加速度位移積分-展示頁
2024-08-20 04:08本頁面
【正文】 t velocity using some rule (trapezoidal, etc.)3. Remove the mean from the velocity4. Integrate again to get displacement.5. Remove the mean. Note, if you plot this, you will see drift over time.6.Double integration of raw acceleration data is a pretty poor estimate for displacement. The reason is that at each integration, you are積分操作主要有兩種方法：時域積分和頻域積分，積分中常見的問題就是會產(chǎn)生二次趨勢。訂閱分類：20130204 05:30:00|加速度積分位移 MatlabMATLAB應(yīng)用|舉報|字號最近做有關(guān)加速度的數(shù)據(jù)處理，需要把加速度積分成位移，網(wǎng)上找了找相關(guān)資料，發(fā)現(xiàn)做這個并不多，把最近做的總結(jié)一下吧！關(guān)于積分的方法，在國外一個論壇上有人提出了如下說法，供參考。pounding the noise in the data.If you are dead set on working in theTo eliminate (some to most) of the drift (trend), use a least squares fit (high degree depending on data) to determine polynomial coefficients.7. Remove the least squares polynomial function from your data.A much better way to get displacement from acceleration data is to work in the frequency domain. To do this, follow these steps...1. Remove the mean from the accel. data2. Take the Fourier transform (FFT) of the accel. data.3. Convert the transformed accel. data to displacement data by dividing each element by omega^2, where omega is the frequency band.4. Now take the inverse FFT to get back to the timedomain and scale your result.This will give you a much better estimate of displacement.說到底就是頻域積分要比時域積分效果更好，實(shí)際測試也發(fā)現(xiàn)如此。下面做一些測試，對一個正弦信號的二次微分做兩次積分，正弦頻率為50Hz，采樣頻率1000Hz，恢復(fù)效果如下時域
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