scholarly journals Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

AVITEC ◽  
2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Freddy Kurniawan ◽  
Muhammad Ridlo Erdata Nasution ◽  
Okto Dinaryanto ◽  
Lasmadi Lasmadi
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Navigation System ◽  
Unmanned Aircraft ◽  
Average Error ◽  
Accelerometer Data ◽  
Static State ◽  
Dynamic Conditions ◽  
Measurement Noise Covariance ◽  
Magnetometer Data

In an unmanned aircraft vehicle, a navigation system is needed to calculate its orientation and translation. The navigation system can utilize data from the accelerometer, gyroscope, magnetometer, and GPS. The orientation can be precisely calculated from the accelerometer and magnetometer data when the sensor is in a static state. Meanwhile, under dynamic conditions, the orientation can be more precisely calculated from the gyroscope data. In order to obtain the robust navigation system, a data fusion based on Kalman filter is built to calculate the orientation from the accelerometer, gyroscope, and magnetometer. The Kalman filter trusts more in the data from the accelerometer and magnetometer when the UAV is static and trusts more in to the gyroscope data when the UAV is in dynamic conditions. Meanwhile, the UAV translation is obtained by performing data fusion of the accelerometer data with location data from the GPS sensor. The Kalman filter combines data from the accelerometer and GPS when available, otherwise trusts in data from the accelerometer only. The trust level shifting is done by changing the measurement noise covariance. The data fusion based on Kalman filter provides more accurately the orientation and translation data. The orientation as a result of the calculation from the gyroscope has an average error of 18.12%, while the orientation as a result of the accelerometer and magnetometer has an error of 1.3%. By using Kalman filter-based data fusion, the error of the orientation decreases to 0.87%

A Review of Multisensor Fusion Methodologies for Aircraft Navigation Systems

2005 ◽  
Vol 58 (3) ◽  
pp. 405-417 ◽  
Author(s):  
David J. Allerton ◽  
Huamin Jia
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Navigation System ◽  
Review Paper ◽  
Fault Tolerant ◽  
Navigation Systems ◽  
System Architectures ◽  
Advantages And Disadvantages ◽  
Aircraft Navigation ◽  
Fusion Methods

This paper reviews currently existing fault-tolerant navigation system architectures and data fusion methods used in the design and development of integrated aircraft navigation systems and also compares their advantages and disadvantages. Four fault-tolerant navigation system architectures are reviewed and the associated Kalman filter architectures and algorithms are discussed. These techniques have been used in most integrated aircraft navigation systems. The aim of this review paper is to provide a guide for navigation system designers to develop future aircraft multisensor navigation systems.

Multi-Sensor Data Fusion for a Tethered Unmanned Helicopter Using a Square-Root Unscented Kalman Filter

2016 ◽  
Vol 04 (04) ◽  
pp. 273-287
Author(s):  
Luis A. Sandino ◽  
Manuel Bejar ◽  
Konstantin Kondak ◽  
Anibal Ollero
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Process Model ◽  
Position Estimation ◽  
Unmanned Aircraft ◽  
Sensor Data ◽  
Unmanned Helicopter ◽  
Sensor Data Fusion ◽  
Filter Performance ◽  
Multi Sensor Data Fusion

The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. This work addresses the attitude and position estimation of a small-size unmanned helicopter tethered to a moving platform using a multi-sensor data fusion algorithm based on a numerically efficient sigma-point Kalman filter implementation. 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, barometric altimeter, LIDAR altimeter and magnetic encoders measuring the tether orientation relative to the helicopter. Assuming the tether is kept taut by an actuated device on the platform during the system operation, the helicopter position is estimated relative to the anchor point. Although this configuration avoids the need of a GPS, a standard operation mode for estimation of the absolute position (the position relative to the inertial reference frame) incorporating corrections with the GPS position and velocity measurements, is also implemented in order to highlight the benefits of the proposed tethered setup. The filter performance is evaluated in simulations.

