Range-based collaborative relative navigation for multiple unmanned aerial vehicles using consensus 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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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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Tag-based visual-inertial localization of unmanned aerial vehicles in indoor construction environments using an on-manifold extended Kalman filter

Automation in Construction ◽

10.1016/j.autcon.2021.104112 ◽

2022 ◽

Vol 135 ◽

pp. 104112

Author(s):

Navid Kayhani ◽

Wenda Zhao ◽

Brenda McCabe ◽

Angela P. Schoellig

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Extended Kalman Filter ◽

Aerial Vehicles

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Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles

Remote Sensing ◽

10.3390/rs13081483 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1483

Author(s):

Yuan Sun

Keyword(s):

Unmanned Aerial Vehicles ◽

Real Data ◽

Wide Band ◽

Relative Navigation ◽

Monitoring Method ◽

Integrity Monitoring ◽

Position Information ◽

Aerial Vehicles ◽

Multiple Unmanned Aerial Vehicles ◽

Multi Uav

Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.

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