A ground-based vision system for UAV tracking

In this paper, the Envelope Prediction Algorithm (EPA) is presented based on the Unscented Kalman Filter (UKF) method for the Unmanned Aerial Vehicle (UAV). Firstly, the flight envelope of UAV extended system is calculated by the Attainable Equilibrium Set (AES) algorithm. The discrete Newton iteration method is used to solve the nonlinear algebraic equation of UAV, and then the AES is obtained at the current state. Secondly, the UKF has the characteristics of high accuracy and strong stability, which can effectively to predict the next moment state value of the nonlinear system. Therefore, the UKF in this paper is used to predict the state variables of the UAV system at the next moment. Finally, the AES algorithm is adopted to calculate the next moment flight envelope of the UAV. The simulation results of the UAV indicate that the EPA algorithm can effectively predict the next moment AES of the UAV based on the predict the results.

Large‐scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high‐performance low‐altitude solar‐powered unmanned aerial vehicles (UAVs). However, today these UAVs suffer from small payload capacity, low energetic margins, and high operational complexity. To tackle these problems, this paper presents four individual technical contributions and integrates them into an existing solar‐powered UAV system: First, a lightweight and power‐efficient day/night‐capable sensing system is discussed. Second, means to optimize the UAV platform to the specific payload and to thereby achieve sufficient energetic margins for day/night flight with payload are presented. Third, existing autonomous launch and landing functionality is extended for solar‐powered UAVs. Fourth, as a main contribution an extended Kalman filter (EKF)‐based autonomous thermal updraft tracking framework is developed. Its novelty is that it allows the end‐to‐end integration of the thermal‐induced roll moment into the estimation process. It is assessed against unscented Kalman filter and particle filter methods in simulation and implemented on the aircraft's low‐power autopilot. The complete system is verified during a 26 h search‐and‐rescue aerial sensing mock‐up mission that represents the first‐ever fully autonomous perpetual endurance flight of a small solar‐powered UAV with a day/night‐capable sensing payload. It also represents the first time that solar‐electric propulsion and autonomous thermal updraft tracking are combined in flight. In contrast to previous work that has focused on the energetic feasibility of perpetual flight, the individual technical contributions of this paper are considered core functionality to guarantee ease‐of‐use, effectivity, and reliability in future multiday aerial sensing operations with small solar‐powered UAVs.

A unified detection-tracking-estimation vision navigation system and algorithms framework for position and pose estimation of UAV (unmanned aerial vehicle) is proposed in this paper. The major contribution of this work is a novel cooperative object detection algorithm for robust detection of UAV: the spatial color pattern and temporal flicking pattern of the cooperative markers (groups of LEDs) mounted on UAV are adaptively adjusted to make it more salient from background, hence much easier for the vision system to detect it under unconstrained environment, especially during UAV taking-off/landing phase which is typically near ground and suffer more interference from complex scene and changing of illumination. After extracting position and pose information by vision system, particle filtering is used to smooth the trajectory of UAV based on established vehicle dynamics model. Experiment results shows the position and pose measurement based on vision techniques is consistent with IMU measurement and does not show long term drifting error compared to IMU measurement.

Highlights Measurement of canopy light interception data using a ground-based mobile system. Using UAV-captured aerial images and zenith angle to estimate canopy light interception at different times of the day. Identifying boundaries of individual trees using the maximum likelihood estimator and the watershed algorithm. Abstract. Photosynthetically Active Radiation (PAR) absorbed by the leaves is a key piece of information to study the crop response to environmental conditions that could be used to estimate crop production potential. Canopies in a commercial orchard present differences in their capability to intercept light mainly due to the spatial variability in canopy development. There is a need for developing tools that could capture spatial variability in PAR interception to predict potential yield. Unmanned Aerial Vehicles (UAV) present an interesting alternative to provide this information, as they cover a larger area than ground-based systems in a shorter period with high spatial resolution. The objective of this study was to determine the relationship between the shadow of a tree derived from a ground-based canopy light interception scan obtained using a lightbar mounted on a mobile platform and that acquired from UAV Red-Green-Blue (RGB) images. Information acquired by an UAV was classified to separate canopy from its shadow, grass and sunlit soil using maximum likelihood estimator. Boundaries of individual trees were identified based on their positions using watershed transform algorithm. The relationship between canopy PAR interception data, sun angle in the sky (zenith angle), and the information derived from aerial images was analyzed. Coefficient of determination (R2) values of 0.92 and 0.88 were found for the multiple linear regression between PAR, the shadow area and the cosine of zenith angle obtained for almond and walnut crops, respectively. Moreover, R2 values of 0.81 and 0.86 were found for the relationship between the shadow’s area obtained underneath the canopy and the shadow’s area obtained from the UAV images and the cosine of the zenith angle for almond and walnut crops, respectively. The results show that the PAR interception can be estimated using the zenith angle and the area of the shadow, which can be obtained from a RGB aerial image. Keywords: Almond, Canopy segmentation, Image classification, PAR interception, Shadow area, UAV, Walnut.

