimental and simulation results on range estimation of UAVs and receding horizon tracking of UAVs. Finally, we summarize common techniques that are considered for interdiction of UAVs.

Unauthorized Amateur UAV Detection Based on WiFi Statistical Fingerprint Analysis

Amateur drones are enjoying great popularity in recent years due to the wide commercial diffusion of small, rather low-cost devices. More and more user-friendly, easy-to-pilot aerial and terrestrial drones are available off the shelf, and people can even remotely pilot them using their smartphones. This situation brings up the problem of keeping unauthorized drones away from private or sensitive areas, where they can represent a personal or public threat. With this motivation, after a survey of the existing solutions, we propose a WiFi-based approach aimed at detecting nearby aerial or terrestrial devices by performing statistical fingerprint analysis on wireless traffic. This novel detection technique, tested in a variety of real-life scenarios, proved able to efficiently detect and identify intruder drones in all the considered experimental setups, making it a promising unmanned aerial vehicle detection approach in the framework of amateur drone surveillance.

On the Performance of the Range Imaging Technique Estimated Using Unmanned Aerial Vehicles During the ShUREX 2015 Campaign

For the first time, the performance of the range imaging technique of the very high frequency band middle and upper atmosphere (MU) radar (Shigaraki observatory, Japan), when using frequency diversity, is assessed. This is done by the detection of unmanned aerial vehicles (UAVs) operated near the radar during the Shigaraki UAV radar experiment campaign, carried out from June 1, 2015 to June 14, 2015. During this campaign devoted to the measurements of fine-scale turbulence and stability in the lower troposphere using the turbulence sensors on the DataHawk UAV and the MU radar, the detection of the small UAV by the MU radar provided an excellent opportunity for taking stock of the range imaging technique in the presence of a single hard target. It was found that range imaging reproduces a faithful image of the aircraft position and its displacements with an excellent accuracy (of the order of ~10 m), giving extra credence to the thin echo layers and their vertical displacements generally observed from the range imaging technique in stably stratified conditions.

Acoustic Detection of a Fixed-Wing UAV

The following paper presents results obtained from experiments conducted to investigate the viability of acoustic sensing to form the basis of a non-cooperative aircraft collision avoidance system. An unmanned aerial vehicle (UAV) fitted with two microphones was flown in the vicinity of another airborne UAV to determine the maximum distance at which the intruding aircraft could be detected. A two-dimensional analytical model to approximate the minimum detection distance required to facilitate an avoidance maneuver for a given spatial configuration is presented. A method to increase detection distances by exploiting the harmonic nature of acoustic signals generated by propeller-driven aircraft is also presented. The method significantly increases the detection distances compared to the commonly used incoherent spectral mean. It was found that a small gasoline-powered UAV could be detected at distances up to 678 m, which is more than double the minimum required to avoid a head-on collision.

A UAV Detection Algorithm Based on an Artificial Neural Network

This paper presents an artificial neural network (ANN)-based detection algorithm for an unmanned aerial vehicle (UAV). The slope, kurtosis, and skewness of the signal received from the UAV are employed in this algorithm. The training of the three corresponding feature matrices is done using UAV, and non-UAV signals can be classified effectively for the UAV sensor network based on ANN. Outdoor data over a bridge in the Jimo District, Qingdao, and indoor data from a research laboratory are used for system training and evaluation. The results obtained show that the proposed detection algorithm based on an ANN outperforms methods based on the slope, kurtosis, and skewness of the received signal in terms of the error rate. The recognition rate with the proposed algorithm is greater than 82% within a distance of 3 km, which is better than other UAV detection methods such as active radar, acoustic, and visual recognition.

