scholarly journals Sistem Navigasi Quadrotor Berbasis IMU dengan Kalman Filter

ELKHA ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Lasmadi Lasmadi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Measurement Accuracy ◽  
State Space Model ◽  
System Model ◽  
Test Results ◽  
Position Measurement ◽  
Filter Model ◽  
Space Model ◽  
Navigation Data

The navigation system on quadrotor is important to maintain stability and determine its own position when flying autonomously. The GPS can provide the position measurement, but it has limitations in the specific environments and cannot provide the orientation information. This study aims to design the navigation system for quadrotor based on IMU sensor with Kalman filters using the state space model. The system model was developed using Matlab software. Kalman filter is designed to estimate the navigation data and eliminate noise on the sensor so that it can improve the measurement accuracy. The test results showed that the system model can provide orientation estimation and translation estimation of the quadrotor, while the Kalman filter model is acceptable to reduce noise on the sensor's raw data. When tested indoors, the system can provide the measurement accuracy above 90%.

scholarly journals Interval Kalman Filtering in Navigation System Design for an Uninhabited Surface Vehicle

2013 ◽  
Vol 66 (5) ◽  
pp. 639-652 ◽  
Author(s):  
A Motwani ◽  
SK Sharma ◽  
R Sutton ◽  
P Culverhouse
Keyword(s):  
Kalman Filter ◽  
System Design ◽  
State Space ◽  
Kalman Filtering ◽  
Navigation System ◽  
Potential Application ◽  
State Space Model ◽  
Space Model ◽  
Heading Angle ◽  
Modelling Assumptions

This paper reports on the potential application of interval Kalman filtering techniques in the design of a navigation system for an uninhabited surface vehicle namedSpringer. The interval Kalman filter (IKF) is investigated for this task since it has had limited exposure for such usage. A state-space model of theSpringersteering dynamics is used to provide a framework for the application of the Kalman filter (KF) and IKF algorithms for estimating the heading angle of the vessel under erroneous modelling assumptions. Simulations reveal several characteristics of the IKF, which are then discussed, and a review of the work undertaken to date presented and explained in the light of these characteristics, with suggestions on potential future improvements.

scholarly journals The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

2021 ◽  
Vol 11 (11) ◽  
pp. 5244
Author(s):  
Xinchun Zhang ◽  
Ximin Cui ◽  
Bo Huang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Measurement Accuracy ◽  
Railway Track ◽  
Filter Method ◽  
Integrated Navigation ◽  
High Speed Railway ◽  
Track Geometry ◽  
Integrated Navigation System

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

scholarly journals Variational Bayesian-Based Improved Maximum Mixture Correntropy Kalman Filter for Non-Gaussian Noise

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 117
Author(s):  
Xuyou Li ◽  
Yanda Guo ◽  
Qingwen Meng
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
State Space Model ◽  
Performance Evaluations ◽  
Gaussian State ◽  
Variational Bayesian ◽  
Space Model ◽  
Bandwidth Parameter ◽  
Non Gaussian ◽  
Filtering Problem

The maximum correntropy Kalman filter (MCKF) is an effective algorithm that was proposed to solve the non-Gaussian filtering problem for linear systems. Compared with the original Kalman filter (KF), the MCKF is a sub-optimal filter with Gaussian correntropy objective function, which has been demonstrated to have excellent robustness to non-Gaussian noise. However, the performance of MCKF is affected by its kernel bandwidth parameter, and a constant kernel bandwidth may lead to severe accuracy degradation in non-stationary noises. In order to solve this problem, the mixture correntropy method is further explored in this work, and an improved maximum mixture correntropy KF (IMMCKF) is proposed. By derivation, the random variables that obey Beta-Bernoulli distribution are taken as intermediate parameters, and a new hierarchical Gaussian state-space model was established. Finally, the unknown mixing probability and state estimation vector at each moment are inferred via a variational Bayesian approach, which provides an effective solution to improve the applicability of MCKFs in non-stationary noises. Performance evaluations demonstrate that the proposed filter significantly improves the existing MCKFs in non-stationary noises.

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MODEL TIME VARYING PARAMETER (TVP)

2016 ◽  
Vol 5 (3) ◽  
pp. 117
Author(s):  
I PUTU GEDE DIAN GERRY SUWEDAYANA ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Percentage Error ◽  
Time Varying ◽  
Mean Square ◽  
Explanatory Variables ◽  
Space Model ◽  
A Value ◽  
Time Varying Parameter ◽  
Kalman Filter Algorithm

The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP) model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE) is equal to dan root mean square percentage error (RMSPE) is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016).

scholarly journals A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1596 ◽  
Author(s):  
Xin Zhao ◽  
Haikun Wei ◽  
Chenxi Li ◽  
Kanjian Zhang
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
State Space ◽  
State Space Model ◽  
Hybrid Approach ◽  
Wind Farms ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Space Model ◽  
Wind Speeds

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

ASSESSING PRE-CRISIS FUNDAMENTALS IN SELECTED ASIAN STOCK MARKETS

2005 ◽  
Vol 50 (02) ◽  
pp. 175-196 ◽  
Author(s):  
EE LENG LAU ◽  
G. K. RANDOLPH TAN ◽  
SHAHIDUR RAHMAN
Keyword(s):  
Kalman Filter ◽  
Emerging Markets ◽  
State Space ◽  
Stock Prices ◽  
Stock Markets ◽  
Bubble Formation ◽  
State Space Model ◽  
Space Model ◽  
Rational Bubbles ◽  
Space Formulation

In the folklore of emerging markets, there is a popular belief that bubbles are inevitable. In this paper, our objective is to estimate a state-space model for rational bubbles in selected Asian economies with the aid of the Kalman Filter. For each economy, we derive a possible picture of the bubble formation process that is implied by the state-space formulation. The estimation is based on the rational valuation formula for stock prices. Our results provide a possible way of defining the presence of rational bubbles in the stock markets of Taiwan, Singapore, Korea, and Malaysia.

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