AV-FDTI: Audio-visual fusion for drone threat identification

Small unmanned aerial vehicles (UAVs) have been widely used in different aspects of modern farming management, including pest and disease control in China in recent years. In this study, the spray performance of a small plant protection UAV at low volume spray was evaluated by adjusting the pesticide dosage and adding aerial spraying adjuvants. Droplet deposition, droplet density, coverage, control effect and pesticide residue from field trials were assessed. In addition, the residue and control effect of UAV spray were compared to manual knapsack at high volume spray. The results showed that, the adjuvant applying improved the efficiency of UAV spray. Also, the adjuvant applying reduced the dosage of imidacloprid by 20%. However, there was no significant difference on initial residue between UAV spray and knapsack spray. Thus, plant protection UAV spraying pesticide by adding appropriate adjuvant showed the ability of improving the pesticide effectiveness by improving the control efficiency, reducing the pesticide dosage and residue. Keywords: unmanned aerial vehicle (UAV), aerial spraying adjuvant, deposition, control efficiency, pesticide residue, wheat aphid DOI: 10.25165/j.ijabe.20181105.4298 Citation: Meng Y H, Lan Y B, Mei G Y, Guo Y W, Song J L, Wang Z G. Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle on wheat aphids control. Int J Agric & Biol Eng, 2018; 11(5): 46–53.

Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles used for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, or reconnaissance (ISR). The benefits include increased time on station and range as compared to electric-powered unmanned aerial vehicles and reduced acoustic and thermal signatures not available with gasoline-powered unmanned aerial vehicles. A conceptual design of a small unmanned aerial vehicle with a parallel hybrid-electric propulsion system, the application of a rule-based controller to the hybrid-electric system, and simulation results are provided. The two-point conceptual design includes an internal combustion engine sized for cruise speed and an electric motor and lithium-ion battery pack sized for endurance speed. A rule-based controller based on ideal operating line concepts is applied to the control of the parallel hybrid-electric propulsion system. The energy use for the 13.6 kg (30 Ib) hybrid-electric unmanned aerial vehicle with the rule-based controller during one-hour and three-hour ISR missions is 54% and 22% less, respectively, than for a four-stroke gasoline-powered unmanned aerial vehicle.

In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors, including GPS (global positioning system), an IMU (inertial measurement unit), and visual sensors, to complement the strengths and weaknesses of each hardware component, thereby mitigating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, a high-fidelity UAV model is established in the digital twin based on the real UAV parameters, providing a robust reference for data fusion. By utilizing the extended Kalman filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables the real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multisource, multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

Emergency locator transmitters (ELTs) used to locate manned aircrafts are not well suited to find and recover small crashed unmanned aerial vehicles (UAVs). ELTs utilize an international satellite system for search and rescue (Cospas-Sarsat System), which should leverage its expensive resources to save lives as a priority. Besides, ELTs are too big and heavy to be used within small UAVs. Some of the existing solutions for this problem are based on receivers that detect signal strength, which may be a long and tedious process not suitable for user needs. Others do not have enough range or require radio license and expensive amateur radio receivers. This paper presents an emergency radio beacon specifically designed to locate small UAVs. It is triggered automatically in the event of a crash and allows finding and recovering a crashed UAV in a fast and simple way. It meets not only the required specifications of user-friendliness, size and weight of this kind of application, but also it is a high precision and low cost device. Besides, it has enough range and endurance. The experiments carried out show the operation of the proposed system.

In the last decade, small and mirco unmanned aerial vehicles (MUAV) have become an increasing risk to security and safety in civilian and military scenarios. Further, countermeasures are hard to deploy due to the MUAV ability to operate with highly agile flight maneuver and the physical constrains due to very small cross-sections as well for RADAR as for LiDAR detection. The French-German Research Institute of Saint-Louis (ISL) is studying heterogeneous sensor networks for detection and identification of threats. Shortwave laser gated viewing is used to record images of the target and different image processing algorithms and filters are investigated to perform reliable tracking of MUAV flying in front of textured and clear sky backgrounds. Here, an analysis approach is presented to analyze the MUAV flight behavior and three-dimensional path from image data. Further, a prediction approach is presented to estimate the target position in near future.