Fast Aerial UAV Detection Based on Image Segmentation and HOG-FLD Feature Fusion

Abstract In order to detect non-cooperative target UAV quickly and accurately, a novel method of UAV detection method based on graph theory and HOG-FLD feature fusion is presented in this paper. In order to avoid the time-consuming full search, the candidate areas of the UAV are obtained through the selective search of the image segmentation and the similarity, and the features are extracted through the method of gradient orientation histogram fusion FLD linear to train the SVM classifier with generalization ability to identify the UAV. The method can detect the UAV quickly and accurately under complicated background and circumstances of various position and angle. Compared with the sliding window method based on image segmentation and HOG+SVM, the experimental results show that the speed of this method has been obviously improved with the same recognition accuracy.

基于无线电技术的民用无人机侦测与管控方法在监所环境的探究

基于无线电技术的民用无人机侦测与管控方法在监所环境的探究

Fault Detection and Basic In-Flight Reconfiguration of a Small UAV Equipped with Elevons

The paper introduces the in-flight fault detection and basic reconfiguration of a small unmanned aerial vehicle equipped with two elevons and an electric motor. The considered fault scenario is one control surface stuck at a given position during straight and level flight. The fault detection is solved with Multiple Model Adaptive Estimation considering non-faulty and faulty (left or right surface stuck) system models. Basic reconfiguration to stabilize the flight against atmospheric disturbances is done applying the remaining surface in the lateral channel and the total energy control concept to hold the airspeed and altitude between acceptable limits in the longitudinal channel. Promising results are achieved in software-in-the-loop simulation with the fault detection and reconfiguration.

Research on Obstacle Detection of UAV Based on HSV Color Space

Research on Obstacle Detection of UAV Based on HSV Color Space

UAV Detection with S Transform of t-student Window

So far, the detection of helicopters, especially micro helicopters, has been widely studied, while few works are available on detection of rotor unmanned aerial vehicle (UAV). Due to its low flight height, small RCS and the complicated urban environment, the traditional methods are unable to achieve effective detection of rotor UAV. In this paper, we present a novel S transform with improved window based on t-student to deal with this problem. The simulation and field experiments all show that the proposed S transform can suppress Gaussian noise effectively, and obtain the clear time-frequency distribution of the micro-motion target. Thus, the proposed S transform provides some references for detecting the micro-motion target with small RCS and small rotation amplitude such as the rotor UAV.

Regularized 2-D complex-log spectral analysis and subspace reliability analysis of micro-Doppler signature for UAV detection

Unmanned aerial vehicle (UAV) has become an important radar target recently because of its wide applications and potential security threats. Traditionally, visual features such as spectrogram were often extracted for human operators to identify the micro-Doppler signature (mDS) of UAVs, i.e. sinusoidal modulation. In this paper, the authors aim to design a system for machine automatic classification of UAVs from other targets, particularly from birds as both UAVs and birds are small and slow-moving radar targets. Most existing mDS representations such as spectrogram, cepstrogram and cadence velocity diagram discard the phase spectrum, and only make use of the magnitude spectrum. What’s more, people often take the logarithm of the spectrum to enlarge the weak mDS but without sufficient care, as noise may be enlarged at the same time. The authors thus propose a regularized 2-D complex-log-Fourier transform to address these problems. Furthermore, the authors propose an object-oriented dimension-reduction technique: subspace reliability analysis, which directly removes the unreliable feature dimensions of two class-conditional covariance matrices in two separate subspaces. On the benchmark dataset, the proposed approach demonstrates better performance than the state-of-the-art approaches. More specifically, the proposed approach significantly reduces the equal error rate of the second best approach, cadence velocity diagram, from 6.68% to 3.27%.

Analyzing the threat of unmanned aerial vehicles (UAV) to nuclear facilities

Unmanned aerial vehicles (UAV) are among the major growing technologies that have many beneficial applications, yet they can also pose a significant threat. Recently, several incidents occurred with UAVs violating privacy of the public and security of sensitive facilities, including several nuclear power plants in France. The threat of UAVs to the security of nuclear facilities is of great importance and is the focus of this work. This paper presents an overview of UAV technology and classification, as well as its applications and potential threats. We show several examples of recent security incidents involving UAVs in France, USA, and United Arab Emirates. Further, the potential threats to nuclear facilities and measures to prevent them are evaluated. The importance of measures for detection, delay, and response (neutralization) of UAVs at nuclear facilities are discussed. An overview of existing technologies along with their strength and weaknesses are shown. Finally, the results of a gap analysis in existing approaches and technologies is presented in the form of potential technological and procedural areas for research and development. Based on this analysis, directions for future work in the field can be devised and prioritized.

