Journal articles: 'MEMS IMU'

1

Zhao, Wanliang, Xiangyu Sun, Yijie Rong, Jie Duan, Jiawei Chen, Lijun Song, and Qinyi Pan. "Optimization on the Precision of the MEMS-Redundant IMU Based on Adhesive Joint Assembly." Mathematical Problems in Engineering 2020 (October 9, 2020): 1–9. http://dx.doi.org/10.1155/2020/8855141.

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In order to improve the precision of the spaceborne Inertial Measurement Unit (IMU), this paper proposes an adhesive joint assembly of the MEMS-redundant IMU. That is the application of special redundant installation of multiple MEMS gyroscopes in the IMU, which can improve the reliability of the MEMS-redundant IMU on the basis of reducing the weight of IMU. However, with the change of working environment, the traditional mechanical assembly of MEMS-redundant IMU will produce the large packaging stress and cause the deformation of MEMS gyroscope. This change will lead to changes in installation errors, scale factor errors, and bias errors of the MEMS gyroscope, resulting in a significant reduction in measurement precision of the MEMS-redundant IMU. Therefore, this paper selects the adhesive material that matches the thermal physical parameters of the material with the circuit board by analyzing the requirements of MEMS gyroscope on working environment at first. Then, by optimizing the bonding process, the installation error of each axis of MEMS-redundant IMU under different temperatures is better than the traditional mechanical connection mode. The experiment results of thermal vacuum show that the new assembly method can reduce the influence of temperature on the bias. Compared with the traditional method, the new assembly which is based on adhesive joint assembly can improve the measurement precision of MEMS-redundant IMU by an order of magnitude.

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Hemerly, Elder M. "MEMS IMU stochastic error modelling." Systems Science & Control Engineering 5, no. 1 (December 7, 2016): 1–8. http://dx.doi.org/10.1080/21642583.2016.1262801.

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Liu, Fuchao, Zhong Su, Hui Zhao, Qing Li, and Chao Li. "Attitude Measurement for High-Spinning Projectile with a Hollow MEMS IMU Consisting of Multiple Accelerometers and Gyros." Sensors 19, no. 8 (April 15, 2019): 1799. http://dx.doi.org/10.3390/s19081799.

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A low cost, high precision hollow structure MEMS IMU has been developed to measure the roll angular rate of a high-spinning projectile. The hollow MEMS IMU is realized by designing the scheme of non-centroid configuration of multiple accelerometers. Two dual-axis accelerometers are respectively mounted on the pitch axis and the yaw axis away from the center of mass of the high-spinning projectile. Three single-axis gyros are mounted orthogonal to each other to measure the angular rates, respectively. The roll gyro is not only used to judge the spinning direction, but also to measure and compensate for the low rotation speed of the high-spinning projectile. In order to improve the measurement accuracy of the sensor, the sensor output error is modeled and calibrated by the least square method. By analyzing the influence of noise statistical characteristics on angular rate solution accuracy, an adaptive unscented Kalman filter (AUKF) algorithm is proposed, which has a higher estimation accuracy than UKF algorithm. The feasibility of the method is verified by numerical simulation. By using the MEMS IMU device to build a semi-physical simulation platform, the solution accuracy of the angular rate is analyzed by simulating different rotation speeds of the projectile. Finally, the flight test is carried out on the rocket projectile with the hollow MEMS IMU. The test results show that the hollow MEMS IMU is reasonable and feasible, and it can calculate the roll angular rate in real time. Therefore, the hollow MEMS IMU designed in this paper has certain engineering application value for high-spinning projectiles.

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Cheng, Junbing, Deng-ao Li, and Jumin Zhao. "A MEMS-IMU Assisted BDS Triple-Frequency Ambiguity Resolution Method in Complex Environments." Mathematical Problems in Engineering 2018 (December 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/6041953.

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Emerging technologies such as smart cities and unmanned vehicles all need Global Navigation Satellite Systems (GNSS) to provide high-precision positioning and navigation services. Fast and reliable carrier phase ambiguity resolution (AR) is a prerequisite for high-precision positioning. The poor satellite geometry and severe multipath effect caused by Beidou Navigation Satellite System (BDS) signal occlusion and reflection in complex environments will degrade the AR performance. In this contribution, a fast triple-frequency AR method combining Microelectromechanical System-Inertial Measurement Unit (MEMS-IMU) and BDS is proposed. First, the Extra-Wide Lane (EWL) ambiguity is fixed with the positioning parameters of MEMS-IMU instead of the pseudorange. Then, the phase noise variance of Narrow Lane (NL) observation is obtained from ambiguity-fixed EWL observation to reduce the total noise level of NL observation, and the NL ambiguity can be reliably fixed, and the BDS positioning result is obtained. Finally, the BDS positioning result is used as the posterior measurement of the extended Kalman filter to update the MEMS-IMU positioning parameters to form the coupling loop of MEMS-IMU and BDS. The data of urban road vehicle experiments were collected to verify the feasibility and effectiveness of the proposed algorithm. Results show that MEMS-IMU can speed up AR, and reduction of total noise level can significantly improve the reliability of AR.