This paper is a summary of experiments to assess alternative methods to control a small (<25 kg; under Transport Canada rules, small UAV are classified as under 25 kg; Transport Canada. 2014. TP15263 – Knowledge requirements for pilots of unmanned air vehicle systems (UAV) 25 kg or less, operating within visual line of sight. Transport Canada. August. Available from http://www.tc.gc.ca/eng/civilaviation/publications/page-6557.html [accessed 17 August 2015]) unmanned aerial vehicle (UAV) in manual mode. While it is true that the majority of a typical UAV mission will be in automatic mode (i.e., using an autopilot) this may not always be the case during takeoffs and landings, or if there is a failure of the autopilot. The concept of a manual control backup mode during all flight phases remains in proposed UAV regulations currently being defined in Canada and the US. The research summarized in this paper is an attempt to assess the accuracy of several manual control options for a small UAV. The paper includes both a theoretical discussion of the task of manually controlling a small UAV airframe and results from a series of field experiments investigating the use of first-person view techniques.

This paper describes an ultra-tight integration of a Global Navigation Satellite System (GNSS) receiver and an Inertial Navigation System (INS) for small Unmanned Aerial Vehicles (UAVs). The system is based on a low-cost and low-weight GNSS Intermediate Frequency (IF) sampler which has been fitted onto a small UAV. The storage of IF samples together with measurements from an Inertial Measurement Unit (IMU) has allowed the authors to process an Ultra-Tightly Coupled (U.T.C.) GNSS/INS solution from real data collected with a small UAV. The focus of this paper has been to investigate the potential benefits in signal tracking during flights below dense foliage and tree canopies by using the developed U.T.C. system. The obtained results have been compared to a scalar GNSS receiver and a high-grade commercial reference receiver and have showed significant improvements in tracking capabilities.

As part of the COUNTER program, cooperative control algorithms have been designed, implemented, and tested. This paper describes challenges, and their solutions, that were encountered during the program. The cooperative control challenges include: system configuration, suitability of the algorithms, and algorithm implementation into the system. During flight tests challenges arose such as: selection of autopilot, generation of flyable trajectories, and ability to image targets. In order to advance the state-of-the-art of Intelligence Surveillance and Reconnaissance (ISR) using Unmanned Aerial Vehicles (UAVs) over urban areas, researchers from the Air Force Research Laboratory conducted a project called Cooperative Operations in UrbaN TERrain (COUNTER). COUNTER consists of basic research, algorithm and ground station development, and flight tests. The concept employed in COUNTER is operator-on-the-loop control of Small unmanned Aerial Vehicles (SAVs) and Micro unmanned Aerial Vehicles (MAVs) performing cooperative multilevel ISR. In this concept, a single ground operator controls a single SAV, that flies high enough to view large portions of the urban area, and a team of four MAVs, that fly low enough to get good resolution images of objects selected from the SAV video. For an overview of the COUNTER program, see Jodeh 2008. 1 Since this program has included everything from concept to flight test, there were many conceptual and practical challenges to overcome. These challenges can be separated into two categories: those involving the cooperative control algorithms, and those involving vehicle control. The remainder of this paper provides an overview of the challenges that were encountered, explains how they were resolved, and discusses the lessons learned during the project.

The purpose of the study is to evaluate the effectiveness of the system for protecting a mobileprotected object from a small unmanned aerial vehicle (UAV). In connection with the peculiarityof the construction of the protection system for a mobile object, associated with the movementsynchronously with the object of the critical zone, into which the UAV should not fall, it was necessaryto develop a method and a mathematical model for assessing the effectiveness of the UAV. As an indicator of efficiency, the probability of diverting the UAV from the critical zone was taken.It is analyzed that the removal of the UAV from the critical zone is carried out due to the timelydetection of the UAV and its transition from navigation using the signals of satellite navigationsystems to the onboard inertial navigation system. The timeliness of detection is determined by thedetection range of the UA. The detection range is determined, first of all, by the parameters of thedetection means themselves, the topology of their placement, the size of the critical zone aroundthe protected object, and the direction of movement of the UAV. The size of the critical zone isdetermined by the danger of video filming from the UAV or payload drop. The direction of movementof the UAV most vulnerable to the protected object is the movement to the meeting point withthe protected object. The results of the analysis and the developed algorithms for the functioningof the protection system against a small UAV were taken into account when developing a mathematicalmodel for assessing the effectiveness of protecting a protected object. Due to the fact thatsome of the parameters of the BVS are not known in advance, their values were played withequiprobability. The method of statistical tests (Monte Carlo method) was used to calculate theefficiency indicator. In each test, random parameters of the UAV were played out, initial datawere set, the processes of movement of the protected object and the UAV, changes in the positionof the critical zone were reproduced, the UAV hitting the viewing areas of the detection tools,moving away from the general course of the UAV and getting into the limits of the critical zonewere evaluated. The developed method and mathematical model for evaluating the effectivenessmade it possible to conduct a numerical experiment aimed at assessing the influence of the UAVspeed on the protection efficiency of a mobile guarded object. The results of the work can be usedin the design and development of a system for protecting a protected object from UA, in a comparativeanalysis of alternative systems for protecting against UA. The proposed method and mathematicalmodel can also be used in the underwater marine environment when evaluating the effectivenessof protecting a protected inhabited / uninhabited object from marine robotic systems.

