A Kalman Filter-Based Algorithm for Measuring the Parameters of Moving Objects

Dichev, Dimitar; Koev, Hristofor; Bakalová, Totka; Louda, Petr

doi:10.1515/msr-2015-0004

Cited by 13 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is based on the mathematical model of the function of the dynamic error as an independent component having particular characteristics and participating in the formation of the measurement result. In this way the chosen concept enables not only to examine the characteristics of the dynamic error but also to develop algorithms [9][10][11], methods [12] and instruments [13,14] for discriminating it as an independent component.…”

Section: The Mathematical Model Of the Dynamic Errormentioning

confidence: 99%

А Меаsuring Method for Gyro-Free Determination of the Parameters of Moving Objects

Dichev¹,

Koev²,

Bakalová³

et al. 2016

Metrology and Measurement Systems

Self Cite

View full text Add to dashboard Cite

The paper presents a new method for building measuring instruments and systems for gyro-free determination of the parameters of moving objects. To illustrate the qualities of this method, a system for measuring the roll, pitch, heel and trim of a ship has been developed on its basis. The main concept of the method is based, on one hand, on a simplified design of the base coordinate system in the main measurement channel so as to reduce the instrumental errors, and, on the other hand, on an additional measurement channel operating in parallel with the main one and whose hardware and software platform makes possible performing algorithms intended to eliminate the dynamic error in real time. In this way, as well as by using suitable adaptive algorithms in the measurement procedures, low-cost measuring systems operating with high accuracy under conditions of inertial effects and whose parameters (intensity and frequency of the maximum in the spectrum) change within a wide range can be implemented.

show abstract

Section: The Mathematical Model Of the Dynamic Errormentioning

confidence: 99%

А Меаsuring Method for Gyro-Free Determination of the Parameters of Moving Objects

Dichev¹,

Koev²,

Bakalová³

et al. 2016

Metrology and Measurement Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Micro-Electro-Mechanical Systems (MEMS) gyroscope has advantages of affordable, compactness and low power consumption, which is widely used in the fields of inertial measurement and inertial stabilization with considerably good application prospects [ 1 , 2 , 3 , 4 , 5 ]. However, due to its unique processing technology and material micro-scale effects [ 6 , 7 ], a MEMS gyroscope has considerable errors, which need to be compensated [ 8 , 9 , 10 , 11 ]. MEMS gyroscope errors contain system errors and random errors.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error

Zhang

Peng

Mou

et al. 2018

Sensors

View full text Add to dashboard Cite

To improve the dynamic random error compensation accuracy of the Micro Electro Mechanical System (MEMS) gyroscope at different angular rates, an adaptive filtering approach based on the dynamic variance model was proposed. In this paper, experimental data were utilized to fit the dynamic variance model which describes the nonlinear mapping relations between the MEMS gyroscope output data variance and the input angular rate. After that, the dynamic variance model was applied to online adjustment of the Kalman Filter measurement noise coefficients. The proposed approach suppressed the interference from the angular rate in the filtering results. Dynamic random errors were better estimated and reduced. Turntable experiment results indicated that the adaptive filtering approach compensated for the MEMS gyroscope dynamic random error effectively both in the constant angular rate condition and the continuous changing angular rate condition, thus achieving adaptive dynamic random error compensation.

show abstract

“…As a result of the system optimization the best quality of transient processes is achieved both over the change of revolutions and over the head in supply pipelines. In [3] is presented an algorithm based on Kalman method, which significantly improves the dynamic accuracy of the measured signals in the system.…”

Section: Introductionmentioning

confidence: 99%