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Collin, Jussi. "MEMS IMU Carouseling for Ground Vehicles." IEEE Transactions on Vehicular Technology 64, no. 6 (June 2015): 2242–51. http://dx.doi.org/10.1109/tvt.2014.2345847.

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Rivers, Montgomery C., Alexander A. Trusov, Sergei A. Zotov, and Andrei M. Shkel. "Micro IMU Utilizing Folded MEMS Approach." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2010, DPC (January 1, 2010): 001360–78. http://dx.doi.org/10.4071/2010dpc-wa23.

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In this paper, we propose a novel wafer-level approach for fabrication and 3-D integration of MEMS devices in miniature multi-axis assemblies of inertial, acoustic, and optical sensors. The approach is based on simultaneous fabrication of all sensors on the same substrate connected by flexible electrical interconnects, mechanical hinges and latches. A multi-axis sensor system is then obtained by folding the fabricated structures into 3-D cubes, pyramids, or other rigid shapes, and subsequently micro-welded. In the current work, we demonstrate feasibility of the folded cube approach for creation of miniature MEMS IMU with <1 cm3 volume. Design of the IMU consists of a folded cube or pyramid backbone structure with micromachined accelerometers and gyroscopes on its sidewalls. Silicon-on-insulator (SOI) wafers are used as a substrate for fabrication of both the inertial sensors and the folded backbone structure. Fabrication of the sensors consists of lithography, deep reactive ion etching (DRIE), and HF acid release of the inertial proof masses. Flexible polymer hinges connecting faces of the folded structures are defined on the same substrate and incorporate electrical interconnects. To provide rigidity to the assembled 3-D structure, interlocking silicon latches are fabricated along the edges of each sidewall, which are silicon-to-silicon welded after assembly. The approach allows for creating miniature multi-axis sensor systems without compromising performance of individual sensors. Gyroscopes integrated in the current folded cube IMU have experimentally demonstrated 3-dB bandwidth of 250 Hz and angle random walk (ARW) below 0.1 (deg/s)/rt-Hz in atmospheric pressure. Measured uncompensated temperature coefficients of gyroscope bias and scale factor were 313 (deg/h)/degC and 351ppm/degC, respectively. Accelerometers on the cube have been characterized, yielding a noise floor of 570μG/√Hz. Sensitivity of the accelerometers is measured at 40mV/G with a bandwidth of 100 Hz.

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El Bahnasawy, M., E. Abdelkawy, and S. Shedied. "Error Models for MEMS Based IMU." International Conference on Aerospace Sciences and Aviation Technology 14, AEROSPACE SCIENCES (May 1, 2011): 1–10. http://dx.doi.org/10.21608/asat.2011.23381.

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8

Zhao, Wanliang, Yuxiang Cheng, Sihan Zhao, Xiaomao Hu, Yijie Rong, Jie Duan, and Jiawei Chen. "Navigation Grade MEMS IMU for A Satellite." Micromachines 12, no. 2 (February 4, 2021): 151. http://dx.doi.org/10.3390/mi12020151.

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This paper presents a navigation grade micro-electromechanical system (MEMS) inertial measurement unit (IMU) that was successfully applied for the first time in the Lobster-Eye X-ray Satellite in July 2020. A six-axis MEMS gyroscope redundant configuration is adopted in the unit to improve the performance through mutual calibration of a set of two-axis gyroscopes in the same direction. In the paper, a satisfactory precision of the gyroscope is achieved by customized and self-calibration gyroscopes whose parameters are adjusted at the expense of bandwidth and dynamics. According to the in-orbit measured data, the MEMS IMU provides an outstanding precision of better than 0.02 °/h (1σ) with excellent bias instability of 0.006 °/h and angle random walk (ARW) of around 0.003 °/h1/2. It is the highest precision MEMS IMU for commercial aerospace use ever publicly reported in the world to date.