Unmanned aerial vehicles for surveying marine fauna: assessing detection probability

Aerial surveys are conducted for various fauna to assess abundance, distribution, and habitat use over large spatial scales. They are traditionally conducted using light aircraft with observers recording sightings in real time. Unmanned Aerial Vehicles (UAVs) offer an alternative with many potential advantages, including eliminating human risk. To be effective, this emerging platform needs to provide detection rates of animals comparable to traditional methods. UAVs can also acquire new types of information, and this new data requires a reevaluation of traditional analyses used in aerial surveys; including estimating the probability of detecting animals. We conducted 17 replicate UAV surveys of humpback whales (Megaptera novaeangliae) while simultaneously obtaining a 'census' of the population from land-based observations, to assess UAV detection probability. The ScanEagle UAV, carrying a digital SLR camera, continuously captured images (with 75% overlap) along transects covering the visual range of land-based observers. We also used ScanEagle to conduct focal follows of whale pods (n=12, mean duration=40min), to assess a new method of estimating availability. Acomparison of the whale detections from the UAV to the land-based census provided an estimated UAV detection probability of 0.33 (CV=0.25; incorporating both availability and perception biases), which was not affected by environmental covariates (Beaufort sea state, glare, and cloud cover). According to our focal follows, the mean availability was 0.63 (CV=0.37), with pods including mother/calf pairs having a higher availability (0.86, CV=0.20) than those without (0.59, CV=0.38). The follows also revealed (and provided a potential correction for) a downward bias in group size estimates from the UAV surveys, which resulted from asynchronous diving within whale pods, and a relatively short observation window of 9s. We have shown that UAVs are an effective alternative to traditional methods, providing a detection probability that is within the range of previous studies for our target species. We also describe a method of assessing availability bias that represents spatial and temporal characteristics of a survey, from the same perspective as the survey platform, is benign, and provides additional data on animal behavior.

On-board model-based automatic collision avoidance: application in remotely-piloted unmanned aerial vehicles

This paper focuses on real-world implementation and verification of a local, model-based stochastic automatic collision avoidance algorithm, with application in remotely-piloted (tele-operated) unmanned aerial vehicles (UAVs). Automatic collision detection and avoidance for tele-operated UAVs can reduce the workload of pilots to allow them to focus on the task at hand, such as searching for victims in a search and rescue scenario following a natural disaster. The proposed algorithm takes the pilot's input and exploits the robot's dynamics to predict the robot's trajectory for determining whether a collision will occur. Using on-board sensors for obstacle detection, if a collision is imminent, the algorithm modifies the pilot's input to avoid the collision while attempting to maintain the pilot's intent. The algorithm is implemented using a low-cost on-board computer, flight-control system, and a two-dimensional laser illuminated detection and ranging sensor for obstacle detection along the trajectory of the robot. The sensor data is processed using a split-and-merge segmentation algorithm and an approximate Minkowski difference. Results from flight tests demonstrate the algorithm's capabilities for tele-operated collision-free control of an experimental UAV.

Detection of unmanned aerial vehicles using a visible camera system.

Unmanned aerial vehicles (UAVs) flown by adversaries are an emerging asymmetric threat to homeland security and the military. To help address this threat, we developed and tested a computationally efficient UAV detection algorithm consisting of horizon finding, motion feature extraction, blob analysis, and coherence analysis. We compare the performance of this algorithm against two variants, one using the difference image intensity as the motion features and another using higher-order moments. The proposed algorithm and its variants are tested using field test data of a group 3 UAV acquired with a panoramic video camera in the visible spectrum. The performance of the algorithms was evaluated using receiver operating characteristic curves. The results show that the proposed approach had the best performance compared to the two algorithmic variants.