A filter-bank-based Kalman filtering technique for wavelet estimation and decomposition of random signals

1998 ◽  
Vol 45 (2) ◽  
pp. 237-241 ◽  
Author(s):  
L. Hong ◽  
G. Cheng ◽  
C.K. Chui
Keyword(s):  
Kalman Filtering ◽  
Filter Bank ◽  
Wavelet Estimation ◽  
Filtering Technique ◽  
Random Signals

scholarly journals Kalman filtering for dynamic motion and model estimation

2021 ◽  
Author(s):  
Randal Schumacher.
Keyword(s):  
Kalman Filtering ◽  
Vision System ◽  
Target Position ◽  
Model Estimation ◽  
Mass Center ◽  
Extended Kalman Filtering ◽  
Dynamic Motion ◽  
Filtering Technique ◽  
Time Determination

The fundamental task of a space vision system for rendezvous, capture, and servicing of satellites on-orbit is the real-time determination of the motion of the target vehicle as observed on-board a chaser vehicle. Augmenting the architecture to incorporate the highly regarded Kalman filtering technique can synthesize a system that is more capable, more efficient and more robust. A filter was designed and testing was conducted in an inertial environment and then in a more realistic relative motion orbital rendezvous scenario. The results indicate that a Dynamic Motion Filter based on extended Kalman filtering can provide the vision system routines with excellent initialization leading to faster convergence, reliable pose estimation at slower sampling rates, and the ability to estimate target position, velocity, orientation, angular velocity, and mass center location.

Does the brain implement the Kalman filter?

2004 ◽  
Vol 27 (3) ◽  
pp. 404-405 ◽  
Author(s):  
Valeri Goussev
Keyword(s):  
Kalman Filter ◽  
Data Processing ◽  
Kalman Filtering ◽  
Brain Structures ◽  
Parameter Evaluation ◽  
Filtering Technique ◽  
Processing Techniques ◽  
The Brain

The Kalman filtering technique is considered as a part of concurrent data-processing techniques also related to detection, parameter evaluation, and identification. The adaptive properties of the filter are discussed as being related to symmetrical brain structures.

scholarly journals Design, Simulation, and Performance Analysis of an INS/GPS System using Parallel Kalman Filters Structure

2011 ◽  
Vol 1 (2) ◽  
Author(s):  
Tran Duc-Tan ◽  
Paul Fortier ◽  
Huu-Tue Huynh
Keyword(s):  
Performance Analysis ◽  
Kalman Filtering ◽  
Inertial Navigation System ◽  
Moving Objects ◽  
Practical Applications ◽  
System A ◽  
Mems Technology ◽  
Filtering Technique ◽  
And Performance ◽  
Gps System

Thanks to the strong growth of MEMS technology, the Inertial Navigation System (INS) is widely applied to navigation and guidance of moving objects. However, there exist errors in the inertial sensor’s signals that cause unacceptable drifts. To minimize these effects on the INS system, a GPS is usually employed simultaneously with an INS in order to increase the dimension of the system; the desired parameters are estimated by Kalman filtering technique applied to the enlarged system. In this paper, we present the design, simulation and performance analysis of an INS/GPS system using two parallel Kaman filters in order to increase the accuracy of the parameter estimation process. The results show that this system could be efficiently brought to practical applications.

scholarly journals Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1168 ◽  
Author(s):  
Sebin Park ◽  
Myeong-Seon Gil ◽  
Hyeonseung Im ◽  
Yang-Sae Moon
Keyword(s):  
Kalman Filtering ◽  
Input Parameter ◽  
Original Data ◽  
Measurement Noise ◽  
Sensor Data ◽  
Noise Variance ◽  
Noise Parameters ◽  
Input Noise ◽  
Novel Method ◽  
Filtering Technique

To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user’s experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.

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