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9

Bistrov, Vadim. "Performance Analysis of Alignment Process of MEMS IMU." International Journal of Navigation and Observation 2012 (November 12, 2012): 1–11. http://dx.doi.org/10.1155/2012/731530.

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The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given.

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Jiang, Changhui, Shuai Chen, Yuwei Chen, Boya Zhang, Ziyi Feng, Hui Zhou, and Yuming Bo. "A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)." Sensors 18, no. 10 (October 15, 2018): 3470. http://dx.doi.org/10.3390/s18103470.

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Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Hebei, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.

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Zhu, Zhenshu, Yuming Bo, and Changhui Jiang. "A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network." Mathematical Problems in Engineering 2019 (November 21, 2019): 1–9. http://dx.doi.org/10.1155/2019/5491243.

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Inertial measurement unit (IMU) (an IMU usually contains three gyroscopes and accelerometers) is the key sensor to construct a self-contained inertial navigation system (INS). IMU manufactured through the Micromechanics Electronics Manufacturing System (MEMS) technology becomes more popular, due to its smaller column, lower cost, and gradually improved accuracy. However, limited by the manufacturing technology, the MEMS IMU raw measurement signals experience complicated noises, which cause the INS navigation solution errors diverge dramatically over time. For addressing this problem, an advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed in the MEMS gyroscope noise suppressing. NAS-RNN was the recently invented artificial intelligence method for time series problems in data science community. Different from conventional method, NAS-RNN was able to search a more feasible architecture for selected application. In this paper, a popular MEMS IMU STIM300 was employed in the testing experiment, and the sampling frequency was 125 Hz. The experiment results showed that the NAS-RNN was effective for MEMS gyroscope denoising; the standard deviation values of denoised three-axis gyroscope measurements decreased by 44.0%, 34.1%, and 39.3%, respectively. Compared with the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), the NAS-RNN obtained further decreases by 28.6%, 3.7%, and 8.8% in standard deviation (STD) values of the signals. In addition, the attitude errors decreased by 26.5%, 20.8%, and 16.4% while substituting the LSTM-RNN with the NAS-RNN.

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Jiang, Changhui, Shuai Chen, Yuwei Chen, Yuming Bo, Lin Han, Jun Guo, Ziyi Feng, and Hui Zhou. "Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising." Sensors 18, no. 12 (December 17, 2018): 4471. http://dx.doi.org/10.3390/s18124471.

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Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) is popular in the community for constructing a navigation system, due to its small size and low power consumption. However, limited by the manufacturing technology, MEMS IMU experiences more complicated noises and errors. Thus, noise modeling and suppression is important for improving accuracy of the navigation system based on MEMS IMU. Motivated by this problem, in this paper, a deep learning method was introduced to MEMS gyroscope de-noising. Specifically, a recently popular Recurrent Neural Networks (RNN) variant Simple Recurrent Unit (SRU-RNN) was employed in MEMS gyroscope raw signals de-noising. A MEMS IMU MSI3200 from MT Microsystem Company was employed in the experiments for evaluating the proposed method. Following two problems were furtherly discussed and investigated: (1) the employed SRU with different training data length were compared to explore whether there was trade-off between the training data length and prediction performance; (2) Allan Variance was the most popular MEMS gyroscope analyzing method, and five basic parameters were employed to describe the performance of different grade MEMS gyroscope; among them, quantization noise, angle random walk, and bias instability were the major factors influencing the MEMS gyroscope accuracy, the compensation results of the three parameters for gyroscope were presented and compared. The results supported the following conclusions: (1) considering the computation brought from training dataset, the values of 500, 3000, and 3000 were individually sufficient for the three-axis gyroscopes to obtain a reliable and stable prediction performance; (2) among the parameters, the quantization noise, angle random walk, and bias instability performed 0.6%, 6.8%, and 12.5% improvement for X-axis gyroscope, 60.5%, 17.3%, and 34.1% improvement for Y-axis gyroscope, 11.3%, 22.7%, and 35.7% improvement for Z-axis gyroscope, and the corresponding attitude errors decreased by 19.2%, 82.1%, and 69.4%. The results surely demonstrated the effectiveness of the employed SRU in this application.

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Xing, Li, Xiaowei Tu, Weixing Qian, Zhi Chen, and Qinghua Yang. "Performance Enhancement Method for Angular Rate Measurement Based on Redundant MEMS IMUs." Micromachines 10, no. 8 (August 1, 2019): 514. http://dx.doi.org/10.3390/mi10080514.

