Wavelet Basis Selection for Denoising Experimental Data of Steering Wheel Angle
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摘要: 针对在采用小波去噪方法对含有噪声的汽车方向盘转角试验数据进行去噪时小波基的选择问题,探索研究了一种适用于汽车方向盘试验数据去噪的小波基选择方法。首先,结合小波基参数特性以及汽车方向盘转角试验数据对处理效果的要求,归纳出适用于汽车方向盘转角试验数据处理的小波基特点,得出Daubechies(dbN)小波基和Symlet(symN)小波基适用于汽车方向盘转角试验数据处理的结论;然后,引入重构因子来评价各阶数下小波基的处理效果,从而确定小波基的阶数,并以某车型进行双移线试验时采集的方向盘转角试验数据为例,计算并比较了db2～db20和sym2～sym8共26个小波基的重构因子大小,得出db5、db6、sym4和sym5小波基较适用于该试验数据的结论;最后,对所选择的小波基进行了处理效果的验证。结果表明:用该方法选出的小波基对该试验数据有较好的处理效果。Abstract: Aiming at the problem that how to select the wavelet basis used in denoising the steering wheel angle ex-perimental data, a selection method of wavelet basis is researched.First of all, the features of wavelet basis which is suitable for the steering wheel angle experimental data processing are summarized based on the parameter charac-teristics of wavelet basis and the requirement of processing effects of steering wheel angle experimental data.Then Daubechies (dbN) and Symlet (symN) are selected as the suitable types of wavelet basis.Second, the reconstruc-tion factor is introduced to judge the processing effect of each wavelet basis so that the proper order numbers of wavelet basis are precisely selected.After that, taking a group of steering wheel angle experimental data that col-lected from a vehicle double lane change running test as example, the wavelet bases db5, db6, sym4 and sym5 are selected as the suitable wavelet bases for this test data through calculating and comparing the reconstruction factor of all wavelet bases of dbN and symN.Finally their processing effects are also verified by using the selected wavelet bases to denoise steering wheel angle experimental data.
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Key words:
- wavelet /
- data processing /
- parameter characteristics /
- reconstruction /
- effects
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