Beyond their benign uses, the small Unmanned Aerial Vehicles (UAVs) are expected to take the major role in future smart cities that have attracted the attention of the public and authorities. Therefore, detecting, tracking and classifying the type of UAVs is important for surveillance and air traffic management applications. Existing UAVs detection works focus on radars, visual detection, and acoustic sensors. However, the work was done by applying Support Vector Machine (SVM), k-Nearest Neighbor (KNN) based methods to classify the UAVs need a large number of samples for feature extraction to train a model. In this paper, we propose a new small UAVs classification system using Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGANs) based on the wireless signals collected from the UAVs of various types. Before the classification, using the Universal Software Radio Peripheral (USRP), oscilloscope and antenna to collect the wireless signals, preprocessing and dimensionality reduction to represent information at a lower dimension space. The processed data from UAVs is input to the UAVs' discriminant model of the AC-WGANs for classification. The obtained results show the effectiveness of the proposed system, which can achieve a recognition accuracy of around 95% in the indoor environment and can also be suitable in the outdoor environment.

The motion model of an unmanned aerial vehicle (UAV) is a time-varying function that is often unknown to the tracking entity, which makes accurate tracking of highly maneuvering UAVs a challenging problem. Most state-of-the-art tracking techniques employ a fixed set of parametric models that approximate the possible maneuvers along the target trajectory to a reasonable accuracy. However, such predetermined motion models might not be adequate for frequent and aggressive maneuvers performed by small UAVs. To this end, we build a data driven adaptive filtering algorithm that improves the tracking accuracy by using a recurrent neural network (RNN)-based motion model that is trained on realistic simulated data generated from a medium fidelity simulink model of a fixed-wing UAV. We then train another feed-forward neural network in conjunction with the pretrained RNN-based motion model, to adaptively combine the incoming current measurement with the predicted state based on the output of a change detection algorithm that detects any increase/decrease in the uncertainty in the predicted states. Our analysis and results show that the proposed tracking algorithm outperforms the state-of-the-art interactive multiple model (IMM) algorithm for highly maneuvering trajectories.

The use of multiple heterogeneous, low-cost, small Unmanned Aerial Vehicles (UAVs) as a tool in several application domains is becoming increasingly important. One critical aspect to enable the use of such vehicles is the coordination/planning system, whose task complexity increases with the number of vehicles and the communications constraints that arise due to their small size and large distances. In this work, we propose a control architecture for a platoon of relay UAVs that are independent of the coordination system. The platoon task consists in interconnecting the communication link between the possibly mobile command station and a UAV in a mission. The relays are actively driven to deploy, create a network and maintain a desired Quality-of-Service (QoS) level, defined in this paper. We present an architecture that is composed by a waypoint generator based on the network QoS and a Time Coordinated Path Following (TCPF) controller with a method to reduce the frequency of information exchange between the relay UAVs, through the use of a self-triggered control strategy. Exploiting this architecture, it is possible to plan a mission operation for a UAV without the need of considering vehicle-to-command-station communication constraints that will be satisfied by the introduction of the relay-UAVs platoon. Simulation results are provided to illustrate the efficacy of the developed strategy. The self-triggered approach results in significant reduction of information exchange between the relay UAVs, while maintaining the user desired network QoS.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This article presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a commercial off-the-shelf millimeter wave (mmWave) radar. mmHawkeye does not require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low signal-to-noise ratio and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering and then identify it with a Long Short-Term Memory–based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80 m.

Analysis of a hydrogen fuel cell-PV power system for small UAV

Radar surveillance of unmanned aerial vehicles (UAVs) is actively developing area of scientific research. This article provides a review and analysis of publications in recent years devoted to the methods and radar systems of detection and recognition of classes and types of UAVs. It is noted that the most difficult targets for radar detection are low-sized, low-speed small UAVs (drones) flying at low and extremely low altitudes. If large and medium-sized UAVs can be detected by modern radar systems, then for the detection of small UAVs it is advisable to create specialized highly efficient, highly mobile, portable and inexpensive active UAV detection radars. The technical requirements for such radars are defined and recommendations for their implementation are provided. High-performance protection systems based on adaptive lattice filters are offered to protect UAV detection radars from noise jamming and passive interference. It is shown that the research on the methods of recognizing UAV classes and types is a development of the existing theory and technology of radar recognition of air targets.