Extended Kalman Filter Based Quadrotor State Estimation Based on Asynchronous Multisensor Data

2015 ◽  
Author(s):  
Mohammad Sarim ◽  
Alireza Nemati ◽  
Manish Kumar ◽  
Kelly Cohen
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Extended Kalman Filter ◽  
Real World ◽  
Flight Test ◽  
Communication Delays ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Multisensor Data

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.

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

2017 ◽  
Vol 9 (3) ◽  
pp. 169-186 ◽  
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.

scholarly journals Filter Kalman for solving the problem of coordinates unmanned aerial vehicles

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.

scholarly journals Tracking a Maneuvering Target using AUV

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.

scholarly journals Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1385
Author(s):  
Yurong Feng ◽  
Kwaiwa Tse ◽  
Shengyang Chen ◽  
Chih-Yung Wen ◽  
Boyang Li
Keyword(s):  
System Design ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Visual Inspection ◽  
Spatial Information ◽  
Free Flight ◽  
Object State ◽  
Aerial Vehicles ◽  
Inspection Tasks ◽  
Autonomous Inspection

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.

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