scholarly journals Effect of Strapdown Integration Order and Sampling Rate on IMU-Based Attitude Estimation Accuracy

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2775 ◽  
Author(s):  
Jung Lee ◽  
Mi Choi
Keyword(s):  
Low Cost ◽  
Sampling Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Third Order ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
First Order ◽  
Noise Covariance ◽  
Integration Order

This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).

scholarly journals A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1340 ◽  
Author(s):  
Xudong Wen ◽  
Chunwu Liu ◽  
Zhiping Huang ◽  
Shaojing Su ◽  
Xiaojun Guo ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Low Cost ◽  
Attitude Estimation ◽  
Sensor Data ◽  
Processing Algorithm ◽  
Estimation Accuracy ◽  
First Order ◽  
Data Processing Algorithm ◽  
Adjustment Strategy

There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

Online evaluation of the process noise covariance matrix for event‐based state estimators

2019 ◽  
Vol 120 (2) ◽  
pp. 195-208
Author(s):  
Miguel Martínez‐Rey ◽  
Carlos Santos ◽  
Rubén Nieto ◽  
Cristina Losada ◽  
Felipe Espinosa
Keyword(s):  
Covariance Matrix ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Online Evaluation ◽  
State Estimators ◽  
Noise Covariance ◽  
Event Based

scholarly journals A System for Tracking an Autonomously Controlled Canine

2012 ◽  
Vol 65 (3) ◽  
pp. 427-444 ◽  
Author(s):  
Jeff Miller ◽  
George Flowers ◽  
David Bevly
Keyword(s):  
Fuzzy Logic Controller ◽  
Gps Receiver ◽  
Process Noise ◽  
Embedded Control ◽  
Noise Covariance Matrix ◽  
Adaptive Tuning ◽  
Noise Characteristics ◽  
Noise Covariance ◽  
Motion Characteristics ◽  
Tone Generation

This paper presents an approach for outdoor navigation of an autonomously guided canine using an embedded command module with vibration and tone generation capabilities and an embedded control suite comprised of a microprocessor, wireless radio, GPS receiver, and an Attitude and Heading Reference System. In order to determine the canine's motions, which inherently contain non-conventional noise characteristics, the sensor measurements were integrated using a specialized Extended Kalman Filter (EKF), equipped with a Fuzzy Logic controller for adaptive tuning of the Process Noise Covariance Matrix. This allowed for rejection of un-modelled canine motion characteristics which tend to corrupt accelerometer bias tracking in a standard EKF. The EKF solution provided an optimized estimate of the canine position and velocity and also proved to be effective in tracking the canine's position (within 7·5 m) and velocity (within 1·2 m/s) during simulated 10 second GPS outages.

scholarly journals Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3241 ◽  
Author(s):  
Haonan Jiang ◽  
Yuanli Cai
Keyword(s):  
Information Filtering ◽  
Estimation Procedure ◽  
Estimation Accuracy ◽  
Selection Strategy ◽  
Bayesian Filtering ◽  
Process Noise ◽  
Covariance Matching ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
Noise Covariance

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

Kalman Filter and Complementary Filter in Attitude Estimation of APM

2014 ◽  
Vol 577 ◽  
pp. 794-797 ◽  
Author(s):  
Feng Lin ◽  
Xi Lan Miao ◽  
Xiao Guang Qu
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Attitude Estimation ◽  
Measurement Noise ◽  
Experimental Results ◽  
Extend Kalman Filter ◽  
Noise Covariance Matrix ◽  
Complementary Filter ◽  
Measurement Noise Covariance ◽  
Noise Covariance

This paper presents the results of a quaternion based extend Kalman filter (EKF) and complementary filter for ArduPilotMega (APM) attitude estimation. In addition, a new method to get the measurement noise covariance matrix R is proposed. Experimental results show that the two algorithms can meet the requirements, but the complementary filter can yield better performance than EKF.

Soft Measurement Technology for Tractor-Semi Trailer

2014 ◽  
Vol 494-495 ◽  
pp. 821-824
Author(s):  
Chao Yi Wei ◽  
Xu Guang Li ◽  
Mei Zhi Xie ◽  
Feng Yan Yi
Keyword(s):  
Covariance Matrix ◽  
Estimation Accuracy ◽  
Slip Angle ◽  
Measurement Technology ◽  
Noise Covariance Matrix ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Set Up ◽  
Three Degree Of Freedom ◽  
Vehicle Dynamic Model

A three degree of freedom vehicle dynamic model of a tractor semi trailer is established and a simulation mode is set up with the Matlab/simulink software. Then, design a Kalman state estimator, using easily measured parameters to estimate the tractor side slip angle, trailer yaw rate those are difficult measured or measuring in high cost; the simulation show that: the estimated value coming from estimator agree well with the simulation measurements, and can improve the estimation accuracy by adjusting the process noise covariance matrix and measurement noise covariance matrix.

scholarly journals RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles

2020 ◽  
Vol 12 (11) ◽  
pp. 1704
Author(s):  
Xile Gao ◽  
Haiyong Luo ◽  
Bokun Ning ◽  
Fang Zhao ◽  
Linfeng Bao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Navigation System ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Integrated Navigation System ◽  
Navigation Algorithm ◽  
Noise Covariance

Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.

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