scholarly journals Performance Analysis of Computational Intelligence Correction

2021 ◽  
Author(s):  
Nalini Arasavali ◽  
Sasibhushanarao Gottapu
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Computational Intelligence ◽  
A Priori ◽  
Covariance Matrices ◽  
System Model ◽  
Process Noise ◽  
Navigation Algorithm ◽  
Modified Kalman Filter ◽  
Measurement Noises

Abstract Kalman filter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact filter parameters must be defined a priori to use standard Kalman filters for coping with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undefined, which results in difficulties and challenges in the implementation of the conventional Kalman filter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman filtering technology, a modified Kalman filter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706970.9093 m, y: 6035941.0226 m, z: 1930009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance.

Online Dynamic Response Reconstruction in the Presence of Observation Outliers

2021 ◽  
Author(s):  
Chaodong Zhang ◽  
Jian’an Li ◽  
Youlin Xu
Keyword(s):  
Kalman Filter ◽  
Dynamic Response ◽  
Numerical Study ◽  
Frame Structure ◽  
System Model ◽  
Measurement Information ◽  
Model Errors ◽  
Process Noise ◽  
Robust Kalman Filter ◽  
Response Reconstruction

Previous studies show that Kalman filter (KF)-based dynamic response reconstruction of a structure has distinct advantages in the aspects of combining the system model with limited measurement information and dealing with system model errors and measurement Gaussian noises. However, because the recursive KF aims to achieve a least-squares estimate of state vector by minimizing a quadratic criterion, observation outliers could dramatically deteriorate the estimator’s performance and considerably reduce the response reconstruction accuracy. This study addresses the KF-based online response reconstruction of a structure in the presence of observation outliers. The outlier-robust Kalman filter (OKF), in which the outlier is discerned and reweighted iteratively to achieve the generalized maximum likelihood (ML) estimate, is used instead of KF for online dynamic response reconstruction. The influences of process noise and outlier duration to response reconstruction are investigated in the numerical study of a simple 5-story frame structure. The experimental work on a simply-supported overhanging steel beam is conducted to testify the effectiveness of the proposed method. The results demonstrate that compared with the KF-based response reconstruction, the proposed OKF-based method is capable of dealing with the observation outliers and producing more accurate response construction in presence of observation outliers.

scholarly journals Variational Bayesian Iteration-Based Invariant Kalman Filter for Attitude Estimation on Matrix Lie Groups

Aerospace ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 246
Author(s):  
Jiaolong Wang ◽  
Zeyang Chen
Keyword(s):  
Kalman Filter ◽  
Lie Groups ◽  
Lie Group ◽  
A Priori ◽  
Attitude Estimation ◽  
Estimation Problem ◽  
Superior Performance ◽  
Process Noise ◽  
Variational Bayesian ◽  
Noise Statistics

Motivated by the rapid progress of aerospace and robotics engineering, the navigation and control systems on matrix Lie groups have been actively studied in recent years. For rigid targets, the attitude estimation problem is a benchmark one with its states defined as rotation matrices on Lie groups. Based on the invariance properties of symmetry groups, the invariant Kalman filter (IKF) has been developed by researchers for matrix Lie group systems; however, the limitation of the IKF is that its estimation performance is prone to be degraded if the given knowledge of the noise statistics is not accurate. For the symmetry Lie group attitude estimation problem, this paper proposes a new variational Bayesian iteration-based adaptive invariant Kalman filter (VBIKF). In the proposed VBIKF, the a priori error covariance is not propagated by the conventional steps but directly calibrated in an iterative manner based on the posterior sequences. The main advantage of the VBIKF is that the statistics parameter of the system process noise is no longer required and so the IKF’s hard dependency on accurate process noise statistics can be reduced significantly. The mathematical foundation for the new VBIKF is presented and its superior performance in adaptability and simplicity is further demonstrated by numerical simulations.

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.

Data Assimilation of River Networks Using Extended Kalman Filter

2014 ◽  
Vol 580-583 ◽  
pp. 1923-1927
Author(s):  
Yi Fan Chen ◽  
Jing Lin Qian
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Real Time ◽  
Extended Kalman Filter ◽  
River Network ◽  
Measurement Noise ◽  
River Networks ◽  
Network System ◽  
Process Noise ◽  
Measurement Noises

In order to improve the accuracy of river network hydraulic model, extended kalman filter was used for real-time updating model states. In a simulation example of a river network composed of 14 channels, it systematically analyzed the effects of process and measurement noises on state correction. The results show that the extended kalman filter is able to effectively carry out data assimilation of non-linear river network system, and big process noise in combination with relatively small measurement noise is recommended for state correction.

scholarly journals Performance Analysis of a DEKF for Available Bandwidth Measurement

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Diego Santoro ◽  
Michele Vadursi
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Process Noise ◽  
Available Bandwidth ◽  
Measurement Algorithm ◽  
End To End ◽  
Bandwidth Measurement

The paper presents a characterisation analysis of a measurement algorithm based on a Discrete-time Extended Kalman Filter (DEKF), which has recently been proposed for the estimation and tracking of end-to-end available bandwidth. The analysis is carried out by means of simulations for different rates of variations of the available bandwidth and permits assessing the performance of the measurement algorithm for different values of the filter parameters, that is, the covariance matrixes of the measurement and process noise.

Some New Alternative Formulations of Adaptive Kalman Filter for Market Risk Beta Estimation

2021 ◽  
Vol 8 (2) ◽  
pp. 17-37
Author(s):  
Atanu Das
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Linear Models ◽  
A Priori ◽  
Noise Filtering ◽  
Typical Problem ◽  
Adaptive Kalman Filter ◽  
Empirical Performance ◽  
Noise Covariance ◽  
New Formulation

Kalman filter (KF) provides optimal beta estimate with linear models where the noise covariances are known a priori. Noise covariance adaptation-based adaptive KFs (AKFs) have also been used to get these beta estimates. These AKFs suffer from one typical problem, namely inadequate noise filtering. This paper explores some new formulation of such AKFs to solve this problem in addition to applying other related existing formulations. The proposed methods have been analysed through simulation study along with empirical performance verifications through VaR backtesting, expected shortfall analysis, and in-sample performance analysis. Results show that two new and one existing AKFs are successful to provide smooth beta estimates.

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