scholarly journals UAV attitude calculation algorithm based on acceleration correction model

2021 ◽  
Vol 39 (1) ◽  
pp. 175-181
Author(s):  
Xuhang Liu ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Yue Yang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Dynamic Environment ◽  
Calculation Model ◽  
Gravitational Acceleration ◽  
Calculation Algorithm ◽  
Correction Model ◽  
Attitude Angle ◽  
Aerial Vehicle ◽  
Uav Navigation

In order to improve the accuracy of attitude of unmanned aerial vehicle (UAV) navigation system in dynamic environment, an attitude calculation algorithm based on acceleration correction model is proposed. First, the acceleration correction model is established to calculate the estimated non-gravitational acceleration and external non-gravitational acceleration to modify the output value of the accelerometer, which reduces the influence of non-gravitational acceleration on the attitude calculation in dynamic environment. Then, the attitude calculation model based on Kalman filter is built, attitude angle calculated by corrected acceleration and magnetometer as measurement of filtering model, and the attitude calculation algorithm based on the acceleration correction model is designed. The experimental results show that the algorithm can reduce the interference of non-gravitational acceleration to attitude calculation, which avoids attitude angle divergence of UAV navigation system in dynamic environment, and improves the accuracy and anti-interference ability of UAV navigation system in dynamic environment.

Multirate Adaptive Kalman Filter for Marine Integrated Navigation System

2016 ◽  
Vol 70 (3) ◽  
pp. 628-647 ◽  
Author(s):  
Narjes Davari ◽  
Asghar Gholami ◽  
Mohammad Shabani
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Dynamic Environment ◽  
Statistical Information ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
Imperfect Knowledge ◽  
Marine Navigation

In the conventional integrated navigation system, the statistical information of the process and measurement noises is considered constant. However, due to the changing dynamic environment and imperfect knowledge of the filter statistical information, the process and measurement covariance matrices are unknown and time-varying. In this paper, a multirate adaptive Kalman filter is proposed to improve the performance of the Error State Kalman Filter (ESKF) for a marine navigation system. The designed navigation system is composed of a strapdown inertial navigation system along with Doppler velocity log and inclinometer with different sampling rates. In the proposed filter, the conventional adaptive Kalman filter is modified by adaptively tuning the measurement covariance matrix of the auxiliary sensors that have varying sampling grates based on the innovation sequence. The performance of the proposed filter is evaluated using real measurements. Experimental results show that the average root mean square error of the position estimated by the proposed filter can be decreased by approximately 60% when compared to that of the ESKF.

scholarly journals Indoor Unmanned Aerial Vehicle Navigation System Using LED Panels and QR Codes

2021 ◽  
Vol 33 (2) ◽  
pp. 242-253
Author(s):  
Hiroyuki Ukida ◽  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Navigation System ◽  
Indoor Environment ◽  
Flight Path ◽  
Qr Code ◽  
Qr Codes ◽  
Vehicle Navigation System ◽  
Aerial Vehicle ◽  
Uav Navigation ◽  
Navigation Method

In this study, we propose an unmanned aerial vehicle (UAV) navigation system using LED panels and QR codes as markers in an indoor environment. An LED panel can display various patterns; hence, we use it as a command presentation device for UAVs, and a QR code can embed various pieces of information, which is used as a sign to estimate the location of the UAV on the way of the flight path. In this paper, we present a navigation method from departure to destination positions in which an obstacle lies between them. In addition, we investigate the effectiveness of our proposed method using an actual UAV.

Analysis on the Influence of Random Vibration on MEMS Gyro Precision and Error Compensation

2011 ◽  
Vol 130-134 ◽  
pp. 4164-4168
Author(s):  
Xiang Yu Hao ◽  
Ming Li ◽  
Xue Feng Han ◽  
Hong Guang Jia
Keyword(s):  
Kalman Filter ◽  
Random Vibration ◽  
Low Cost ◽  
Dynamic Environment ◽  
Angle Error ◽  
Random Drift ◽  
Static Test ◽  
Attitude Angle ◽  
Aircraft Navigation ◽  
Calculation Results

In order to improve its precision in dynamic environment, a Kalman filter was designed. Firstly, two sets of random drift data of MEMS gyro were respectively analysed, and it was found that the variance of random drift under random vibration significantly increased and its mean also changed. Then calculation results show that attitude angle error under random vibration is 2.6°, while in the static test it is 0.25°. Analysis on the characteristics of random drift was carried out, and it is found that it can be treated as stable, normally distributed random signal. Finally, a corresponding Kalman filter was designed. The results indicated that after filtering the variance of random drift is reduced to 0.0282, 26.4% of pre-filtering and the attitude angle error is reduced to 1.5°, 57.7% of pre-filtering. The above method can effectively compensate for the attitude angle error of MEMS gyro caused by random vibration. This study can be a reference to the application of low-cost MEMS gyro in aircraft navigation.

