Vehicle Vibration Error Compensation on IMU-accelerometer Sensor Using Adaptive Filter and Low-pass Filter Approaches

Abstract

In vehicle dead reckoning or vehicle positioning systems, an inertial measurement unit (IMU) sensor has an important role to provide acceleration and orientation of the vehicle. The acceleration from the IMU accelerometer is used to calculate the velocity of the vehicle, and then it estimates the vehicle's distance traveled to time. However, the accelerometer suffers from external noises such as vehicle vibrations (generated from the engine, alternator, compressor, etc) and road noises. This paper delivers deep analysis and focuses on how to handle the error from vehicle vibrations. A filter method is proposed by using a combination of adaptive least mean squares (LMS) and low-pass finite impulse response (FIR) filters. The adaptive LMS filter is used to cancel the vehicle vibration error frequencies and adapts those frequency changes in several engine rotation conditions. It is then finalized with the low-pass FIR filter which is used to filter high-frequency vibration noises. Several experiments were made and the results show that the proposed filtering method is able to give better signal to noise ratio (SNR dB) and noise attenuation ratio (ATT dB) in comparison with regular low-pass FIR filter and independent adaptive LMS filter in a particular condition.

CYCLOPS: A cyclists’ orientation data acquisition system using RGB camera and inertial measurement units (IMU)

“…The above implies that acquiring inertial measurement data on irregular terrain would require a detailed analysis of the vibration environment to be carried out beforehand and systems to be designed with IMUs that can withstand the expected conditions, as well as applying advanced filtering techniques to isolate the signals of interest from unwanted vibrations. In this latter aspect, in [47] , the authors use a combination of adaptive least mean squares (LMS) filters and low-pass finite impulse response (FIR) filters to deal with the vibrations that affect the signals from a BNO055 IMU that has been installed on the dashboard of a vehicle. The experiments presented by the authors demonstrate that the proposed filter combination offers a better signal-to-noise ratio (SNR) and noise attenuation ratio (ATT) compared to filtering systems that include MEMS IMUs.…”

On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

“…For Kalman filtering, different measurement noise covariance and dynamic noise covariance matrixes were tuned to eliminate the high-frequency noise [21]. For low-pass filtering, the band-pass frequency and sampling rate were tuned to eliminate the high-frequency noise [22].…”

A New Technique on Vibration Optimization of Industrial Inclinometer for MEMS Accelerometer Without Sensor Fusion

“…In reality, the vehicle and machine operation, especially in heavy industry, produce vibration noise, which affects the acceleration measurement data of the Inertial Measurement Unit (IMU) sensor [12]- [14]. The vibration [15] causes considerable variations in the inclinational data that mislead the whole system's performance.…”