Development of an analytical model for rotational vector of a sphere using MEMS inertial sensors

Microelectromechanical Systems (MEMS) based inertial sensors are finding greater applications in sensing position, orientation and motion in automotives, aerospace and consumer electronics [1]-[2]. One particular MEMS inertial sensor is an inertial measurement unit (IMU), which is an integrated chip consisting of a tri-axis gyroscope and tri-axis accelerometer. A gyroscope can sense rate of rotation in a particular axis while an accelerometer can measure the acceleration in an axis. IMUs are used to find the complete motion data which can be processed with appropriate algorithm to find orientation or navigation [3]. The focus of this research is to use a MEMS IMU to find the rotational velocity of a sphere and its axis of rotation. This research aims to build a model using detected motion data from the IMU then use it to calculate the rotation vector (speed and orientation) of an object, so that a great quantity of applications could be achieved. One of these applications we are researching is to detect the rotation of a sphere. Rotation about an arbitrary axis had been researched, however, to calculate the rotation axis from the detected IMU motion data is challenging and has not been addressed in the literature. To solve the problem, we used linear algebra as the tool to calculate the rotation matrix by dividing a 3-D rotation into several pieces of 2-D rotation [4]. The rotation motions were represented as a matrix, which could simplify the process of calculation. Simultaneous orthogonal rotation angle (SORA) concept was also important in this research because it is well-suited to calculate real-time rotation vectors [5]. As 3-D rotations in general are not commutative, the results of an improper sequential addition of the 3 rotation motions in 3-D would lead to a wrong orientation. However, only if the rotation angle is infinitely small, then the error could negligible because they are nearly commutative. Therefore, we divided the rotation angles into infinitesimally small rotations then we integrated the three rotations together. In the experiments, an IMU was placed on a spherical object and mounted on top of a rotating table. The information from IMU was sampled at 375Hz and was collected by a coupled microprocessor. The IMU data provided raw information about dynamic motion in all three axes. Using the IMU data and SORA algorithm the rotational axis and velocity of a moving rotational sphere was found. The outcome of this research achieved to detect rotation axis based on IMU sensors, which was impossible in the past. Key words: MEMS, IMU, SORA, Gyroscop

Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method

Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments

Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient (GCG) framework and the alternating minimization (AltMin) approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed estimator and achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimators in terms of estimation performance, computational complexity and robustness against array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS channel estimator.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications

This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extreme learning machine (ELM) based non-iterative receivers and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we also develop a data-aided receiver, where data is used as virtual training sequence in ELM training. It is shown that the ELM based receivers significantly outperform conventional polynomial based receivers; iterative receivers can achieve huge performance gain compared to non-iterative receivers; and the data-aided receiver can reduce training overhead considerably. This work can also be extended to radio frequency communications, e.g., to deal with the nonlinearity of power amplifiers