A novel Kalman filter for combining outputs of MEMS gyroscope array

Xue, Liang; Jiang, Chengyu; Chang, Honglong; Yang, Yong; Qin, Wei; Yuan, Weizheng

doi:10.1016/j.measurement.2011.12.016

Cited by 62 publications

(40 citation statements)

References 7 publications

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“…In the process of calibrating, the adaptive algorithm is based on the observation variance to select the appropriate observation matrix and the parameters. Adjust the different parameters of the observation points to verify compensation performance of adaptive compensation algorithm, finally getting the appropriate adaptive observation parameters [9] . During the experiment, the noise of angular velocity is removed by wavelet domain denoising to reduce the bias.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Application Of Strongly Tracking Kalman Filter In MEMS Gyroscope Bias Compensation

2017

2017 6th International Conference on Advanced Materials and Computer Science (ICAMCS 2017)

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Abstract. In this paper, we make a research about a integrated system. The system consists of two main parts: micro-electro-mechanical systems (MEMS) gyroscopes and compass. In order to eliminate the temperature drift bias of MEMS gyroscope, we use a strong tracking Kalman filter and chooses an adaptive algorithm. We established a model about the bias of the temperature of the gyroscope. The parameters of the model are selected for the state variables to change intelligently with the temperature to increase the precision of the MEMS gyroscope [1][2] . In the static temperature experiment, the compensated heading error is less than 0.7°. We can draw a conclusion that the traditional Kalman filter compensation method, multiple linear regression compensation method, improved least squares method and strong tracking Kalman adaptive filtering algorithm all can compensate the gyroscope drift bias, but the adaptive filtering algorithm can be more accurate about the compensation of MEMS gyro drift bias, and eliminate the impact of temperature on its accuracy.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Application Of Strongly Tracking Kalman Filter In MEMS Gyroscope Bias Compensation

2017

2017 6th International Conference on Advanced Materials and Computer Science (ICAMCS 2017)

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“…sensor noise caused by poor illumination and/or high temperature, and/or transmission e.g. electronic circuit noise Speckle noise (multiplicative noise) can be caused by random values that added to video frames [14,15]. Images obtained from these surfaces by coherent imaging systems such as laser, SAR, and ultrasound suffer from a common phenomenon called speckle.…”

Section: Video Degradation and Suggested Filtering Techniquesmentioning

confidence: 99%

“…These scattered signals add coherently; that is, they add constructively and destructively depending on the relative phases of each scattered waveform. Speckle noise results from these patterns of constructive and destructive interference shown as bright and dark dots in the image [16].…”

Section: Video Degradation and Suggested Filtering Techniquesmentioning

confidence: 99%

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Ata¹,

El-Darieby²,

Elnaby³

et al. 2017

IJIGSP

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Abstract-In this paper, an enhancement based traffic video has been proposed in the state of the art of computer vision. The main target is to develop a decision making criteria for removing the most probable video degradations. Such traffic video degradations would have an adverse impact on the transportation system. In order to establish the appropriate analysis, three types of video degradations have been added to the test video; salt and pepper noise, Gaussian noise, and speckle noise, we have simulated rainy, fog, and darkness conditions for the traffic video. First of all, back ground subtraction and Kalman filter techniques have been used for detecting and tracking vehicles respectively. By using such algorithms, it would be easily to estimate average number of assigned tracks which express the efficacy of correct detection and prediction of vehicles in each frame. Furthermore, video degradations would be applied in order to studying its effect on the average number of assigned tracks which would be deviated than noiseless video. Spatial filtering system has been applied to state the most suitable filter mask which satisfy the least deviation in the average number of assigned tracks. Experimental results show that median filter satisfies the least deviation in all cases of video degradations.

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“…Al-Majed and Alsuwaidan presented a multi-filters adaptive estimator to improve the angular rate accuracy by using the noise correlation in [7] but with no simulation or experimental results. Jiang and Xue used six-gyro array in [8][9][10][11]. They also adopted a novel optimal Kalman filter to combine the gyroscopes [8].…”

Section: Open Accessmentioning

confidence: 99%

“…Jiang and Xue used six-gyro array in [8][9][10][11]. They also adopted a novel optimal Kalman filter to combine the gyroscopes [8]. Their further study used a first-order Markov process to model the rate signal to improve the performance in dynamic condition [11].…”

Section: Open Accessmentioning

confidence: 99%

Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation

Liu¹,

Shen²,

Qin³

2015

Micromachines

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Abstract:A signal processing technique is presented to improve the angular rate accuracy of Micro-Electro-Mechanical System (MEMS) gyroscope by combining numerous gyroscopes. Based on the conditional correlation between gyroscopes, a dynamic data fusion model is established. Firstly, the gyroscope error model is built through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process to improve overall performance. Then the conditional covariance obtained through dynamic conditional correlation (DCC) estimator is used to describe the correlation quantitatively. Finally, the approach is validated by a prototype of the virtual gyroscope, which consists of six-gyroscope array. The experimental results indicate that the weights of gyroscopes change with the value of error. Also, the accuracy of combined rate signal is improved dramatically compared to individual gyroscope. The results indicate that the approach not only improves the accuracy of the MEMS gyroscope, but also discovers the fault gyroscope and eliminates its influence.

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A novel Kalman filter for combining outputs of MEMS gyroscope array

Cited by 62 publications

References 7 publications

Application Of Strongly Tracking Kalman Filter In MEMS Gyroscope Bias Compensation

Application Of Strongly Tracking Kalman Filter In MEMS Gyroscope Bias Compensation

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation

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