Design and Research of Quadcopter Navigator

2014 ◽  
Vol 635-637 ◽  
pp. 1220-1223 ◽  
Author(s):  
Zi Jian Yang ◽  
Cong Sun
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Location Information ◽  
Integrated Navigation ◽  
Height Measurement ◽  
Complementary Filter ◽  
Ultrasonic Ranging ◽  
Navigation Equipment

This paper studies the autonomous navigation equipment of Quadcopter. SINS/GPS integrated navigation technology, ultrasonic ranging and barometric altimeter is used in the autonomous navigation system. Navigation board includes measuring height subsystem and measurement position subsystem. The former includes an inertial navigation module and GPS module using the Kalman filter for data fusion, which gets a more accurate and stable location information. Quadcopter height measurement subsystem, using ultrasound and barometers. Complementary filter for effective data fusion, is used to ensure the reliability of height measurement.

An Adaptive & Fault Tolerant SINS/GPS Integrated Navigation Scheme Robustified against Slowly Growing Errors in GPS Updates

2013 ◽  
Vol 390 ◽  
pp. 500-505 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng ◽  
Jamshaid Ali
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Random Errors ◽  
Actual Measurement ◽  
Integrated Navigation System ◽  
System Noise ◽  
Measurement Noise Covariance ◽  
Noise Covariance

The Strapdown Inertial Navigation System (SINS) renders excellent attitude, position and velocity solutions on short term basis, but when used as stand-alone navigation system, its accuracy deteriorates with the passage of time. On the other hand GPS has long-standing stability with a consistent precisiongenerally having only bounded random errors in position and velocity. Integrated navigation system is used to augment the complementary features of SINS and GPS. In integrated navigation system external fixes for position and/or velocity and/or attitude are used to contain the growing errors of SINS. Kalman filter is generally used as integration tool for integrated navigation system. Kalman filter algorithm is based on the assumptions that the system model and the measurement models are linear and the system random errors and measurement random errors are Gaussian in nature expressed with fixed covariances. But in real navigation systems these assumptions are seldom fulfilled and hence Kalman filter renders unsatisfactory results. Adaptive Kalman filter provides the solution to the problem by adjusting the system noise covariance and measurement noise covariance in real time in the light of actual measurement errors or actual dynamics of thevehicle. In this paper an innovation and residual based adaption of measurement noise covariance and system noise covariance is presented. The presented scheme has been applied on an SINS/GPS Integrated Navigation Systemand it has been validated that the scheme provide significantly better results as compared to standard Kalman filter on occurrence slowly growing errors as well as excessive random errors in GPS measurements.

Research and Design of MEMS SINS/GPS Integrated Navigation System Based on Adaptive Filter

2013 ◽  
Vol 341-342 ◽  
pp. 1048-1052
Author(s):  
Gao Wei Zhang ◽  
Xiao Yu Zhang ◽  
Chun Lei Song ◽  
Ting Ting Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Low Cost ◽  
Integrated Navigation ◽  
Variance Matrix ◽  
Integrated Navigation System ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Research And Design

A MIMU/GPS integrated navigation system principle prototype is designed, and the structure of the system is introduced by different module. To handle the influence of Kalman filter parameters on system filtering performance (Including the system noise variance matrix Q and measurement noise covariance matrix R), adaptive estimation Kalman filter is designed. The test results show that satisfactory performance can be obtained using adaptive estimation techniques for the low-cost MIMU/GPS integrated navigation.

scholarly journals Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Melinda G. Conners ◽  
Théo Michelot ◽  
Eleanor I. Heywood ◽  
Rachael A. Orben ◽  
Richard A. Phillips ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Flapping Flight ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Fine Scale ◽  
Magnetometer Data ◽  
Behavioral Classification ◽  
Soaring Flight

AbstractBackgroundInertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective  classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.MethodsWe deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.ResultsHMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.ConclusionsThe use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

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