The correction method of the aircraftastro-inertial system

2020 ◽  
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Navigation System ◽  
Control Algorithm ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Correction Method ◽  
Navigation Systems ◽  
Aerial Vehicle ◽  
Representation Method

The accuracy increasing problem of the unmanned aerial vehicle navigation complex in an algorithmic way is investigated. The algorithmic correction schemes of navigation systems for modern high-precision unmanned aerial vehicles are considered. The error compensating method of the inertial navigation system corrected by astro-system signals is proposed. Correction is carried out in the structure of the inertial navigation system using a non-linear Kalman filter and a control algorithm. A nonlinear control algorithm based on the SDC representation method is used. Keywords unmanned aerial vehicle; inertial navigation system; astro-system; navigation complex; nonlinear Kalman filter; error model; regulator; SDC method

scholarly journals Application and development trend of unmanned aerial vehicle navigation technology

2021 ◽  
Vol 336 ◽  
pp. 04006
Author(s):  
Haohan Bei ◽  
Yubo Liu ◽  
Wenhao Li ◽  
Ying Huang
Keyword(s):  
Automatic Control ◽  
Unmanned Aerial Vehicle ◽  
Navigation System ◽  
Development Trend ◽  
Practical Application ◽  
Relevant Research ◽  
Development Direction ◽  
Different Types ◽  
Aerial Vehicle ◽  
Uav Navigation

UAV (Unmanned Aerial Vehicle) equipment is novel because of its remote automatic control characteristics, so navigation technology is particularly critical. It is necessary to study and apply UAV technology. This paper first analyzes the UAV navigation technology, then the different types of UAV navigation system, and finally looks forward to the development direction of UAV navigation system, hoping that through this study, we can have a general understanding of the application and development trend of UAV navigation technology, and hope that the relevant research can be further enriched and improved to guide practical application.

Distributed Filtering-Based Autonomous Navigation System of UAV

2015 ◽  
Vol 03 (01) ◽  
pp. 17-34 ◽  
Author(s):  
Long Zhao ◽  
Ding Wang ◽  
Baoqi Huang ◽  
Lihua Xie
Keyword(s):  
Kalman Filter ◽  
Flight Control ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Vertical Channel ◽  
Positioning System ◽  
Vision Positioning ◽  
Aerial Vehicle ◽  
Sensor Information ◽  
Autonomous Navigation System

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

scholarly journals Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton

2021 ◽  
Vol 11 (4) ◽  
pp. 1373
Author(s):  
Jingyu Zhang ◽  
Zhen Liu ◽  
Guangjun Zhang
Keyword(s):  
Measurement Errors ◽  
Structural Characteristics ◽  
Geometric Model ◽  
Formation Flight ◽  
Depth Information ◽  
Feature Points ◽  
Feature Point Extraction ◽  
Aerial Vehicle ◽  
Uav Navigation ◽  
Rigid Skeleton

Pose measurement is a necessary technology for UAV navigation. Accurate pose measurement is the most important guarantee for a UAV stable flight. UAV pose measurement methods mostly use image matching with aircraft models or 2D points corresponding with 3D points. These methods will lead to pose measurement errors due to inaccurate contour and key feature point extraction. In order to solve these problems, a pose measurement method based on the structural characteristics of aircraft rigid skeleton is proposed in this paper. The depth information is introduced to guide and label the 2D feature points to eliminate the feature mismatch and segment the region. The space points obtained from the marked feature points fit the space linear equation of the rigid skeleton, and the UAV attitude is calculated by combining with the geometric model. This method does not need cooperative identification of the aircraft model, and can stably measure the position and attitude of short-range UAV in various environments. The effectiveness and reliability of the proposed method are verified by experiments on a visual simulation platform. The method proposed can prevent aircraft collision and ensure the safety of UAV navigation in autonomous refueling or formation flight.

Sign in / Sign up

Export Citation Format

Share Document