Drone Tracking Using an Innovative UKF

2020 59th IEEE Conference on Decision and Control (CDC), 2020

Accurate localisation of unmanned aerial vehicles is vital for the next generation of automation tasks. This paper proposes a minimum energy filter for velocity-aided pose estimation on the extended special Euclidean group. The approach taken exploits the Lie-group symmetry of the problem to combine Inertial Measurement Unit (IMU) sensor output with landmark measurements into a robust and high performance state estimate. We propose an asynchronous discretetime implementation to fuse high bandwidth IMU with low bandwidth discrete-time landmark measurements typical of real-world scenarios. The filter's performance is demonstrated by simulation. I. INTRODUCTION Unmanned aerial vehicles (UAV) are an increasingly important technology in modern society with applications in photography, logistical deliveries, mapping, inspection tasks, etc. Accurate estimation of a vehicle's position and orientation (pose) is critical for the new generation of applications of such vehicles. Most pose estimation algorithms for aerial vehicles depend heavily on Global Navigation Satellite Systems (GNSS) to provide ground truth on position [1]. However, GNSS signals become unreliable and fail entirely in urban canyons, forest environments, or any situation where direct reception of the satellite signal is compromised [1]. This has focused attention on the development of algorithms that use exteroceptive sensors such as vision and lidar to provide position information [2]-[5]. Historically, the most successful state estimation algorithms for UAV systems have been derived from stochastic principles. Early work on attitude estimation built upon the Extended Kalman Filter (EKF), typically representing the system state using Euler angles [6]. Markley's Multiplicative EKF (MEKF) [7] introduced a quaternion attitude representation and exploited group multiplication to propagate error estimates. Recently, Filipe et al. [8] extended this to pose estimation. Bonnabel et al. [9] proposed a general filtering framework for Lie-groups, introducing the Invariant Extended Kalman Filter (IEKF) [10]. This framework has lead to applications in velocity aided attitude estimation [11], visual odometry and Simultaneous Localisation and Mapping (SLAM) [3], [12]. An alternative to the stochastic approach is

AIAA Guidance, Navigation, and Control (GNC) Conference, 2013

2007

This chapter considers the application of Extended Kalman Filtering (EKF) towards Unmanned Aerial Vehicle (UAV) Identification. A 3 Degree-Of-Freedom (DOF), coupled, attitude dynamics is considered for formulating the mathematical model towards numerical simulation as well as Hardware-In-Loop (HIL) tests. Results are compared with those obtained from a state-space estimation method known as the Error Mapping Identification (EMId) in the context of processing power and computational load. Furthermore the statistical health of the EKF during the estimation process is analysed in terms of covariance propagation and rejection of anomalous sensor data.

IEEE Transactions on Industrial Electronics, 2012

A main problem in autonomous vehicles in general, and in Unmanned Aerial Vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Three Axis Attitude Determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the Extended Kalman Filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller.

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2022

This research presents the method to improve the robustness of indoor UAV localization via fusion of visual SLAM and Lidar SLAM with Extended Kalman Filter (EKF). The visual and Lidar SLAM methodologies are applied to compensate for different pose errors in various situations, such as various lighting and reflection, respectively. In the experiment, Lidar and a stereo camera with SLAM methods are installed on the drone. When starting SLAM both methods will localize and provide position and orientation data. The data will be fused by Extended Kalman Filter and provides updated data. Therefore, if there is an error in either of the SLAM methods, the system will continue to work properly. In the test, the drone was conducted in various situations where the drone used to have an error using both SLAM. A result shows that the data obtained from the EKF remains normal in various situations.

