Frequency-scanning interferometry for dynamic measurement using adaptive Sage-Husa Kalman filter

Wang, Zian; Liu, Zhigang; Tian, Kai; Zhang, Huakun

doi:10.1016/j.optlaseng.2023.107545

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…The SHAKF consists of a mutually coupled Kalman filter and the Sage-Husa noise estimator. The Kalman filter is a linear minimum variance filter based on the original signal characteristics, and the Sage-Husa noise estimator is a sub-optimal unbiased extremely large a posteriori estimator that can estimate the noise statistics online [17].…”

Section: A the Sage-husa Adaptive Filter And Signal Fusionmentioning

confidence: 99%

Adaptive Kalman Filter Based on Online ARW Estimation for Compensating Low-Frequency Error of MHD ARS

Su,

Han,

et al. 2024

IEEE Trans. Instrum. Meas.

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Magnetohydrodynamic (MHD) ARS can precisely detect angular vibration information with a bandwidth of up to one kilohertz. However, due to secondary flow and viscous force, it experiences performance degradation when measuring lowfrequency angular vibrations. This paper presents an adaptive Kalman filter that uses online angular random walk (ARW) estimation to correct for the low-frequency error of MHD ARS, where a microelectromechanical system (MEMS) gyroscope is used to measure low-frequency vibrations. The proposed algorithm determines the signal frequency based on the ARW coefficients and adjusts the measurement noise covariance to achieve accurate fusion results. Thus, the method solves the problem of frequency-dependent variation of the amplitude response of the sensors in data fusion. Initially, the algorithm calculates ARW coefficient recursively utilizing the measurement signals of both sensors. Then, the operational frequencies of both sensors are determined by analyzing the correlation between the ARW coefficient and frequency. Subsequently, in the Sage-Husa adaptive Kalman filter, the Kalman gain matrix is adjusted by modifying the measurement noise variances of both sensor signals individually. Moreover, the stability of the proposed algorithm is achieved by introducing an adaptive matrix to constrain the measurement noise covariance estimation. In the experiment, the fusion effects of single-frequency and mixedfrequency signals are tested separately. The experimental results show that for frequency variation and frequency mixing, the proposed algorithm in this study significantly improves the fusion results.

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Section: A the Sage-husa Adaptive Filter And Signal Fusionmentioning

confidence: 99%

Adaptive Kalman Filter Based on Online ARW Estimation for Compensating Low-Frequency Error of MHD ARS

Su,

Han,

et al. 2024

IEEE Trans. Instrum. Meas.

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“…From equations ( 9) and (10), f(v k )achieves the maximum value when v k = µ = 0. This shows that the great likelihood estimation estimates the state from the perspective of the maximum probability of occurrence of the system quantity measurement, and the most important feature is that it considers not only the change of the innovation v k , but also the change of the innovation covariance matrix C v k .…”

Section: Maximum Likelihood Estimation Theorymentioning

confidence: 99%

“…The method can improve the estimation accuracy of the integrated GPS/SINS navigation system by estimating the statistical properties of the noise in real time. Wang improved the Sage-Husa adaptive Kalman filtering algorithm in the literature [10] to solve the problem of filter divergence caused by improperly selected initial values, but the noise-statistical information valuator is often in a critical steady state, which can easily cause the GPS/SINS integrated navigation system to SINS integrated navigation system's estimation results are scattered. Huang proposed an improved adaptive extended Kalman filtering algorithm in the literature [11] and applied it to wheeled electric vehicle speed estimation (PAUKF).…”

Section: Introductionmentioning

confidence: 99%

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Wang

Cheng

et al. 2023

Meas. Sci. Technol.

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Traditional adaptive Kalman filtering algorithms based on innovation are often used to solve the problem of reduced or even divergent filtering estimation accuracy under abnormal measurement noise. However, these algorithms are usually characterized by difficulties in selecting window width and window weight, which cannot simultaneously take into account the filtering tracking sensitivity and filtering accuracy.In this paper, an adaptive Kalman filtering algorithm based on maximum likelihood estimation is proposed, which determines the window size and window weight size under the kth moment by designing a window adaptive selection function and a weight function to change the innovation covariance at the kth moment, which in turn changes the measurement noise covariance at the kth moment, so that the measurement noise covariance is no longer a fixed single value, but can better adapt to the changes in the environment, reflecting good adaptive characteristics. The simulation results based on GPS/SINS integrated navigation system demonstrate that the new filtering algorithm of this paper reflects higher filtering accuracy and stronger robustness under the carrier in multiple motion states and accompanied by time-varying measurement noise interference. Compared with the traditional adaptive Kalman filtering algorithm based on innovation, the accuracy of attitude angle estimation error under this method is improved by 119.97%; the accuracy of velocity estimation error is improved by 264.42%; the accuracy of position estimation error is improved by 156.69%.

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“…The theoretical derivation of constructing STF is more complicated, and there are problems such as large computational volume for solving the asymptotic cancellation factor and arbitrary insertion position [ 14 ]. Reference [ 15 ] proposed a novel adaptive Sage-Husa, which solves divergence caused by wrong selection values and has strong robustness. Reference [ 16 ] studies a joint filtering algorithm based on multi-source sensors.…”

Section: Introductionmentioning

confidence: 99%

Adaptive UAV Navigation Method Based on AHRS

Lu,

Li,

Xiong

et al. 2024

Sensors

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To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy.

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Frequency-scanning interferometry for dynamic measurement using adaptive Sage-Husa Kalman filter

Cited by 8 publications

References 28 publications

Adaptive Kalman Filter Based on Online ARW Estimation for Compensating Low-Frequency Error of MHD ARS

Adaptive Kalman Filter Based on Online ARW Estimation for Compensating Low-Frequency Error of MHD ARS

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Adaptive UAV Navigation Method Based on AHRS

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