Matlab滤波加速度计数据中的电流尖峰 [英] Matlab filter electical spikes in accelerometric data

问题描述

我有一个加速度计数据的数据集受到电流尖峰的影响。

我正在寻找一个很好的方法来滤除或减少这些尖峰，因为需要在这些数据上计算一个FFT窗口和其他统计指标，例如峰度和偏度。我不能简单地删除这些异常值或用NaN替换它们。
Sampling 2000 [hz]

直到现在我已经试过了MATLAB 2012b：

• 小波消噪（Haar小波）
  
• 中值过滤器
  
• 消除和消除方法
  
你可以建议一个合适的方法来处理这些数据吗？



下载示例数据集


解决方案

我会建议一些本地平滑。通过定义阈值并平均所有低于和高于其的值。
％阈值
Tl = -0.6;
To = 0.6;

peaks = find（Af< T1 | Af> To）;
Af（峰值）=（Af（峰值-1）+Δf（峰值+1））/ 2;


这种方法的问题在于您的大纲有时最多包含6个样本。
所以你需要使用while循环在多个步骤中平滑：



  Af = data.example1; 
％阈值
 Tl = -0.6; 
 To = 0.6; 
 
初始化
 peaks = find（Af  counter = 0; 
 
 while〜isempty（peaks）
 peaks = find（Af  To）; 
 Af（峰值）=（Af（峰值-1）+Δf（峰值+1））/ 2; 
 counter = counter + 1; 
 end 
  

经过6次迭代，您将得到以下结果：





I have a dataset of accelerometric data that is affected by electical spikes.

I'm looking for a good method to filter out or reduce these spikes as need to calculate on these data a rolling window of FFT and other statistical indicators such as kurtosis and skewness. I can't simply delete these outliers or replace them with NaN. Sampling 2000[hz]

Until now I've tried on MATLAB 2012b:

• Wavelet denoising (Haar wavelet)
• Median Filter
• Despike and iterpolate approach

Can you suggest a proper approach to deal with these data?

Download example dataset

解决方案

I would suggest some local smoothing. By defining thresholds and averaging all values below and above.

Af = data.example1;
% Thresholds
Tl = -0.6;
To = 0.6;

peaks = find( Af < Tl | Af > To);
Af(peaks) = ( Af(peaks-1) + Af(peaks+1) ) / 2;

The problem with this approach is that your outliners sometimes consist of up to 6 samples. So you need to smooth in multiple steps using a while loop:

Af = data.example1;
% Thresholds
Tl = -0.6;
To = 0.6;

% initialisation
peaks = find( Af < Tl | Af > To);
counter = 0;

while ~isempty(peaks)
    peaks = find( Af < Tl | Af > To);
    Af(peaks) = ( Af(peaks-1) + Af(peaks+1) ) / 2;
    counter=counter+1;
end

after 6 iterations you get the following result:

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