Relevant bibliographies by topics / Directional cosine matrix filter

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Directional cosine matrix filter'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Directional cosine matrix filter"

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Choukroun, D., H. Weiss, I. Y. Bar-Itzhack, and Y. Oshman. "Direction Cosine Matrix Estimation from Vector Observations using a Matrix Kalman Filter." IEEE Transactions on Aerospace and Electronic Systems 46, no. 1 (2010): 61–79. http://dx.doi.org/10.1109/taes.2010.5417148.

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Bieda, Robert, and Krzysztof Jaskot. "Determinig of an object orientation in 3D space using direction cosine matrix and non-stationary Kalman filter." Archives of Control Sciences 26, no. 2 (2016): 223–44. http://dx.doi.org/10.1515/acsc-2016-0013.

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Abstract This paper describes a method which determines the parameters of an object orientation in 3D space. The rotation angles calculation bases on the signals fusion obtained from the inertial measurement unit (IMU). The IMU measuring system provides information from a linear acceleration sensors (accelerometers), the Earth’s magnetic field sensors (magnetometers) and the angular velocity sensors (gyroscopes). Information about the object orientation is presented in the form of direction cosine matrix whose elements are observed in the state vector of the non-stationary Kalman filter. The vector components allow to determine the rotation angles (roll, pitch and yaw) associated with the object. The resulting waveforms, for different rotation angles, have no negative attributes associated with the construction and operation of the IMU measuring system. The described solution enables simple, fast and effective implementation of the proposed method in the IMU measuring systems.
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Hall, Derek, and Timothy Sands. "Vehicle Directional Cosine Calculation Method." Vehicles 5, no. 1 (2023): 114–32. http://dx.doi.org/10.3390/vehicles5010008.

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Teaching kinematic rotations is a daunting task for even some of the most advanced mathematical minds. However, changing the paradigm can highly simplify envisioning and explaining the three-dimensional rotations. This paradigm change allows a high school student with an understanding of geometry to develop the matrix and explain the rotations at a collegiate level. The proposed method includes the assumption of a point (P) within the initial three-dimensional frame with axes (x^i, y^i, z^i). The method then utilizes a two-dimensional rotation view (2DRV) to measure how the coordinates of point P translate after a rotation around the initial axis. The equations are used in matrix notation to develop a rotation matrix for follow-on direction cosine matrixes. The method removes the requirement to use Euler’s formula, ultimately, providing a high school student with an elementary and repeatable process to compose and explain kinematic rotations, which are critical to attitude direction control systems commonly found in vehicles.
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Zhang, Dong, Yongshun Zhang, and Cunqian Feng. "Enhanced 2D-DOA Estimation for Large Spacing Three-Parallel Uniform Linear Arrays." International Journal of Antennas and Propagation 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/3679494.

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An enhanced two-dimensional direction of arrival (2D-DOA) estimation algorithm for large spacing three-parallel uniform linear arrays (ULAs) is proposed in this paper. Firstly, we use the propagator method (PM) to get the highly accurate but ambiguous estimation of directional cosine. Then, we use the relationship between the directional cosine to eliminate the ambiguity. This algorithm not only can make use of the elements of the three-parallel ULAs but also can utilize the connection between directional cosine to improve the estimation accuracy. Besides, it has satisfied estimation performance when the elevation angle is between 70° and 90° and it can automatically pair the estimated azimuth and elevation angles. Furthermore, it has low complexity without using any eigen value decomposition (EVD) or singular value decompostion (SVD) to the covariance matrix. Simulation results demonstrate the effectiveness of our proposed algorithm.
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Kim, Y. V. "Rotation vector and directional cosine matrix in problems of satellite attitude control." International Journal of Aerospace System Science and Engineering 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijasse.2021.10036566.

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Kim, Y. V. "Rotation vector and directional cosine matrix in problems of satellite attitude control." International Journal of Intelligent Systems Technologies and Applications 20, no. 5 (2022): 363. http://dx.doi.org/10.1504/ijista.2022.125583.

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Ryzhkov, Lev. "Vector methods for determination of rigid body orientation." MECHANICS OF GYROSCOPIC SYSTEMS, no. 41 (December 28, 2022): 102–11. http://dx.doi.org/10.20535/0203-3771412021269249.

