Inertial sensor arrays — A literature review

Nilsson, John-Olof; Skog, Isaac

doi:10.1109/euronav.2016.7530551

Cited by 77 publications

(43 citation statements)

References 187 publications

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“…Here J hs denotes the Jacobian of h s (ω). From the Fisher information matrix in (16) we can see that for the considered type of array, the CRB for the specific force is given by…”

Section: A Square Arrays With Uncorrelated Measurement Errorsmentioning

confidence: 99%

Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Skog

Nilsson

Händel

et al. 2016

IEEE Trans. Signal Process.

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Abstract-A maximum likelihood estimator for fusing the measurements in an inertial sensor array is presented. The maximum likelihood estimator is concentrated and an iterative solution method is presented for the resulting low-dimensional optimization problem. The Cramér-Rao bound for the corresponding measurement fusion problem is derived and used to assess the performance of the proposed method, as well as to analyze how the geometry of the array and sensor errors affect the accuracy of the measurement fusion. The angular velocity information gained from the accelerometers in the array is shown to be proportional to the square of the array dimension and to the square of the angular speed. In our simulations the proposed fusion method attains the Cramér-Rao bound and outperforms the current state-of-the-art method for measurement fusion in accelerometer arrays. Further, in contrast to the state-of-theart method that requires a 3D array to work, the proposed method also works for 2D arrays. The theoretical findings are compared to results from real-world experiments with an inhouse developed array that consists of 192 sensing elements.

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“…Here J hs denotes the Jacobian of h s (ω). From the Fisher information matrix in (16) we can see that for the considered type of array, the CRB for the specific force is given by…”

Section: A Square Arrays With Uncorrelated Measurement Errorsmentioning

confidence: 99%

Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Skog

Nilsson

Händel

et al. 2016

IEEE Trans. Signal Process.

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“…Published in 2016, Nilsson and Skog assembled an impressive literature review on the subject of MEMS sensor arrays that became a valuable resource for this investigation [19]. They give a very brief explanation of the paradigms common to sensor array research and reference over 300 publications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The code written in the Arduino language (C++) for the recoding of measurements from the IMUs is given in this appendix. // ---Encoder ---: Encoder encoder (18,19); float encoderRatio = 4096.0/180.0; float encoderAngle; // ---IMU_0 ---: MPU9255 IMU_0; int16_t accelVect_0 [3]; int16_t gyroVect_0 [3]; int16_t magVect_0 [3]; int16_t tempData_0; // ---IMU_1 ---: MPU9255 IMU_1; int16_t accelVect_1 [3]; int16_t gyroVect_1 [3]; int16_t magVect_1 [3]; int16_t tempData_1; // Timer Variables: float loopTime = 0; // (sec) unsigned long loopTime_desired = 10000; // (micro-seconds)adjust for desired cycle rate -10000 for Hz, 92000 for 95Hz float totalTime = 0; // (sec) unsigned long timerStart = 0; // (sec) void setup() // Setup Loop { Serial.begin(115200); //Start the serial connection.…”

Section: Appendix A: Optimized Kalman Filter Codementioning

confidence: 99%

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Milam¹

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Most small unmanned aerial systems in use today, employ extended Kalman filter sensor fusion algorithms in order to provide accurate estimations of attitude or orientation. These complex algorithms use measurements from GPS receivers and magnetometer sensors that can be rendered useless in GPS denied environments or areas of significant magnetic interference, such as inside buildings or other structures. The complexity of these algorithms makes them inaccessible for some researchers and hobbyists who wish to code their own attitude estimation algorithms. This complexity is also computationally expensive and requires processors that are powerful enough to operate the algorithms along with any command and control functions required by the application. In contrast, there are simple sensor fusion algorithms such as the complementary filter or linear Kalman filter, that are commonly used by hobbyists because they are relatively easy to implement and computationally lightweight. However, these methods are not as accurate as the extended Kalman filter and therefore, are not adequate for some of the emerging precision applications in aerial robotics. The goal of this research is to investigate an attitude estimation algorithm that uses two separate inertial measurement units (IMUs), each consisting of tri-axis accelerometers and tri-axis gyroscopes. This dual or twin IMU (TIMU) algorithm is compared to several common algorithms that only use one IMU, such as the complementary filter and linear Kalman filter. Analysis of a one degree of freedom experiment shows that the TIMU algorithm provides a more accurate attitude estimate. The analysis also shows that distance between the IMU and the rotating body's center of gravity can have an inverse effect on attitude accuracy. The ability of the algorithms to provide an accurate estimate of the rate of attitude change is used as a performance metric, in addition to the accuracy of attitude estimates. The complexity of the twin IMU algorithm is kept to a minimum. It is presented in a way that can be easily programed by the layman and has a small computational footprint.

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“…The module for digital processing enables the implementation of a number of algorithms, related to improving the quality of the measurement information [6,7,8,9]. These algorithms can be based on mathematical models, allowing both the expansion of the types of the measurement information, and the increase of the accuracy of measurement [10]. It is through this module that algorithms for correction of the error from nonlinearity can be implemented.…”

Section: Fig1 Structural Diagram Of a Measuring Instrumentmentioning

confidence: 99%

Method of Analysis and correction of the Error from Nonlinearity of the Measurement Instruments

Dichev¹,

Kogia²,

Koev³

et al. 2016

JESTR

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In this work are examined the characteristics of the error from nonlinearity of the static characteristic of the measuring instruments. The possibilities to correct this error are analyzed. Mathematical models are presented, in order to allow evaluation of the error from nonlinearity regarding the permissible error of the measurement result, as well as to draw the necessary algorithms for correction of this error. The models are developed, based on the method of the smallest modules.

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Inertial sensor arrays — A literature review

Cited by 77 publications

References 187 publications

Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Method of Analysis and correction of the Error from Nonlinearity of the Measurement Instruments

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