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Aiming at the low-cost, wide-range, and accurate measurement requirement for Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) on a multi-rotor Unmanned Aerial Vehicle (UAV), the paper designs a heterogeneous parallel redundancy configuration scheme. In redundant MEMS IMUs, a high-cost and small-range MEMS gyroscope is combined with low-cost and large-range MEMS gyroscopes. Then, an adaptive data fusion method of redundant MEMS gyroscopes is proposed. By the designed experiments based on the simulation data and the sensor measurement data, the proposed method has been proved that it can effectively improve the angular rate measurement performance of the multi-rotor UAV and broaden the angular rate measurement range on the basis of saving the configuration cost and volume of the micro IMU.

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Li, Sen, Yunchen Niu, Chunyong Feng, Haiqiang Liu, Dan Zhang, and Hengjie Qin. "An Onsite Calibration Method for MEMS-IMU in Building Mapping Fields." Sensors 19, no. 19 (September 25, 2019): 4150. http://dx.doi.org/10.3390/s19194150.

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Light detection and ranging (LiDAR) is one of the popular technologies to acquire critical information for building information modelling. To allow an automatic acquirement of building information, the first and most important step of LiDAR technology is to accurately determine the important gesture information that micro electromechanical (MEMS) based inertial measurement unit (IMU) sensors can provide from the moving robot. However, during the practical building mapping, serious errors may happen due to the inappropriate installation of a MEMS-IMU. Through this study, we analyzed the different systematic errors, such as biases, scale errors, and axial installation deviation, that happened during the building mapping, based on a robot equipped with MEMS-IMU. Based on this, an error calibration model was developed. The problems of the deviation between the calibrated and horizontal planes were solved by a new sampling method. For this method, the calibrated plane was rotated twice; the gravity acceleration of the six sides of the MEMS-IMU was also calibrated by the practical values, and the whole calibration process was completed after solving developed model based on the least-squares method. Finally, the building mapping was then calibrated based on the error calibration model, and also the Gmapping algorithm. It was indicated from the experiments that the proposed model is useful for the error calibration, which can increase the prediction accuracy of yaw by 1–2° based on MEMS-IMU; the mapping results are more accurate when compared to the previous methods. The research outcomes can provide a practical basis for the construction of the building information modelling model.

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Han, Shipeng, Zhen Meng, Xingcheng Zhang, and Yuepeng Yan. "Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions." Micromachines 12, no. 2 (February 20, 2021): 214. http://dx.doi.org/10.3390/mi12020214.

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Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

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Le, Yang, Xiu Feng He, and Ru Ya Xiao. "MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter." Applied Mechanics and Materials 568-570 (June 2014): 970–75. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.970.

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This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

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Jin, Hai Wei, Jie Liu, Lan Zhang, and Xu Qian. "MEMS Micro-Era Missile-Borne." Applied Mechanics and Materials 705 (December 2014): 178–81. http://dx.doi.org/10.4028/www.scientific.net/amm.705.178.

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This article describes the basic meaning, features and foreign developments of the MEMS-IMU and the RF-MEMS technology, presents and analyzes the research and the actual application of the MEMS technology in missile guidance, navigation, data link, detection, control, etc. and the changes carried out by MEMS missile technology.

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Niu, Xiaoji, Sameh Nasser, Chris Goodall, and Naser El-Sheimy. "A Universal Approach for Processing any MEMS Inertial Sensor Configuration for Land-Vehicle Navigation." Journal of Navigation 60, no. 2 (April 20, 2007): 233–45. http://dx.doi.org/10.1017/s0373463307004213.

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Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

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Kuznetsov, A. G., V. I. Galkin, A. V. Molchanov, B. I. Portnov, and A. M. Yakubovich. "MEMS IMU: Development and flight test results." Gyroscopy and Navigation 3, no. 4 (October 2012): 255–64. http://dx.doi.org/10.1134/s2075108712040050.

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Brown, A. K. "GPS/INS uses low-cost MEMS IMU." IEEE Aerospace and Electronic Systems Magazine 20, no. 9 (September 2005): 3–10. http://dx.doi.org/10.1109/maes.2005.1514768.

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Ahmed, Haseeb, Ihsan Ullah, Uzair Khan, Muhammad Bilal Qureshi, Sajjad Manzoor, Nazeer Muhammad, Muhammad Usman Shahid Khan, and Raheel Nawaz. "Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory." Sensors 19, no. 24 (December 5, 2019): 5357. http://dx.doi.org/10.3390/s19245357.