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Vector methods for determining the orientation of a rigid body using information about vectors in the reference and body-related coordinate systems are considered. The analysis is based on the least squares method in vector-matrix form. This approach allows us to consider the application of different methods of solving the problem from a single standpoint: matrix (using the directional cosine matrix), geometric (using the Gibbs vector), quaternion (using the quaternion of rotation). Numerical evaluation of the accuracy of eight orientation determination algorithms obtained on the basis of these methods was performed.
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Wu, Guoli, Hefeng Dong, Ganpan Ke, and Junqiang Song. "An adapted eigenvalue-based filter for ocean ambient noise processing." GEOPHYSICS 85, no. 1 (2020): KS29—KS38. http://dx.doi.org/10.1190/geo2018-0861.1.

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Accurate approximations of Green’s functions retrieved from the correlations of ambient noise require a homogeneous distribution of random and uncorrelated noise sources. In the real world, the existence of highly coherent, strong directional noise generated by ships, earthquakes, and other human activities can result in biases in the ambient-noise crosscorrelations (NCCs). We have developed an adapted eigenvalue-based filter to attenuate the interference of strong directional sources. The filter is based on the statistical model of the sample covariance matrix and can separate different components of the data covariance matrix in the eigenvalue spectrum. To improve the effectiveness and make it adaptable for different data sets, a weight is introduced to the filter. Then, the NCCs can be calculated directly from the filtered data covariance matrix. This approach is applied to a 1.02 h data set of ambient noise recorded by a permanent reservoir monitoring receiver array installed on the seabed. The power spectral density indicates that the noise recordings were contaminated by strong directional noise over nearly half of the whole observation period. Beamforming and crosscorrelation results indicate that the interference still exists even after applying traditional temporal and spectral normalization techniques, whereas the adapted eigenvalue-based filter can significantly attenuate it and help to obtain improved crosscorrelations. The approach makes it possible to retrieve reliable approximations of Green’s functions over a much shorter recording time.
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Lim, SuYeon, MinKyeong Choi, and Yunglyul Lee. "Frequency-Based Adaptive Interpolation Filter in Intra Prediction." Applied Sciences 13, no. 3 (2023): 1475. http://dx.doi.org/10.3390/app13031475.

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This paper proposes a method to improve the fractional interpolation of reference samples in the Versatile Video Coding (VVC) intra prediction. The proposed method uses additional interpolation filters which use more integer-positioned reference samples for prediction according to the frequency information of the reference samples. In VVC, a 4-tap Discrete Cosine Transform-based interpolation filter (DCT-IF) and 4-tap Smoothing interpolation filter (SIF) are alternatively performed on the block size and block directional prediction mode for reference sample interpolation. This paper uses four alternative interpolation filters such as 8-tap/4-tap DCT-IFs, and 4-tap/8-tap SIFs and an interpolation filter selection method using a high-frequency ratio calculated from one-dimensional (1D) transform of the reference samples are proposed. The proposed frequency-based Adaptive Filter allows to achieve the overall Bjøntegaard Delta (BD) rate gains of −0.16%, −0.13%, and −0.09% for Y, Cb, and Cr components, respectively, compared with VVC.
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Phuong, Nguyen Ho Quoc, Hee-Jun Kang, Young-Soo Suh, and Young-Sik Ro. "A DCM Based Orientation Estimation Algorithm with an Inertial Measurement Unit and a Magnetic Compass." JUCS - Journal of Universal Computer Science 15, no. (4) (2009): 859–76. https://doi.org/10.3217/jucs-015-04-0859.

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In this paper, Direction Cosine Matrix (DCM) method for attitude and orientation estimation is discussed. DCM method was chosen due to some advantages over the popular methods such as namely Euler Angle, Quaternion in light of reliability, accuracy and computational efforts. Proposed model for each method is developed for methodology comparison. It is shown that normal Kalman Filter in DCM method is better than extended Kalman Filter in Euler and Quaternion based method because it helps avoid the first order approximation error. Methodology errors are verified using Aerospace Blockset of Matlab Simulink.
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Dissertations / Theses on the topic "Directional cosine matrix filter"

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Bouffard, Joshua Lee. "An Alternative Sensor Fusion Method For Object Orientation Using Low-Cost Mems Inertial Sensors." ScholarWorks @ UVM, 2016. https://scholarworks.uvm.edu/graddis/537.

