Tag-based visual-inertial localization of unmanned aerial vehicles in indoor construction environments using an on-manifold extended Kalman filter

For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario.

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Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

International Journal of Micro Air Vehicles ◽

10.1177/1756829317695564 ◽

2017 ◽

Vol 9 (3) ◽

pp. 169-186 ◽

Cited By ~ 33

Author(s):

Kexin Guo ◽

Zhirong Qiu ◽

Wei Meng ◽

Lihua Xie ◽

Rodney Teo

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Extended Kalman Filter ◽

Ultra Wideband ◽

Cooperative Localization ◽

Relative Localization ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Displacement Measurements

This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors’ states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

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Range-based collaborative relative navigation for multiple unmanned aerial vehicles using consensus extended Kalman filter

Aerospace Science and Technology ◽

10.1016/j.ast.2021.106647 ◽

2021 ◽

Vol 112 ◽

pp. 106647

Author(s):

Baichun Gong ◽

Sha Wang ◽

Mingrui Hao ◽

Xujun Guan ◽

Shuang Li

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Extended Kalman Filter ◽

Relative Navigation ◽

Aerial Vehicles ◽

Multiple Unmanned Aerial Vehicles

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Filter Kalman for solving the problem of coordinates unmanned aerial vehicles

«System analysis and applied information science» ◽

10.21122/2309-4923-2019-1-26-34 ◽

2019 ◽

pp. 26-34

Author(s):

N. N. Arefyev

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Extended Kalman Filter ◽

Utilization Efficiency ◽

Navigation Systems ◽

Distance Measurements ◽

Aerial Vehicles ◽

Filter Approach ◽

Noisy Measurements ◽

Uav Navigation

Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. Some miniaturized UAVs rely entirely on the global positioning system (GPS) for navigation. GPS is vulnerable to accidental or deliberate interference that can cause it to fail. It is not unusual, even in a benign environment, for a GPS outage to occur for periods of seconds to minutes. For UAVs relying solely on GPS for navigation such an event can be catastrophic. This article proposes an extended Kalman filter approach to estimate the location of a UAV when its GPS connection is lost, using inter-UAV distance measurements Increasing the accuracy of coordinate’s determination is one of the most crucial tasks of the modern UAV navigation. This task can be solved by using different variants of integration of navigation systems. One of the modern variants of integration is the combination of GPS/GLONASS-navigation with the extended Kalman filter, which estimates the accuracy recursively with the help of incomplete and noisy measurements. Currently different variations of extended Kalman filter exist and are under development, which include various number of variable states [1]. This article will show the utilization efficiency of extended Kalman filter in modern developments.

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Tracking a Maneuvering Target using AUV

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3026.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 292-296

Keyword(s):

Mathematical Modeling ◽

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Extended Kalman Filter ◽

Communication System ◽

Personal Communication ◽

Personal Communication System ◽

Maneuvering Target ◽

Aerial Vehicles ◽

Simulation Results

In this paper effort is made to track a maneuvering target using Unmanned Aerial Vehicles (UAV) with range, bearing and elevation measurements. Extended Kalman filter is preferred to processmeasurements tampered with noise. Algorithm to detect the maneuver of target is developed in this paper. This information about range, bearing and elevation is communicated to weapon guidance station by means of personal communication system between UAV and weapon guidance station. Mathematical modeling in detail and simulation results is presented.

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KALMAN FILTER IMPLEMENTATION FOR UNMANNED AERIAL VEHICLES NAVIGATION DEVELOPED WITHIN A GRADUATE COURSE

IFAC Proceedings Volumes ◽

10.3182/20050703-6-cz-1902.02265 ◽

2005 ◽

Vol 38 (1) ◽

pp. 12-17 ◽

Cited By ~ 3

Author(s):

Vasko Sazdovski ◽

Tatjana Kolemishevska-Gugulovska ◽

Mile Stankovski

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Graduate Course ◽

Aerial Vehicles

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A Nonlinear Double Model for Multisensor-Integrated Navigation Using the Federated EKF Algorithm for Small UAVs

Sensors ◽

10.3390/s20102974 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2974 ◽

Cited By ~ 3

Author(s):

Yue Yang ◽

Xiaoxiong Liu ◽

Weiguo Zhang ◽

Xuhang Liu ◽

Yicong Guo

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Short Distance ◽

Inertial Navigation System ◽

Experimental Tests ◽

Integrated Navigation ◽

Aerial Vehicles ◽

The Common ◽

Gaussian System ◽

Solution Accuracy

Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy.

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Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles

Aerospace Science and Technology ◽

10.1016/j.ast.2019.06.009 ◽

2019 ◽

Vol 92 ◽

pp. 446-463 ◽

Cited By ~ 13

Author(s):

H. Bonyan Khamseh ◽

S. Ghorbani ◽

F. Janabi-Sharifi

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Unmanned Aerial Vehicles ◽

Unscented Kalman Filter ◽

Aerial Vehicles

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EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels

Sensors ◽

10.3390/s19194174 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4174 ◽

Cited By ~ 2

Author(s):

Rodrigo Munguía ◽

Sarquis Urzua ◽

Antoni Grau

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Dynamic Models ◽

Model Parameters ◽

State Variables ◽

Test Bed ◽

Estimation Process ◽

Observability Analysis ◽

Aerial Vehicles ◽

Aerial Vehicle

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.

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