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Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

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Abbate, Nunzio, Adriano Basile, Carmen Brigante, Alessandro Faulisi, and Fabrizio La Rosa. "Modern Breakthrough Technologies Enable New Applications Based on IMU Systems." Journal of Sensors 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/707498.

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This paper describes IMU (Inertial Measurement Unit) platforms and their main target applications with a special focus on the 10-degree-of-freedom (10-DOF) inertial platform iNEMO and its technical features and performances. The iNEMO module is equipped with a 3-axis MEMS accelerometer, a 3-axis MEMS gyroscope, a 3-axis MEMS magnetometer, a pressure sensor, and a temperature sensor. Furthermore, the Microcontroller Unit (MCU) collects measurements by the sensors and computes the orientation through a customized Extended Kalman Filter (EKF) for sensor fusion.

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Fu, Zhen Xian, Guang Ying Zhang, Yu Rong Lin, and Yang Liu. "Wireless Input Technology Based on MEMS Inertial Measurement Unit - A Survey." Applied Mechanics and Materials 870 (September 2017): 79–84. http://dx.doi.org/10.4028/www.scientific.net/amm.870.79.

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Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

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Gonzalez, Rodrigo, and Paolo Dabove. "Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation." Sensors 19, no. 18 (September 7, 2019): 3865. http://dx.doi.org/10.3390/s19183865.

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Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °.

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Du, Shuang, Wei Sun, and Yang Gao. "MEMS IMU Error Mitigation Using Rotation Modulation Technique." Sensors 16, no. 12 (November 29, 2016): 2017. http://dx.doi.org/10.3390/s16122017.

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Kim, Jeong Won, Chang Woo Nam, Jae-Cheul Lee, Sung Jin Yoon, and Jaewook Rhim. "Development of MEMS-IMU/GPS Integrated Navigation System." Journal of Positioning, Navigation, and Timing 3, no. 2 (June 15, 2014): 53–62. http://dx.doi.org/10.11003/jpnt.2014.3.2.053.

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Židek, Kamil, and Alexander Hošovský. "Wireless Device Based on MEMS Sensors and Bluetooth Low Energy (LE/Smart) Technology for Diagnostics of Mechatronic Systems." Applied Mechanics and Materials 460 (November 2013): 13–21. http://dx.doi.org/10.4028/www.scientific.net/amm.460.13.

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This paper deals with usability of MEMS sensors for diagnostics of mechatronics system state wirelessly. We can acquire basic kinematics and dynamics mechanism parameters (spatial position, speed, acceleration, vibration, angular rate, orientation, etc.) and some environment condition (local/remote temperature, humidity, pressure, electromagnetic noise) by MEMS sensors. Acquired data are sent to remote application in desktop computer. This system can replace expensive and separate diagnostic tools by small integrated solution with one wireless communication interface (with limitation of MEMS sensors precision). This solution can be battery powered with long operation time, because there is used new wireless technology based on Bluetooth 4 protocol (Low Energy/Smart Bluetooth). Some of integrated MEMS sensors measures same variable on different measuring principle. For example angle can be acquired from three different sensors: magnetometer, accelerometer or gyroscope. Combination of these sensor data can significantly improve value accuracy. The designed diagnostic tool can serve as an inertia measuring unit IMU or Wireless IMU (WIMU).

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Wahyudi, Adhi Susanto, Wahyu Widada, and Sasongko P. Hadi. "Simultaneous Calibration for MEMS Gyroscopes of the Rocket IMU." Advanced Materials Research 896 (February 2014): 656–59. http://dx.doi.org/10.4028/www.scientific.net/amr.896.656.

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MEMS (Microelectromechanical System), as an advanced sensor technology, is low power, low cost, and small size. Gyroscope sensor produced with microelectromechanical technology is an angular rate sensor. IMU (Inertial Measurement Unit) sensor for rocket should have a very wide range of measurements. At the beginning of the motion, the rocket accelereation is very high, for which the rocket IMU requires a multisensor with different sensitivity. This paper presents the design of the rocket IMU and its calibration method for all MEMS gyroscopes. Calibration for each sensor is necessary including its varying characteristics. The calibration of the gyroscope sensors use three-axis motion simulator model ST 3176 with resolutions 0.00001 for all axes. Simultaneous calibration was mutually applied which require a short calibration time. The results show that root mean square errors (RMSE) of the calibrated gyroscope for all axes are under 2.5 %. Therefore, that the calibrated gyroscope can be used in the proposed real application.

