Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

Yuan, Guangmin; Yuan, Weizheng; Xue, Liang; Xie, Jian; Chang, Honglong

doi:10.3390/s151127590

Cited by 22 publications

(10 citation statements)

References 19 publications

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“…For the gyro random error, a simple and effective method is to average a large number of gyro outputs at each rotation position. Other filtering approaches can be found in [ 21 , 22 , 24 , 25 ] . Increase the number of measurement points (ensure that the north finding time is less than the time scale of the bias instability [ 4 ]).…”

Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

“…Recently, the structures of different gyros have been comprehensively analyzed to constrain the gyros’ bias error, scale-factor error and random error, e.g., MEMS gyros [ 6 , 7 , 8 , 9 ], ring laser gyros [ 10 , 11 ], fiber optic gyros [ 10 , 12 ], resonant optical gyros [ 13 , 14 , 15 , 16 ] and dynamically tuned gyros [ 17 , 18 , 19 ]. Other very active areas include constraining the gyro bias error and modeling of temperature drift through different kinds of filter methods [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. However, only few works present the details of building a complete gyro north finder system that provides high precision azimuth reference information.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

Luo

Wang

Shen

et al. 2016

Sensors

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Even when the Global Positioning System (GPS) signal is blocked, a rate gyroscope (gyro) north finder is capable of providing the required azimuth reference information to a certain extent. In order to measure the azimuth between the observer and the north direction very accurately, we propose a multi-position non-continuous rotation gyro north finding scheme. Our new generalized mathematical model analyzes the elements that affect the azimuth measurement precision and can thus provide high precision azimuth reference information. Based on the gyro’s principle of detecting a projection of the earth rotation rate on its sensitive axis and the proposed north finding scheme, we are able to deduct an accurate mathematical model of the gyro outputs against azimuth with the gyro and shaft misalignments. Combining the gyro outputs model and the theory of propagation of uncertainty, some approaches to optimize north finding are provided, including reducing the gyro bias error, constraining the gyro random error, increasing the number of rotation points, improving rotation angle measurement precision, decreasing the gyro and the shaft misalignment angles. According them, a north finder setup is built and the azimuth uncertainty of 18” is obtained. This paper provides systematic theory for analyzing the details of the gyro north finder scheme from simulation to implementation. The proposed theory can guide both applied researchers in academia and advanced practitioners in industry for designing high precision robust north finder based on different types of rate gyroscopes.

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Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

Luo

Wang

Shen

et al. 2016

Sensors

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“…IWSN has highly cognitive function [39,40]. Using distributed collaboration of agents, each intelligent node is able to communicate with other intelligent nodes in the neighborhood which have access to environmental information and to process and to manage a large amount of real-time data in the intelligent network [41,42].…”

Section: Introductionmentioning

confidence: 99%

The Algorithms of Distributed Learning and Distributed Estimation about Intelligent Wireless Sensor Network

Tan

2020

Sensors

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The intelligent wireless sensor network is a distributed network system with high “network awareness”. Each intelligent node (agent) is connected by the topology within the neighborhood which not only can perceive the surrounding environment, but can adjusts its own behavior according to its local perception information to constructs a distributed learning algorithms. Therefore, three basic intelligent network topologies of centralized, non-cooperative, and cooperative are intensively investigated in this paper. The main contributions of the paper include two aspects. First, based on algebraic graph, three basic theoretical frameworks for distributed learning and distributed parameter estimation of cooperative strategy are surveyed: increment strategy, consensus strategy, and diffusion strategy. Second, based on classical adaptive learning algorithm and online updating law, the implementation process of distributed estimation algorithm and the latest research progress of above three distributed strategies are investigated.

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“…The technology of multiple-sensor fusion provides a new way for improving the precision of a MIMU [9,10,11,12,13,14]. An array of MEMS gyroscopes can be configured and mounted on each orthogonally sensitive axis of a MIMU to provide redundant signals at the same condition.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Correlation in MEMS Gyroscope Array and its Influence on Accuracy Improvement for the Combined Angular Rate Signal

Xue

Wang

et al. 2018

Micromachines

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Obtaining a correlation factor is a prerequisite for fusing multiple outputs of a mircoelectromechanical system (MEMS) gyroscope array and evaluating accuracy improvement. In this paper, a mathematical statistics method is established to analyze and obtain the practical correlation factor of a MEMS gyroscope array, which solves the problem of determining the Kalman filter (KF) covariance matrix Q and fusing the multiple gyroscope signals. The working principle and mathematical model of the sensor array fusion is briefly described, and then an optimal estimate of input rate signal is achieved by using of a steady-state KF gain in an off-line estimation approach. Both theoretical analysis and simulation show that the negative correlation factor has a favorable influence on accuracy improvement. Additionally, a four-gyro array system composed of four discrete individual gyroscopes was developed to test the correlation factor and its influence on KF accuracy improvement. The result showed that correlation factors have both positive and negative values; in particular, there exist differences for correlation factor between the different units in the array. The test results also indicated that the Angular Random Walk (ARW) of 1.57°/h0.5 and bias drift of 224.2°/h for a single gyroscope were reduced to 0.33°/h0.5 and 47.8°/h with some negative correlation factors existing in the gyroscope array, making a noise reduction factor of about 4.7, which is higher than that of a uncorrelated four-gyro array. The overall accuracy of the combined angular rate signal can be further improved if the negative correlation factors in the gyroscope array become larger.

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Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

Cited by 22 publications

References 19 publications

Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

The Algorithms of Distributed Learning and Distributed Estimation about Intelligent Wireless Sensor Network

Analysis of Correlation in MEMS Gyroscope Array and its Influence on Accuracy Improvement for the Combined Angular Rate Signal

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