Attitude Heading Reference System based vehicle stationary state detection

Amin, Md. Syedul; Reaz, Mamun Bin Ibne; Nasir, Salwa Sheikh; Bhuiyan, Mohammad Arif Sobhan

doi:10.1109/iceeict.2014.6919141

Cited by 2 publications

(1 citation statement)

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“…Both velocity and angular velocity were suggested to be compared with a specified threshold, while the standard deviation (STD) was proposed to calculate the vehicle acceleration in a sliding window and was used to compare it with the threshold deduced from the stationary data [6,15]. An attitude heading reference system was put forward to detect the stationary states with acceleration data, which achieved an 87% correction rate in a vehicular test [17,18]. A neural network was also trained to use velocity and IMU measurements for stationary detection in GNSS outages [16,19].…”

Section: Introductionmentioning

confidence: 99%

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Li,

Nie,

Suvorkin

et al. 2024

Remote Sensing

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The inertial navigation system (INS) and global satellite navigation system (GNSS) are two of the most significant systems for land navigation applications. The inertial measurement unit (IMU) is a kind of INS sensor that measures three-dimensional acceleration and angular velocity measurements. IMUs based on micro-electromechanical systems (MEMSs) are widely employed in vehicular navigation thanks to their low cost and small size, but their magnitude and noisy biases make navigation errors diverge very fast without external constraint. The zero-velocity update (ZVU) function is one of the efficient functions that constrain the divergence of IMUs for a stopped vehicle, and the key of the ZVU is the correct stationary detection for the vehicle. When a land vehicle is stopped, the idling engine produces a very stable vibration, which allows us to perform frequency analysis and a comparison based on the fast Fourier transform (FFT) and IMU measurements. Hence, we propose a stationary detection method based on the FFT for a stopped land vehicle with an idling engine in this study. An urban vehicular navigation experiment was carried out with our GNSS/IMU integration platform. Three stops for 10 to 20 min were set to analyze, generate and evaluate the FFT-based stationary detection method. The FFT spectra showed clearly idling vibrational peaks during the three stop periods. Through the comparison of FFT spectral features with decelerating and accelerating periods, the amplitudes of vibrational peaks were put forward as the key factors of stationary detection. For the consecutive stationary detection in the GNSS/IMU integration process, a three-second sliding window with a one-second updating rate of the FFT was applied to check the amplitudes of peaks. For the assessment of the proposed stationary detection method, GNSS observations were removed to simulate outages during the three stop periods, and the proposed detection method was conducted together with the ZVU. The results showed that the proposed method achieved a 99.7% correct detection rate, and the divergence of the positioning error constrained via the ZVU was within 2 cm for the experimental stop periods, which indicates the effectiveness of the proposed method.

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