Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion

TL;DR

This paper characterizes noise in an IMU sensor, analyzes its components, and demonstrates improved attitude measurement performance through filtering and sensor fusion techniques, especially using a Kalman filter.

Contribution

It provides a detailed noise analysis of the MPU-6050 IMU and shows how Kalman filtering effectively reduces noise for better attitude sensing.

Findings

High-frequency noise partially filtered by smoothing filters

Noise identified as white Gaussian and effectively removed by Kalman filter

Performance degradation over time due to error accumulation

Abstract

We describe the characterization and removal of noise present in the Inertial Measurement Unit (IMU) MPU-6050. This IMU was initially used in an attitude sensor (AS) developed in-house, and subsequently implemented in a pointing and stabilization platform developed for small balloon-borne astronomical payloads. We found that the performance of the IMU degrades with time due to the accumulation of different errors. Using the Allan variance analysis method, we identified the different components of noise present in the IMU and verified the results using a power spectral density analysis (PSD). We tried to remove the high-frequency noise using smoothing filters, such as moving average filter and Savitzky-Golay filter. Although we did manage to filter some of the high-frequency noise, the performance of these filters was not satisfactory for our application. We found the distribution of the random noise present in the IMU using a probability density analysis, and identified the noise to be white Gaussian in nature which we successfully removed by a Kalman filter in real time.