Abstract:

Aiming at the nonlinear characteristics of passive observation platforms’ measurements and serious impact on the filter estimate accuracy caused by the random noise, a measurement data pre-processing algorithm is proposed. First of all, the confidence level of the measured data is used to detect the measurements in sliding window type and the invalid data are discriminated initially, then the support vector machine (SVM) is used whose kernel function has been weighted with data’s characteristic to reclassify the measurements, in order to determine the invalid data ultimately. Finally a test based on the actual measurements is given， whose result shows that the proposed algorithm can effectively eliminate the impact of various types of noise on the measurement data, and the proposed algorithm has some engineering application value.