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This thesis develops an alternative sensor fusion approach for object orientation using low-cost MEMS inertial sensors. The alternative approach focuses on the unique challenges of small UAVs. Such challenges include the vibrational induced noise onto the accelerometer and bias offset errors of the rate gyroscope. To overcome these challenges, a sensor fusion algorithm combines the measured data from the accelerometer and rate gyroscope to achieve a single output free from vibrational noise and bias offset errors. One of the most prevalent sensor fusion algorithms used for orientation estimation is the Extended Kalman filter (EKF). The EKF filter performs the fusion process by first creating the process model using the nonlinear equations of motion and then establishing a measurement model. With the process and measurement models established, the filter operates by propagating the mean and covariance of the states through time. The success of EKF relies on the ability to establish a representative process and measurement model of the system. In most applications, the EKF measurement model utilizes the accelerometer and GPS-derived accelerations to determine an estimate of the orientation. However, if the GPS-derived accelerations are not available then the measurement model becomes less reliable when subjected to harsh vibrational environments. This situation led to the alternative approach, which focuses on the correlation between the rate gyroscope and accelerometer-derived angle. The correlation between the two sensors then determines how much the algorithm will use one sensor over the other. The result is a measurement that does not suffer from the vibrational noise or from bias offset errors.
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Miller, William H. "Analog Implementation of DVM and Farrow Filter Based Beamforming Algorithms for Audio Frequencies." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1531951902410037.

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Book chapters on the topic "Directional cosine matrix filter"

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Britanak, Vladimir, and K. R. Rao. "MDCT/MDST, MLT, ELT, and MCLT Filter Banks: Definitions, General Properties, and Matrix Representations." In Cosine-/Sine-Modulated Filter Banks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61080-1_3.

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Yong, Li, Zhang Zhiyuan, Zhao Zhongshi, and Li Zhiyong. "Research on Attitude Updating Algorithm for Strapdown Inertial Navigation of Long-Range Precision-Guided Projectile Based on Directional Cosine Matrix." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0238-5_74.

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Conference papers on the topic "Directional cosine matrix filter"

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Van Long, Hoang. "Applying Neural Networks to Improve the Accuracy of the Directional Cosine Matrix Between the Gimbal Coordinate System and Geographic Coordinate System." In 2024 International Conference on Control, Robotics and Informatics (ICCRI). IEEE, 2024. https://doi.org/10.1109/iccri64298.2024.00017.

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Choukroun, Daniel, Haim Weiss, Itzhack Bar-Itzhack, and Yaakov Oshman. "DIRECTION COSINE MATRIX ESTIMATION FROM VECTOR OBSERVATIONS USING A MATRIX KALMAN FILTER." In AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-5482.

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Hodes, Alex T., Carl P. Weiner, Huazhen Fang, Sarah K. Kieweg, and Sara E. Wilson. "Filtering Techniques for Accurate Identification of Clinician Hand Posture." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52290.

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Inertial and magnetic sensors are commonly used to determine orientation as they do not rely on a line of sight [1, 2]. There are many different techniques to fuse inertial measurement unit (IMU) data and obtain useful rotational data [1–3]. This study uses two separate data fusion techniques; a direction cosine matrix-based (DCM) technique and a quaternion-based Extended Kalman Filter (EKF) technique [1–3]. These techniques were altered based on performance metrics to weight sensor data when certain sensors proved not as reliable as others [2]. IMU sensors were tested on a hand mannequin and filters were developed using MATLAB software. Simulation results displayed a root-mean-squared error of less than .06° for each rotation angle. Experimental results maintained errors of less than 8° in each rotation angle.
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Wang, Yan, and Rajesh Rajamani. "Attitude Estimation With a 9-Axis MEMS Based Motion Tracking Sensor." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9700.