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Fukuda, Gen, Daisuke Hatta, Xiaoliang Guo, and Nobuaki Kubo. "Performance Evaluation of IMU and DVL Integration in Marine Navigation." Sensors 21, no. 4 (February 4, 2021): 1056. http://dx.doi.org/10.3390/s21041056.

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Global navigation satellite system (GNSS) spoofing poses a significant threat to maritime logistics. Many maritime electronic devices rely on GNSS time, positioning, and speed for safe vessel operation. In this study, inertial measurement unit (IMU) and Doppler velocity log (DVL) devices, which are important in the event of GNSS spoofing or outage, are considered in conventional navigation. A velocity integration method using IMU and DVL in terms of dead-reckoning is investigated in this study. GNSS has been widely used for ship navigation, but IMU, DVL, or combined IMU and DVL navigation have received little attention. Military-grade sensors are very expensive and generally cannot be utilized in smaller vessels. Therefore, this study focuses on the use of consumer-grade sensors. First, the performance of a micro electromechanical system (MEMS)-based yaw rate angle with DVL was evaluated using 60 min of raw data for a 50 m-long ship located in Tokyo Bay. Second, the performance of an IMU-MEMS using three gyroscopes and three accelerometers with DVL was evaluated using the same dataset. A gyrocompass, which is equipped on the ship, is used as a heading reference. The results proved that both methods could achieve less than 1 km horizontal error in 60 min.

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Ren, Yafei, and Xizhen Ke. "Particle Filter Data Fusion Enhancements for MEMS-IMU/GPS." Intelligent Information Management 02, no. 07 (2010): 417–21. http://dx.doi.org/10.4236/iim.2010.27051.

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Hasan, Md Abid, and Mohammad Nasimuzzaman Mishuk. "MEMS IMU Based Pedestrian Indoor Navigation for Smart Glass." Wireless Personal Communications 101, no. 1 (April 17, 2018): 287–303. http://dx.doi.org/10.1007/s11277-018-5688-3.

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Jafari, M., M. Sahebjameyan, B. Moshiri, and T. A. Najafabadi. "Skew redundant MEMS IMU calibration using a Kalman filter." Measurement Science and Technology 26, no. 10 (August 26, 2015): 105002. http://dx.doi.org/10.1088/0957-0233/26/10/105002.

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Emel’yantsev, G. I., A. P. Stepanov, and B. A. Blazhnov. "Aircraft navigation using MEMS IMU and ground radio beacons." Gyroscopy and Navigation 8, no. 3 (July 2017): 173–80. http://dx.doi.org/10.1134/s207510871703004x.

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Zhao, Hongyu, Zhelong Wang, Hong Shang, Weijian Hu, and Gao Qin. "A time‐controllable Allan variance method for MEMS IMU." Industrial Robot: An International Journal 40, no. 2 (March 2013): 111–20. http://dx.doi.org/10.1108/01439911311297702.

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Quoc, Dung Duong, Jinwei Sun, Van Nhu Le, and Nguyen Ngoc Tan. "Sensor Fusion based on Complementary Algorithms using MEMS IMU." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 2 (February 28, 2015): 313–24. http://dx.doi.org/10.14257/ijsip.2015.8.2.30.

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Ku, Do Yeou, and Amir Patel. "Kinematic State Estimation Using Multiple DGPS/MEMS-IMU Sensors." IEEE Sensors Letters 4, no. 12 (December 2020): 1–4. http://dx.doi.org/10.1109/lsens.2020.3040661.

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Geng, Xingshou, Kanghua Tang, and Meiping Wu. "Design of an MEMS-IMU/GNSS integrated navigation algorithm." Journal of Physics: Conference Series 1654 (October 2020): 012054. http://dx.doi.org/10.1088/1742-6596/1654/1/012054.

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Abdel-Hamid, W., T. Abdelazim, N. El-Sheimy, and G. Lachapelle. "Improvement of MEMS-IMU/GPS performance using fuzzy modeling." GPS Solutions 10, no. 1 (June 23, 2005): 1–11. http://dx.doi.org/10.1007/s10291-005-0146-6.

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Du, Shuang, Xudong Gan, Ruiqi Zhang, and Zebo Zhou. "The Integration of Rotary MEMS INS and GNSS with Artificial Neural Networks." Mathematical Problems in Engineering 2021 (January 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/6669682.

