Noise Reduction Method for MEMS Gyroscope Based on Evolved Adaptive Kalman Filter

2015 ◽  
pp. 3-12
Author(s):  
Youcong Ni ◽  
Fengping Ang ◽  
Xin Du
Keyword(s):  
Kalman Filter ◽  
Noise Reduction ◽  
Reduction Method ◽  
Adaptive Kalman Filter ◽  
Mems Gyroscope

scholarly journals A Noise Reduction Method for MEMS Gyroscope Based on Direct Modeling and Kalman Filter

2018 ◽  
Vol 51 (31) ◽  
pp. 172-176 ◽  
Author(s):  
Shuo Cai ◽  
Yunfeng Hu ◽  
Haitao Ding ◽  
Hong Chen
Keyword(s):  
Kalman Filter ◽  
Noise Reduction ◽  
Reduction Method ◽  
Mems Gyroscope ◽  
Direct Modeling

The Study of the MEMS Gyro Zero Drift Signal Based on the Adaptive Kalman Filter

2012 ◽  
Vol 500 ◽  
pp. 635-639 ◽  
Author(s):  
Wei Cao Chen ◽  
Guo Wei Gao ◽  
Juan Wang ◽  
Li Li Liu ◽  
Xi Lin Li
Keyword(s):  
Kalman Filter ◽  
Random Noise ◽  
Zero Drift ◽  
Ar Model ◽  
Original Signal ◽  
Analysis Method ◽  
Adaptive Kalman Filter ◽  
Mems Gyroscope ◽  
Filtering Effect ◽  
Gyro Drift

The random noise is an important factor that affects the precision of the MEMS gyroscope. Based on the time-series analysis method, the AR model of the MEMS gyro drift signal is established. Then the adaptive Kalman filter is used to filter the drift signal. Comparison the original signal and the signal filtered by the adaptive Kalman filter, we found that the adaptive Kalman filter has the good filtering effect in the processing the zero drift signal of the MEMS gyro.

scholarly journals A reduction method for magnetic disturbance caused by DC railway system

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xuezhen Ding ◽  
Yuguo Li ◽  
Yunju Wu ◽  
Shuangmin Duan ◽  
Zhuoxuan Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Reduction Method ◽  
Filter Method ◽  
Magnetic Disturbance ◽  
Adaptive Kalman Filter ◽  
Railway Systems ◽  
Geomagnetic Data ◽  
Railway System ◽  
Kalman Filter Method ◽  
High Level

AbstractThe stray current of direct current (DC) railway systems causes magnetic disturbance in geomagnetic measurements, which may complicate the identification of useful information. The magnetic disturbance exhibits broadband characteristics in the frequency domain. In this paper, we propose a noise reduction method based on the adaptive Kalman filter to extract useful signals from the geomagnetic data with a high level of noise. The covariance matrixes of both the process noise (Q) and measurement noise (R) can be adaptively estimated to improve the performance of the adaptive Kalman filter. The proposed method is adopted to process the geomagnetic data collected at the Beijing Geomagnetic Observatory (BJI), which is affected by the DC railway system. The magnetic disturbance is largely reduced, and the signal-to-noise ratios of the horizontal and vertical components of the geomagnetic field are improved by more than 14 dB and 27 dB, respectively. The K-indices are calculated to evaluate the performance of the adaptive Kalman filter method. To assess the influence of the adaptive Kalman filter on natural signals, the geomagnetic data that contain rapid variations are processed. The denoising results show that the adaptive Kalman filter can effectively reduce the magnetic disturbance caused by DC railway system without large impact on the natural geomagnetic rapid variations.

scholarly journals Wireless Localization Method Based on AHP-WKNN and Amendatory AKF

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Su Pan ◽  
Sheng Hua ◽  
Duowei Pan ◽  
Xixia Sun
Keyword(s):  
Noise Reduction ◽  
Nearest Neighbor ◽  
Reduction Method ◽  
Indoor Localization ◽  
Signal Strength ◽  
Reference Points ◽  
Adaptive Kalman Filter ◽  
Wireless Localization ◽  
Analytic Hierarchy ◽  
Hierarchy Process

In this paper, we propose an AHP-WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K -nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP-WKNN method and the modified AKF can improve positioning accuracy effectively.

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