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This paper discusses the development of the attitude estimation algorithm for a MEMS based 9-axis motion tracking sensor, which includes a tri-axis accelerometer, a tri-axis gyroscope and a tri-axis magnetometer. The comparison between the Euler angles and the direction cosine matrix (DCM) based approach is presented to illustrate the advantage of DCM. It will be shown that the kinematic model for DCM can be transformed into a linear time-varying state space form, which greatly simplifies the development of the estimation algorithm. Different from the existing estimation algorithms, which incorporate a nonlinear kinematic model and the nonlinear Kalman filter, such as extended Kalman filter (EKF) or unscented Kalman filter (UKF), the non-linearity in the kinematic model is not the trouble maker anymore. Hence, global convergence can always be guaranteed. Finally, the estimation algorithm is demonstrated by using the real measurement data collected from InvenSenses MPU9250, which is one of the most popular 9-axis motion tracking sensors in the market.
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Nomura, Yusuke, Ryutaro Ogawa, Seisuke Kyochi, and Taizo Suzuki. "Multiscale directional transforms based on cosine-sine modulated filter banks for sparse directional image representation." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282331.

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Georgiou, Evangelos, Peter L. Morgan, Jonathan San Diego, Jian Dai, and Michael Luck. "Self Localization Using a 9DOF IMU Sensor With a Directional Cosine Matrix." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12906.

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In the field of robotics, the most essential requirement for successful navigation is an accurate and numerically inexpensive method for self-localization. This paper presents a method that exploits the principles of directional cosines to setup a rotation matrix to deal with a closed loop PI feedback based model. The system uses a 9 degree of freedom (9DOF) sensor and exploits the benefits of the accuracy of a 3-axis gyroscope by leveraging the measurement of a 3-axis accelerometer and 3-axis magnetometer, which compensates and corrects the accumulative drift generated by integrating the gyroscope velocity measurements. The results show that the method is relatively accurate with a small level of error when compared to vision based glyph recognition and tracking methods for self-localization and is a sustainable method for removing accumulative drift.
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Ogawa, Ryutaro, Seisuke Kyochi, and Taizo Suzuki. "Design of oversampled cosine-sine modulated filter banks for directional image representation." In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015. http://dx.doi.org/10.1109/apsipa.2015.7415296.

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Tseng, Chien-Cheng, and Su-Ling Lee. "Design of Matrix Filter Using Discrete Cosine Transform and Path Graph." In 2022 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2022. http://dx.doi.org/10.1109/apccas55924.2022.10090257.

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Tseng, Chien-Cheng, and Su-Ling Lee. "A sampling rate increasing method using matrix low-pass filter and discrete cosine transform." In 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, 2016. http://dx.doi.org/10.1109/icce-tw.2016.7520916.

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Chen, Jun, R. C. Jaeger, and J. C. Suhling. "Piezoresistive Theory for 4H Silicon Carbide Stress Sensors on Four-Degree Off-Axis Wafers." In ASME 2019 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ipack2019-6461.

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Abstract Piezoresistive stress sensors have been shown to be a powerful tool for experimental evaluation of die stress distributions. Silicon Carbide (SiC) wide bandgap semiconductors are promising materials for development of high temperature power electronics. In the past, the analysis and design of stress sensors on silicon carbide have assumed that the wafer surface is aligned with the crystallographic axes. However, 4H silicon carbide wafers are produced with a four-degree off-axis cut to ensure high-quality homoepitaxial growth, so that the tilted wafer surface does not perfectly coincide with the fundamental crystallographic axis. Thus, the prior “on-axis” theory is an approximation, and errors in piezoresistive theory caused by such tilted wafer plane need to be discussed. These errors can affect both the results from calibration experiments as well as the stresses extracted during application of sensor rosettes. This paper discusses the theory and extraction of piezoresistive coefficients for 4H-SiC silicon carbide materials in the presence of off-axis starting wafers. Coordinate transformations for the piezoresistive coefficients are reviewed, the required direction cosines between the new rotated axes and the ideal axes are discussed, and the 6 × 6 matrix of piezoresistive coefficients (π-matrix) for the on-axis and off-axis cases are calculated. Many of the elements of the ideal on-axis π-matrixes are zero. In contrast, the off-axis is completely filled with non-zero values indicating additional coupling, particularly among the shear coefficients. Examples of the overall impact of these new terms on calibration and stress measurement are discussed.
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