A wireless micro inertial measurement unit (IMU)

Abstract

Abstract-In this paper, we present a wireless micro inertial measurement unit (IMU) with the smallest volume and weight requirements available at the moment. With a size of 18 mm x 16 mm x 4 mm, this IMU provides full control over the data of a three-axis accelerometer, a three-axis gyroscope, and a threeaxis magnetometer. It meets the design prerequisites of a spacesaving design and eliminates the need of a hard-wired data communication while still being comparable to state of the art commercially available MEMS IMUs. A CC430 microcontroller sends the collected raw data to a base station wirelessly with a maximum sensor sample rate of 640 samples per second. Thereby, the IMU performance is optimized by moving data post processing to the base station. This development offers important features in embedded microsystem applications with their significant size and weight requirements.

Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

“…Data denoted as "Ref," "GyroQuaternion," "Acc," "Gyro-Euler," and "GPS," respectively, are derived from referenced Mti-G, gyroscope integration based on quaternion algorithm, accelerometer, gyroscope integration based on Euler algorithm, and the yaw angle measured by GPS-pseudoattitude algorithm. Figure 6 shows that the gyro-based attitudes derived from quaternion and Euler algorithm have short-term accuracy, which suffers from long-term accumulated errors caused by gyroscope bias drift with reference to (5) and (16). The attitude algorithm by accelerometer could provide roll and pitch estimation of long-term accuracy but suffers from sensor errors and vibration noises due to inevitable disturbances and dynamic motions with reference to (10).…”

“…It has obvious advantages over the traditional centralized EKF algorithm, but heavy calculation burden could not satisfy the demands of real time processing. The other most widely researched method is that the attitude estimate using CF fusion algorithm is utilized as the state coefficient [26] or measurement vector [16,17,25] in modified Kalman filter, which could be regarded as one benchmark algorithm in the field of GPS and INS data fusion. Among this category algorithm, the quaternion estimation algorithm (QUEST) [3,11,25] is usually used as attitude algorithm for Micro IMU, which could remove the adverse effect by linear acceleration of the navigation system to attain good dynamic performance.…”

“…Nowadays, the different attitude estimation algorithms have been developed, which are employed to Micro IMUs and integrated with magnetic sensors, Global navigation satellite system (GNSS) receiver, and other sensors [1][2][3][4][10][11][12][13][14][15][16][17]. Among them, representative researches like multiinformation fusion technique utilize the different MEMS sensor [10][11][12], different Kalman filtering methods for attitude estimation [3,[13][14][15], and methodology combining data fusion with filtering strategies [16,17].…”

“…Among them, representative researches like multiinformation fusion technique utilize the different MEMS sensor [10][11][12], different Kalman filtering methods for attitude estimation [3,[13][14][15], and methodology combining data fusion with filtering strategies [16,17]. Utilizing barometer and low cost IMU, the data fusion method via the complementary filter is proposed to fuse altitude data for unmanned aerial vehicle (UAV) system [10].…”

“…The data fusion is implemented before a Kalman filter combined with the zero velocity update. The methodology for wireless IMU is adopted to track a person in an indoor area [16]. Integrating double-antenna Global Positioning System (DA-GPS) with other low cost in-vehicle sensors, two parallel extended Kalman filters (EKFs) are adopted to estimate the roll angle firstly; then vehicle sideslip and yaw angles are estimated through fusion methodology [17].…”