Abstract

MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.

Highlights

  • There has been a growing trend toward using cost-effective MEMS technology-based sensors for navigation purposes in aerospace systems, such as on light aircrafts and unmanned aerial vehicles (UAVs)

  • This paper proposed a novel concept of filtering inertial data with an enhanced capability of providing smooth data under harsh environments, eliminating low frequency vibration influences

  • It is advantageous to fuse data in such a way that very low-frequency content corresponding to the steady-state flight conditions is taken from the ACC’s measurements and higher-frequency content corresponding to changes of flight conditions is obtained from the angular rate sensors (ARSs)’s measurements

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Summary

Introduction

There has been a growing trend toward using cost-effective MEMS (micro-electro-mechanical system) technology-based sensors for navigation purposes in aerospace systems, such as on light aircrafts and unmanned aerial vehicles (UAVs). Unlike ACC, data are directly utilized for the attitude compensation under only steady-state conditions when the gravity distribution in the sensor’s framework can be estimated This corresponds to situations in which the aircraft performs a direct and unaccelerated flight. The proposed filtering algorithm is adaptive in the sense that the filtering bandwidth is modified based on the signal history This enables the usage of ACC-based attitude compensation, even under variable low-frequency vibration impacts, while common commercial AHRS systems fail to have the correct compensation capability. ARS data are filtered with a constant bandwidth, and when no low-frequency vibrations arise, all data are filtered with the same bandwidth of 5 Hz, which provides the same delay of data pre-processing for the majority of the flight This approach brings an added advantage to inertial data pre-processing and enhances the ACC-based attitude compensation possibilities.

Methodology
Principles of Sinusoidal Signal Estimation
Sum Squared Error Calculation to Estimate the Filtering Bandwidth
Filtering Algorithm
Performance of the Filtering Algorithm with Different Bandwidths
Demonstration of Data Smoothing
Application of the Multistage Filtering Algorithm on Simulated Data
Application of the Algorithm on Real Flight Data
Conclusions
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