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The rotary INS (inertial navigation system) has been applied to compensate the navigation errors of the MEMS (micro-electro-mechanical-systems) INS recently. In such system, the PVA (position, velocity, and attitude) errors can be compensated through IMU (inertial measurement unit) carouseling. However, the navigation errors are only partially compensated due to the intrinsic property of the inertial system and the randomness of the IMU errors. In this paper, we present an integrated rotary MEMS INS/GNSS (global navigation satellite systems) system based on the ANN (artificial neural networks) technique. The ANFIS (adaptive neuro-fuzzy inference system) is applied to eliminate the residual PV (position and velocity) errors of the rotary MEMS INS during GNSS outages. A cascaded velocity-position structure is designed to recognize the pattern of the rotary MEMS INS PV errors and to reduce them of the rotary inertial system in standalone mode. The road tests are conducted with artificial GNSS outages to evaluate the ability of the integrated system to predict the PV errors. Compared to the position errors of the integrated rotary INS/GNSS system based on an EKF (extended Kalman filtering), they are reduced by 79.98% in the proposed system.

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Du, Shuang, Xudong Gan, Ruiqi Zhang, and Zebo Zhou. "The Integration of Rotary MEMS INS and GNSS with Artificial Neural Networks." Mathematical Problems in Engineering 2021 (January 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/6669682.

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The rotary INS (inertial navigation system) has been applied to compensate the navigation errors of the MEMS (micro-electro-mechanical-systems) INS recently. In such system, the PVA (position, velocity, and attitude) errors can be compensated through IMU (inertial measurement unit) carouseling. However, the navigation errors are only partially compensated due to the intrinsic property of the inertial system and the randomness of the IMU errors. In this paper, we present an integrated rotary MEMS INS/GNSS (global navigation satellite systems) system based on the ANN (artificial neural networks) technique. The ANFIS (adaptive neuro-fuzzy inference system) is applied to eliminate the residual PV (position and velocity) errors of the rotary MEMS INS during GNSS outages. A cascaded velocity-position structure is designed to recognize the pattern of the rotary MEMS INS PV errors and to reduce them of the rotary inertial system in standalone mode. The road tests are conducted with artificial GNSS outages to evaluate the ability of the integrated system to predict the PV errors. Compared to the position errors of the integrated rotary INS/GNSS system based on an EKF (extended Kalman filtering), they are reduced by 79.98% in the proposed system.

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Chow, J. C. K. "STATISTICAL SENSOR FUSION OF A 9-DOF MEMS IMU FOR INDOOR NAVIGATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 333–38. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-333-2017.

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Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8&ndash;89.5&thinsp;% and the dead-reckoned positioning accuracy by 72.9&ndash;92.8&thinsp;%, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user’s dynamics.

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Tsai, F., H. Chang, and A. Y. S. Su. "Combining MEMS-based IMU data and vision-based trajectory estimation." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4 (April 23, 2014): 267–71. http://dx.doi.org/10.5194/isprsarchives-xl-4-267-2014.

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This paper presents an efficient location tracking algorithm that integrates vision-based motion estimation and IMU data. Orientation and translation parameters of the mobile device are estimated from video frames or highly overlapped image sequences acquired with built-in cameras of mobile devices. IMU data are used to maintain continuity of the orientation estimation between sampling of the image homography calculation. The developed algorithm consists of six primary steps: (1) pre-processing; (2) feature points detection and matching; (3) homography calculation; (4) control points detection and registration; (5) motion estimation and filtering; (6) IMU data integration. The pre-processing of the input video frames or images is to control the sampling rate and image resolution in order to increase the computing efficiency. The overlap rate between selected frames is designed to remain above 60 % for matching. After preprocessing, feature points will be extracted and matched between adjacent frames as the conjugate points. A perspective homography is constructed and used to map one image to another if the co-planar feature points between subsequent images are fully matched. The homography matrix can provide the camera orientation and translation parameters according to the conjugate pairs. An area-based image-matching method is employed to recognize landmarks as reference nodes (RNs). In addition, a filtering mechanism is proposed to ensure the rotation angle was correctly recorded and to increase the tracking accuracy. Comparisons of the trajectory results with different combinations among vision-based motion estimation, filtering mechanism and IMU data integration are evaluated thoroughly and the accuracy is validated with on-site measurement data. Experimental results indicate that the develop algorithm can effectively estimate the trajectory of moving mobile devices and can be used as a cost-effective alternative for LBS device both in outdoor and indoor environment.