This paper presents a new approach to estimate the location and height of the obstacle located between two unmanned aerial vehicles (UAVs) using Received Signal Strength Indication (RSSI) observations. The goal of developing this approach is to improve the localization of a radio frequency (RF) source by estimating the effect of obstacles on the signal attenuation. The effect of an obstacle on signal strength attenuation is the most important source of error in distance estimation based on general or empirical path loss model. Therefore, mapping the primary obstacle, which has the greatest effect on the signal attenuation, can improve the distance estimation. The main idea of the proposed approach is in the distinction between the effects of obstacle(s) on the signal attenuation, i.e. the diffraction loss, and the path loss. The proposed approach uses a model of diffraction loss to estimate the height and position of the primary knife-edge obstacle. The observations include the diffraction losses which are collected on the UAV's paths. Due to the Gaussian distribution of the diffraction loss observations and nonlinearity of the observation function, extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter are implemented and compared with each other. The results of the simulations show that the proposed approach is able to map an obstacle between two RF sources. Furthermore, the detected and estimated obstacle can be used for better RF source localization.

Heuristic position allocation methods for forming multiple UAV formations

We present a vision system based on a monocular camera to track the 3D position and orientation of an Unmanned Aerial Vehicle (UAV) during the landing process aboard a ship. The proposed method uses a 3D model-based approach based on a Particle Filter (PF) with proposal distributions given by an Unscented Kalman Filter (UKF) for the translational motion and filters based on directional statistics for the rotational motion. Our main contributions are (i) the development of a novel 3D model-based tracking architecture based on directional statistics that can be easily adapted to other tracking problems, and (ii) the development of the Unscented Bingham-Gauss Filter (UBiGaF) for rotation estimation. We show the advantages of using directional statistics based filters on 3D model-based tracking in a series of quantitative tests in a challenging simulation scenario with real video data. The obtained position and angular error are compatible with the automatic landing system requirements when using directional statistics. We obtain lower error when using the UBiGaF scheme for the vast majority of the tested combinations.

In some complex applications, Flying Ad Hoc Network (FANET) can provide important support for multi UAV (Unmanned Aerial Vehicle) cooperation. In FANET each UAV is equivalent to a router, and the wireless link between them forms a network to achieve the purpose of relay communication. Throughput is an important network performance, and the position of UAV nodes affects it. In this paper, we analyze the influencing factors of FANET throughput with UAV position and terminator selection in first; Secondly we construct the mathematical model of throughput optimization of FANET; Thirdly we propose an algorithm based on genetic algorithm to optimize the position of UAV, and then maximize the throughput. Preparing for using genetic algorithm, we design the related details: Numbering Area, Determining the adjacency matrix and correlation matrix, determining the range of UAV node position movement. The key points of the genetic algorithm for FANET is proposed include the following aspects: coding and population initialization, fitness function, and chromosome replication/crossover/mutation and termination criteria. At last, Matlab is used to simulate the proposed algorithm from three aspects: performance, effect of Radius of Position Constraint (RPC) and effect of Radius of Particle Size (RPS). The results show that the throughput can reach the expected goal by controlling the UAV position, and the optimization speed is related to the RPC and RPS.

Unmanned Aerial Vehicles (UAVs), also known as drones, are often considered the solution to complex robotics problems. The significant freedom to explore an environment is a major reason why UAVs are a popular choice for automated solutions. UAVs, however, have a very limited flight time due to the low capacity and weight ratio of current batteries. One way to extend the vehicles' flight time is to use a tether to provide power from external batteries, generators on the ground, or another vehicle. Attaching a tether to a vehicle may constrain its navigation but it may also create some opportunities for improvement of some tasks, such as landing. A tethered UAV can still explore an environment, but with some additional limitations: the tether can become wrapped around or bent by an obstacle, stopping the drone from traveling further and requiring backtracking to undo; the tether can fall loose and get caught while dragging on the ground; or the base of the tether could be mobile and the UAV needs to have a way to return to it. Most issues, like those listed above, could be solved with a vision system and various kinds of markers, but this approach could not work in situations of low light, where cameras are no longer effective. In this project, a state machine was developed to land a tethered, vertical take-off and landing (VTOL) UAV using only angles taken from both ends of the tether, the tension in the tether, and the height of the UAV. The main scenarios focused on in this project were normal operation, obstacle interference, loose tether, and a moving base. Normal operation is essentially tether guidance using the tether as a direction back to the base. The obstacle case has to determine the best action for untangling the tether. The loose tether case has to handle the loss of information given by the angle sensors, as the tether direction is no longer available. This case is performed as a last-ditched effort to find the landing pad with only a moderate chance for success. Lastly, the moving base case uses the change in the angles over time to determine the speed needed to reach the base. The software was not the only focus of this project. Two hardware components of this project were a landing platform and a matching landing gear to support the landing process. These two components were designed to

