Accurate real time inertial navigation device by application and processing of arrays of MEMS inertial sensors

Tanenhaus, M.; Geis, Tim; Carhoun, D.O.; Holland, A.

doi:10.1109/plans.2010.5507137

Cited by 14 publications

(13 citation statements)

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“…There is an upper limit on the number of sensors that can reasonably be combined into an array. The performance of 64 sensors are shown as diamonds as an example of a large, but still practical array (for instance [32] has an array of 100 sensors). It can be seen that the effect of even a small array moves the Xsens sensor well inside the stability boundary but even with the large array of combined Bosch accelerometers and ST gyroscopes has only just made it to the boundary, and the Invensense MPU-9150 array is still far away.…”

Section: Array Techniquesmentioning

confidence: 99%

The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays

2016

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Section: Array Techniquesmentioning

confidence: 99%

The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays

2016

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“…Currently, combining virtual gyroscope technology with other filtering technologies is becoming increasingly prevalent. Martin Tanenhaus et al [9] combined denoised sensor data with the Kalman filter offset compensation algorithm to lower the bias stability (standard deviation) of the gyro to less than 0.1 degrees/hour. Kuan-Ying Huang et al [10] combined a MEMS array with M-estimation filters to effectively suppress non-Gaussian impulse noise and provide angular velocity measurements with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

High-Efficiency Wavelet Compressive Fusion for Improving MEMS Array Performance

Liang

Zhu

Zhao

et al. 2020

Sensors

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With the rapid development of microelectromechanical systems (MEMS) technology, low-cost MEMS inertial devices have been widely used for inertial navigation. However, their application range is greatly limited in some fields with high precision requirements because of their low precision and high noise. In this paper, to improve the performance of MEMS inertial devices, we propose a highly efficient optimal estimation algorithm for MEMS arrays based on wavelet compressive fusion (WCF). First, the algorithm uses the compression property of the multiscale wavelet transform to compress the original signal, fusing the compressive data based on the support. Second, threshold processing is performed on the fused wavelet coefficients. The simulation result demonstrates that the proposed algorithm performs well on the output of the inertial sensor array. Then, a ten-gyro array system is designed for collecting practical data, and the frequency of the embedded processor in our verification environment is 800 MHz. The experimental results show that, under the normal working conditions of the MEMS array system, the 100 ms input array data require an approximately 75 ms processing delay when employing the WCF algorithm to support real-time processing. Additionally, the zero-bias instability, angle random walk, and rate slope of the gyroscope are improved by 8.0, 8.0, and 9.5 dB, respectively, as compared with the original device. The experimental results demonstrate that the WCF algorithm has outstanding real-time performance and can effectively improve the accuracy of low-cost MEMS inertial devices.

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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%

“…In particular, the performance of a KF approach for fusing six fully uncorrelated MEMS gyroscopes was further analyzed and evaluated in [17], and it demonstrated that performance can be better than that of an averaging process. Additionally, Tanenhaus et al reported a method for constructing a MIMU [9,11] in which multiple gyroscopes are placed on each sensitive axis of the MIMU, and a wavelet de-noising method was used to combine outputs of the gyroscope array. Moreover, Lucian et al also designed a redundant inertial attitude measurement system by placing four separated gyroscopes on each sensitive axis of a MIMU [18]; it used a weighted statistical method for making signal fusion of multiple gyroscopes through setting a weighted factor associated for each sensor.…”

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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Accurate real time inertial navigation device by application and processing of arrays of MEMS inertial sensors

Cited by 14 publications

References 0 publications

The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays

The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays

High-Efficiency Wavelet Compressive Fusion for Improving MEMS Array Performance

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

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