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Sabatini, Roberto, Celia Bartel, Anish Kaharkar, Tesheen Shaid, Leopoldo Rodriguez, David Zammit-Mangion, and Huamin Jia. "Low-Cost Navigation and Guidance Systems for Unmanned Aerial Vehicles — Part 1: Vision-Based and Integrated Sensors." Annual of Navigation 19, no. 2 (December 1, 2012): 71–98. http://dx.doi.org/10.2478/v10367-012-0019-3.

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Abstract In this paper we present a new low-cost navigation system designed for small size Unmanned Aerial Vehicles (UAVs) based on Vision-Based Navigation (VBN) and other avionics sensors. The main objective of our research was to design a compact, light and relatively inexpensive system capable of providing the Required Navigation Performance (RNP) in all phases of flight of a small UAV, with a special focus on precision approach and landing, where Vision Based Navigation (VBN) techniques can be fully exploited in a multisensor integrated architecture. Various existing techniques for VBN were compared and the Appearance-Based Approach (ABA) was selected for implementation. Feature extraction and optical flow techniques were employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway and body rates. Additionally, we addressed the possible synergies between VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors, as well as the aiding from Aircraft Dynamics Models (ADMs). In particular, by employing these sensors/models, we aimed to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. An Extended Kalman Filter (EKF) was developed to fuse the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Two different integrated navigation system architectures were implemented. The first used VBN at 20 Hz and GPS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also included the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. Simulation of these two modes was accomplished in a significant portion of the AEROSONDE UAV operational flight envelope and performing a variety of representative manoeuvres (i.e., straight climb, level turning, turning descent and climb, straight descent, etc.). Simulation of the first integrated navigation system architecture (VBN/IMU/GPS) showed that the integrated system can reach position, velocity and attitude accuracies compatible with CAT-II precision approach requirements. Simulation of the second system architecture (VBN/IMU/GPS/ADM) also showed promising results since the achieved attitude accuracy was higher using the ADM/VBS/IMU than using VBS/IMU only. However, due to rapid divergence of the ADM virtual sensor, there was a need for frequent re-initialisation of the ADM data module, which was strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed

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Nüchter, A., D. Borrmann, P. Koch, M. Kühn, and S. May. "A MAN-PORTABLE, IMU-FREE MOBILE MAPPING SYSTEM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 17–23. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-17-2015.

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Mobile mapping systems are commonly mounted on cars, ships and robots. The data is directly geo-referenced using GPS data and expensive IMU (inertial measurement systems). Driven by the need for flexible, indoor mapping systems we present an inexpensive mobile mapping solution that can be mounted on a backpack. It combines a horizontally mounted 2D profiler with a constantly spinning 3D laser scanner. The initial system featuring a low-cost MEMS IMU was revealed and demonstrated at <i>MoLaS: Technology Workshop Mobile Laser Scanning at Fraunhofer IPM</i> in Freiburg in November 2014. In this paper, we present an IMU-free solution.

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Abdulrahim, Khairi, Chris Hide, Terry Moore, and Chris Hill. "Using Constraints for Shoe Mounted Indoor Pedestrian Navigation." Journal of Navigation 65, no. 1 (November 25, 2011): 15–28. http://dx.doi.org/10.1017/s0373463311000518.

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Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

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Sekiya, Hidehiko, Takeshi Kinomoto, and Chitoshi Miki. "Determination Method of Bridge Rotation Angle Response Using MEMS IMU." Sensors 16, no. 11 (November 9, 2016): 1882. http://dx.doi.org/10.3390/s16111882.

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Kuang, Jian, Xiaoji Niu, and Xingeng Chen. "Robust Pedestrian Dead Reckoning Based on MEMS-IMU for Smartphones." Sensors 18, no. 5 (May 1, 2018): 1391. http://dx.doi.org/10.3390/s18051391.

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Abosekeen, A., and A. Abdalla. "Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System." International Conference on Electrical Engineering 8, no. 8th (May 1, 2012): 1–23. http://dx.doi.org/10.21608/iceeng.2012.30810.

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Miao, Z. Y., F. Shen, D. J. Xu, C. M. Tian, and K. P. He. "Online estimation method of Allan variance coefficients for MEMS IMU." Journal of Instrumentation 9, no. 09 (September 2, 2014): P09001. http://dx.doi.org/10.1088/1748-0221/9/09/p09001.

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Georgy, Jacques, Tashfeen Karamat, Umar Iqbal, and Aboelmagd Noureldin. "Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter." GPS Solutions 15, no. 3 (September 29, 2010): 239–52. http://dx.doi.org/10.1007/s10291-010-0186-4.

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Journal articles on the topic 'MEMS IMU'

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