Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth

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.

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“…A variable bandwidth filtering (VBF) method was proposed to suppress the effects of vibration and sensor noise [ 39 ]. In this method, the sinusoidal estimation is used to continuously adjust the filtering bandwidth of accelerometer output data to restrain the influence of vibration and noise before attitude estimation is treated [ 39 ]. The flow chart of the VBF process is depicted in Figure 5 .…”

“…The proposed filtering process is adaptive because the bandwidth of the entire filtering process can vary with the frequency content of the signal, and it can be divided into two main stages [ 39 ]. In the first stage, a variable bandwidth Kaiser windowed filter with a coefficient of was used to filter the signal, while the second stage adopted a low pass filter (LP) with a variable decomposition, which is called an LP wavelet filter [ 39 ]. In addition, the coefficients of the Kaiser windowed LP filter can be calculated with a mathematical formula, which was discussed in reference [ 39 ].…”

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“…Vehicle vibrations and electromagnetic interference contribute to noise and other unwanted signal components in the inertial signal samples. Therefore, the selection of the appropriate signal filtering technique is necessary to employ a signal filter to minimize the noise and maximize the signal [6], [7]. Previous work attempted to address this problem by applying different signal filtering methods.…”

“…However, it is well known that the geospatial position estimates from low-cost GPS receivers are inaccurate mostly because of signal deterioration in urban environments and their low update rates [5]. The inertial sensors also suffer from bias instability [6]. Moreover, the non-uniform sample rate of the sensors results in additive signal noise and position alignment errors that reduce the signal-to-noise ratio (SNR) needed to detect and localize track irregularities reliably.…”