A design and realization of FPGA-based IMU data acquisition system

Bai, Changrui; Zhang, Zhou; Han, Xiaoying

doi:10.1109/edssc.2011.6117656

Cited by 5 publications

(6 citation statements)

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“…The circuit scheme is much larger than the designed chip in size, but it has a DSP on the board and is stronger in computing power than the designed chip. The solutions in [10,[12][13][14] are implemented based on FPGA. We will also implement the design on an FPGA [26].…”

Section: Performance Test Results Of the Chipmentioning

confidence: 99%

“…For more general IMU data acquisition and processing systems, there is no need to provide a definitive execution algorithm but rather an interface or other means to support other data processing algorithms. The system in [10] is an example. It solved the synchronization problems when data acquiring and accomplished redundant design for the system.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of an On-Chip Low-Power and High-Flexibility System for Data Acquisition and Processing of an Inertial Measurement Unit

Gao

Zhou

et al. 2020

Sensors

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For signal processing of a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU), a digital-analog hybrid system-on-chip (SoC) with small area and low power consumption was designed and implemented in this paper. To increase the flexibility of the processing circuit, the designed SoC integrates a low-power processor and supports three startup or debugging modes for different application scenarios. An application-specific computing module and communication interface are designed in the circuit to meet the requirements of IMU signal processing. The configurable clock allows users to dynamically balance computing speed and power consumption in their applications. The chip was taped out under SMIC 180 nm CMOS technology and tested for performance. The results show that the chip’s maximum running frequency is 105 MHz. The total area is 33.94 mm 2 . The dynamic and static power consumption are 0.65 mW/MHz and 0.30 mW/MHz, respectively. When the system clock is 25 MHz, the dynamic and static power consumption of the chip is 76 mW and 66 mW, and the dynamic and static power consumption of the FPGA level are 634 mW and 520 mW. The results verify the superiority of the application specific integrated circuit (ASIC) solution in terms of integration and low power consumption.

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Section: Performance Test Results Of the Chipmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an On-Chip Low-Power and High-Flexibility System for Data Acquisition and Processing of an Inertial Measurement Unit

Gao

Zhou

et al. 2020

Sensors

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“…Compared with hardware architecture of an IMU which normally provides original raw data of inertial sensor measurements [14] only, AHRS usually has an additional on-board processing engine for attitude and heading computation [29]. Owing to MEMS technology, researchers are able to build a miniature inertial measurement unit (MIMU).…”

Section: General Hardware Architecture Of Ahrsmentioning

confidence: 99%

“…For solving (11), RK4 (4-order Rungse-Kutta), a numerical solution for differential equations, is adopted for attitude and heading estimation in high frequency region. We simplify (11) into (14) for convenience of explaining RK4. According to RK4, attitude quaternions can be recursively figured out as (15), where 1 , 2 , 3 , and 4 are given by (16) to (18) and represents the elapsed time between two sequential quaternions:…”

Section: Ahrs Based On Rk4 and Complementary Filtering (Cf)mentioning

confidence: 99%

“…Hardware configuration of a MEMS AHRS normally consists of inertial and magnetic sensors, or rather MEMS ICs of rate gyros, accelerometers, and magnetometers [13] of all three Cartesian axes, as well as a data acquisition and processing engine to provide solved attitude and heading solutions [14][15][16][17]. Rate gyros directly measure rotation rates, and by integrating those rates we can get rotation Euler angles.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of an AHRS Based on MEMS Sensors and Complementary Filtering

Wang

Ning

Chen

et al. 2014

Advances in Mechanical Engineering

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This paper presents design and implementation of an attitude and heading reference system (AHRS) based on low-cost MEMS sensors and complementary filtering (CF). Different from traditional solutions, information fusion is performed with Euler angles directly, which is more straightforward for understanding; however it proposes many challenges for reaching a stable and accurate estimation as when these angles approach or traverse their range boundaries, estimation may get discontinuous. Thus an effective discontinuity avoiding strategy is suggested in this paper to refine the estimation. Besides, instead of extended Kalman filtering (EKF), CF is utilized for state estimation of AHRS as it features fusion of high-frequency and low-frequency signals. In order to make up for shortcomings of MEMS sensors such as multiple errors, drifts, and bad accuracy, some effective calibration and filtering algorithms are proposed to guarantee agreeable AHRS performance. Also, architecture of the MEMS IMU (inertial measurement unit) and mathematical principles for AHRS solution are explained and implemented in this paper. Meanwhile, experimental comparisons have proved feasibility and acceptable performance of this AHRS design.

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