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首页> 外文期刊>Journal of Sensors >Delay-Free Tracking Differentiator Design Based on Variational Mode Decomposition: Application on MEMS Gyroscope Denoising
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Delay-Free Tracking Differentiator Design Based on Variational Mode Decomposition: Application on MEMS Gyroscope Denoising

机译:基于变分模式分解的延迟跟踪鉴别器设计:在MEMS陀螺仪去噪的应用

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This paper presents a delay-free tracking differentiator based on variational mode decomposition (VMD) for extracting the useful signal from a noisy measurement of gyroscope. Sigmoid function-based tracking differentiator (STD) is a novel tracking differentiator with the advantages of noise-attenuation ability and dynamical performance. However, there is a contradiction in STD; i.e., selecting a larger acceleration factor may cause faster convergence but bad random noise reduction whereas selecting a smaller acceleration factor may lead to signal delay but effective random noise reduction. Here, multiscale transformation is introduced to overcome the contradiction of STD. VMD is selected to decompose the noisy signal into multiscale components, and the correlation coefficients between each component and original signal are calculated, then the component with biggest correlation coefficient is reserved and other components are filtered by the proposed adaptive STD algorithm based on the correlation coefficient of each component, and finally the denoising result is obtained after reconstruction. The prominent advantages of the proposed algorithm are as follows: (i) compared to traditional tracking differentiators, better noise suppression ability can be achieved with suppression of time delay; (ii) compared to other widely used denoising methods, a simpler structure but better denoising ability can be obtained.
机译:本文介绍了基于变分模式分解(VMD)的无延迟跟踪区分器,用于从陀螺仪的噪声测量中提取有用信号。基于SIGMOID函数的跟踪微分器(STD)是一种新型跟踪区分器,具有噪声衰减能力和动力性能的优点。但是,STD存在矛盾;即,选择较大的加速度因数可能导致更快的收敛性但随机降噪不良,而选择较小的加速度因子可能导致信号延迟但有效的随机降噪。在这里,介绍了多尺度转换以克服STD的矛盾。选择VMD以将噪声信号分解为多尺度组件,并且计算每个组件和原始信号之间的相关系数,然后保留具有最大相关系数的组件,并且基于相关系数通过所提出的自适应STD算法滤除其他组件每个组分,最后在重建后获得去噪结果。该算法的突出优点如下:(i)与传统的跟踪差差相比,可以通过抑制时间延迟来实现更好的噪声抑制能力; (ii)与其他广泛使用的去噪方法相比,可以获得更简单但更好的去噪能力。

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