Periodicals of Engineering and Natural Sciences (PEN)

In recent years, using Unmanned Arial Vehicles (UAV) like quadcopter in civilian and military fields are increased dramatically. Performance and robustness are the most important specifications required for most applications. Different sensors are usually used for a quadcopter to provide the necessary measured states (attitude and position) for control. The white noise generated by physical sensors is one of the important issues that affect the quality of states measurements. The available solutions are still have limited performance for a wide range of nonlinearity. In this paper, Unscented Kalman Filter (UKF) is proposed as a robust estimator that has the ability to work efficiently with high nonlinear systems. Modified PID (PI-D) controller which has better properties than traditional PID controller is used with proposed filter in order to get better performance of quadcopter. The obtained results are compared with that of Extended Kalman Filter (EKF) and proved to be more reliable. Moreover, the results show that the proposed filter largely decreases the error generated by noise and improves the performance of quadcopter better than the EKF.

Proceedings of the 2015 International Conference On Unmanned Aircraft Systems (ICUAS15, Denver, USA), 2015

The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. In this work a numerically efficient implementation of a sigma-point Kalman filter is applied to the attitude and relative position estimation of a small-size tethered unmanned helicopter. For that purpose, the state prediction is performed using a kinematic process model driven by measurements of the inertial sensors (accelerometer and gyroscope) onboard the helicopter and the subsequent correction is done using information from additional sensors like magnetometer, radar altimeter and magnetic encoders measuring the tether orientation relative to the helicopter. Assuming the tether is kept taut by an actuated device on ground during the system operation, this approach avoids the need of a global positioning system (GPS) as the position is estimated relative to the anchor point. The filter performance is evaluated in simulations

Aerospace

In this paper, we propose a nonlinear tracking solution for maneuvering aerial targets based on an adaptive interacting multiple model (IMM) framework and unscented Kalman filters (UKFs), termed as AIMM-UKF. The purpose is to obtain more accurate estimates, better consistency of the tracker, and more robust prediction during sensor outages. The AIMM-UKF framework provides quick switching between two UKFs by adapting the transition probabilities between modes based on a distance function. Two modes are implemented: a uniform motion model and a maneuvering model. The experimental validation is performed with Monte Carlo simulations of three scenarios with ACAS Xa tracking logic as a benchmark, which is the next generation of airborne collision avoidance systems. The two algorithms are compared using hypothesis testing of the root mean square errors. In addition, we determine the normalized estimation error squared (NEES), a new proposed noise reduction factor to compare the estimation...

Iraqi Journal for Electrical and Electronic Engineering, 2020

The gyroscope and accelerometer are the basic sensors used by most Unmanned Aerial Vehicle (UAV) like quadcopter to control itself. In this paper, the fault detection of measured angular and linear states by gyroscope and accelerometer sensors are present. Uncertainties in measurement and physical sensors itself are the main reasons that lead to generate noise and cause the fault in measured states. Most previous solutions are process angular or linear states to improving the performance of quadcopter. Also, in most of the previous solutions, KF and EKF filters are used, which are inefficient in dealing with high nonlinearity systems such as quadcopter. The proposed algorithm is developed by the robust nonlinear filter, Unscented Kalman Filter (UKF), as an angular and linear estimation filter. Simulation results show that the proposed algorithm is efficient to decrease the effect of sensors noise and estimate accurate angular and linear states. Also, improving the stability and perf...

2008 9th International Conference on Signal Processing, 2008

An extended target tracking problem for high resolution sensors is considered. An ellipsoidal model is proposed to exploit sensor measurement of target extent, which can provide extra information to enhance tracking accuracy, data association performance, and target identification. Due to the presence of high nonlinearity of the model, a Rao-Blackwellised unscented Kalman filter (RBUKF) is adopted in this paper. In contrast to the most commonly used extended Kalman filter (EKF), the RBUKF provides more accurate and reliable estimation performance, without increasing any computational complexity. An interacting multiple model (IMM) technique is combined with the RBUKF method to adapt the target maneuver and motion mode switching problem which is vital for nonlinear filtering. The developed IMM-RBUKF algorithm on extended target tracking problem is validated and evaluated by computer simulations.