Currently, the study of unmanned aerial vehicle (UAV) in complex environments is relatively well developed and has many achievements. However, the fact that the landing process of UAVs in complex or unknown environments is a significant challenge for scientists. In this paper, the problem on how to determine the desired landing orbit will be solved. The approach and landing process might be constructed from the straight lines and orbit curves. This study indicates that the UAV has four landing trajectories because the UAV can turn left or right to reach the left or right circle in the landing process. The calculation is based on solving aircraft equations of motion and analytical method. This paper also shows the methodologies to determine the landing routes that the UAV can be landing in any direction or given direction. The simulation results utilising MATLAB software show that the calculated landing orbit is the shortest.

Flying vehicle’s navigation, direction, and control in real‐time results in the design of a strap‐down inertial navigation system (INS). The strategy results in low accuracy, performance with correctness. Aiming at the attitude estimation problem, many data fusion or filtering methods had been applied, which fail in many cases, which attains the nonlinear measurement model, process dynamics, and high navigation range. The main problem in unmanned aerial vehicles (UAVs) and flying vehicles is the determination of attitude angles. A novel attitude estimation algorithm is proposed in this study for the unmanned aerial vehicle (UAV). This research article designs two filtering algorithms for fixed‐wing UAVs which are nonlinear for the attitude estimation. The filters are based on Kalman filters. The unscented Kalman filter (UKF) and cubature Kalman filter (CKF) were designed with different parameterizations of attitude, i.e., Euler angle (EA) and INS/unit quaternion (UQ) simultaneously. These filters, EA‐UKF and INS‐CKF, use the nonlinear process and measurement model. The computational results show that among both filters, the CKF attains a high accuracy, robustness, and estimation for the attitude estimation of the fixed‐wing UAV.

This paper discusses the algorithms of vision systems for automatic UAV (Unmanned Aerial Vehicle) landing. The basic algorithms for finding helipad and adapting the UAV control system to an autonomous UAV landing are presented. The authors consider algorithms to solve navigation problems, building block diagrams of automatic UAV control. Also, the developed algorithms make it possible to implement an automatic landing system for UAVs using technical vision systems, using various camera parameters, algorithms allowing research, as part of the autonomous navigation of unmanned aerial vehicles. The article discusses the developed algorithm that allows you to highlight certain characteristic points for visual navigation. Also, a search system for characteristic points has been developed that allows automatic UAV landing. These algorithms may be useful for the autonomous landing UAV systems. Algorithms can also be used to track a trajectory of the UAV system.

A multiple model based Unscented Kalman Filter (MMUKF) approach for position tracking of CNC servo drives is presented in this paper. The motion controller in the CNC machine has to generate motion profile to drive servo motor based on the feed back from the Encoder. The output of the feed back is affected by measurement variations due to friction and non linear behavior of tool motion. The unscented kalman filter(UKF)gives better results in non-linear motion applications by deterministic sampling of sigma points. The MM algorithm gives good estimate by combining the individual estimates of parallel filters matched to different motion models of the CNC Tool. In this paper, an Unscented Kalman Filter is used inside the multiple model algorithm to improve the estimation accuracy of tool motion at different dynamic regions of motion profile. The motion profile of tool is simulated by including fixed velocity and turn model and the MM based UKF gives better results compared to traditional kalman filters.

This paper considers the problem of tracking a moving target using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained at two unmanned aerial vehicles (UAVs) with varying baseline. Accumulation time is necessary because the emitter position cannot be estimated at each emission when there are only two UAVs as sensors. And the target position and velocity estimation accuracy often suffer from low convergence in conventional formation flight mode no matter what nonlinear filtering algorithm is used. Based on the analysis of the influence of different flight modes on the positioning performance, a moving target tracking system from two UAVs with varying baseline is proposed. The performance of unscented Kalman filter (UKF) under proposed system is analysed and compared with the Cramer-Rao lower bound (CRLB). Simulation results show that the proposed system can speed up the convergence.

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.