A reliable adaptive estimation method for the installation error of optical angular encoder in outfield tests

Measurement ◽  
2021 ◽  
Vol 174 ◽  
pp. 109035
Author(s):  
Xuxing Zhao ◽  
Renjian Feng ◽  
Yinfeng Wu ◽  
Ning Yu ◽  
Xiaofeng Meng ◽  
...  
Keyword(s):  
Adaptive Estimation ◽  
Estimation Method

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

scholarly journals An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief

2018 ◽  
Vol 18 (51) ◽  
pp. 23-40
Author(s):  
Alireza Entezari ◽  
Fatemeh Mivaneh ◽  
Khosro Rezaei ◽  
Fatemeh Rahimi
Keyword(s):  
Thermal Comfort ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Comfort Indices

scholarly journals Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Cuifeng Wang ◽  
Wenjun Lv ◽  
Xiaochuan Li ◽  
Mingliang Mei
Keyword(s):  
Adaptive Estimation ◽  
Traditional Approach ◽  
Transition Probability ◽  
Estimation Method ◽  
Temporal Correlation ◽  
Learning System ◽  
Terrain Classification ◽  
Integrated Sensor ◽  
Bayesian Filter ◽  
Kinematics Model

As a type of skid-steering mobile robot, the tracked robot suffers from inevitable slippage, which results in an imprecise kinematics model and a degradation of performance during navigation. Compared with the traditional robot, the kinematics model is able to reflect the influences of slippage through the introduction of instantaneous centres of rotation (ICRs). However, ICRs cannot be measured directly and are time-varying with terrain variation, and thus, here, we aim to develop an online estimation method to acquire the ICRs of a robot by means of data fusion technologies. First, an innovation-based extended Kalman filter (IEKF) is employed to fuse the readings from two incremental encoders and a GPS-compass integrated sensor, to provide a real-time ICR estimation. Second, a decision tree-based learning system is used to classify the terrains that the robot traverses, according to the vibration signals gathered by an accelerometer. The results of this terrain classification are improved via a Bayesian filter, by utilizing temporal correlation in the terrain time series. Third, the performances of the ICR estimation and terrain classification are mutually promoted. On one hand, terrain variation is detected with the aid of the terrain classification, and therefore, the process noise variance of IEKF can be automatically adjusted. Hence, the results of ICR estimation are smooth if the terrain does not change and converge rapidly upon terrain variation. On the other hand, the sudden changes in innovation are used to adjust the state transition probability during the recursive calculation of the Bayesian filter, thus increasing the accuracy of the terrain classification. A real-world experiment was undertaken on a tracked robot to validate the effectiveness of the proposed method. It is also demonstrated that the terrain adaptive odometry outperforms the traditional approach with the knowledge of ICRs.

Adaptive state estimation for cyber physical systems under sparse attacks

2018 ◽  
Vol 41 (6) ◽  
pp. 1571-1579 ◽  
Author(s):  
Hao Zhang ◽  
Chen Peng ◽  
Hongtao Sun ◽  
Dajun Du
Keyword(s):  
State Estimation ◽  
Adaptive Estimation ◽  
Computing Time ◽  
Estimation Method ◽  
Cyber Physical Systems ◽  
Estimation Problem ◽  
Step Size ◽  
Estimation Errors ◽  
Physical Systems ◽  
Dynamic Estimation

This paper investigates the state estimation problem for cyber physical systems under sparse attacks. Firstly, the fundamental state estimation problem is transferred to an optimization problem with a unique solution. Secondly, an adaptive estimation method for sparse attacks is proposed, which convergence property is well proved. The advantage of proposed method is that the step-size can be adaptively adjusted based on the dynamic estimation errors. Therefore, the computing time is less than some existing methods while guaranteeing the desired performance. Then, a suitable state feedback is designed to improve the computing speed while enhancing the resiliency for the destroyed system. Finally, the speed performance and accuracy of proposed algorithm are verified by two numerical examples.

A nonlinear adaptive estimation method based on local approximation

1997 ◽  
Vol 45 (7) ◽  
pp. 1831-1841 ◽  
Author(s):  
Y. Iiguni ◽  
I. Kawamoto ◽  
N. Adachi
Keyword(s):  
Adaptive Estimation ◽  
Estimation Method ◽  
Local Approximation

Generalized Multiple-Model Adaptive Estimation for Mars Entry Navigation

2011 ◽  
Vol 383-390 ◽  
pp. 1190-1194 ◽  
Author(s):  
Kai Wu
Keyword(s):  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Dead Reckoning ◽  
Density Model ◽  
Multiple Model ◽  
Atmospheric Density ◽  
Multiple Model Adaptive Estimation ◽  
Precision Landing ◽  
Navigation Method

In order to satisfy the needs of Mars precision landing, a Mars entry navigation method is proposed for the problem of Mars atmospheric density model uncertainty. The navigation system processes accelerometer outputs as measurements, employs an extended Kalman filter bank regulated by the generalized multiple-model adaptive estimation method. Simulation results demonstrate the navigation system can identify the real atmospheric density model automatically, and show adaptivity and robustness to the uncertainty of atmospheric density. The navigation performance is greatly improved compared with traditional dead-reckoning

Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

2013 ◽  
Vol 753-755 ◽  
pp. 2117-2120 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Data Fusion ◽  
Nonlinear Filtering ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Cost Effective ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model

The accuracy of multi-sensor navigational data fusion by federated Kalman filter will be reduced in condition that the systems dynamics model is nonlinear and the noise statistical properties are unknown. To address this problem, a federated Interacting Multiple Model-Unscented Kalman Filteing (IMM-UKF) algorithm is presented. The UKF is a nonlinear estimation method which can achieve the accuracy at least to the second-order. The IMM estimation algorithm is one of the cost-effective adaptive estimation algorithm for systems involving parametric changes. The combination of IMM with UKF could deal with the problem of nonlinear filtering with uncertain noise. Simulation results show that the method can improve the accuracy of INS/GPS/odometer integrated navigation.

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