Adaptive Kalman Filter Enhanced With Spectrum Analysis for Wide-Bandwidth Angular Velocity Estimation Fusion

Ji, Yue; Du, Yajie; Yan, Guozhong; Li, Xingfei; Wu, Jiangqin; Tuo, Weixiao; Li, Jinyi

doi:10.1109/jsen.2020.2997780

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Figure 3 provides the relative error comparisons of various SNR estimation schemes, including spectrum analysis (SA) estimation [25], maximum likelihood (ML) estimation [26] and the CNN-LSTM scheme considered in this paper. From Fig.…”

Section: Snr Evaluationmentioning

confidence: 99%

Spectrum sensing based on adaptive sampling of received signal

Miao

Tan

Zhang

et al. 2021

J Wireless Com Network

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Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

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Section: Snr Evaluationmentioning

confidence: 99%

Spectrum sensing based on adaptive sampling of received signal

Miao

Tan

Zhang

et al. 2021

J Wireless Com Network

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“…Iwata T. et al [14] presented an improved Wiener filter to fuse MHD ARS and a low frequency measurement system to reduce the measurement error and noise. Ji Y. et al [15] presented an adaptive Kalman filter to reduce the low frequency bandwidth error and noise of MHD ARS. These methods require accurate mathematical models and noise characteristics.…”

Section: Introductionmentioning

confidence: 99%

Research on Magnetohydrodynamic Angular Rate Sensor Denoising for a Space Laser Stabilization Control System

Han

et al. 2023

Applied Sciences

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The magnetohydrodynamic angular rate sensor (MHD ARS) is a high-bandwidth, high-accuracy sensor that is increasingly used to measure spacecraft harmonic vibration. However, the amplitude of harmonic vibration is usually on the order of microradian to milliradian, and the induced electric potential signal of MHD ARS is only on the order of nanovolt to microvolt, which is easily disturbed by noise. In this paper, an improved method based on autocorrelation with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet Threshold Denoising (WTD) is proposed to denoise the signal of MHD ARS. Firstly, CEEMDAN is used to decompose noisy signals and obtain intrinsic mode functions (IMFs), and autocorrelation is used to determine the relevant modes where the effective signals are located. Then, the improved threshold and thresholding function are used to denoise the relevant modes. Finally, the denoised signal is obtained by combining the denoised relevant modes. In the experiment, noisy MHD ARS signals were recorded in static and dynamic conditions, and the effects of the proposed method and conventional methods were compared. The results of the Allan variance in the static condition and root-mean-square error in the dynamic condition show that the proposed method can effectively overcome the shortcomings of conventional methods and obtain a better denoising effect.

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