针对外测数据误差分离领域中测量数据趋势项提取模型构建复杂的问题,提出一种利用小波多尺度分解提取测量数据趋势项,然后将测量信号与趋势项相减,把得到的残差作为随机误差的误差分离方法。小波的分解层数直接影响误差分离的效果,恰当的选择小波分解层数是该方法的关键,针对随机误差为低阶AR模型的雷测数据,给出了最佳层数的确定方法。从随机误差分离过程可以看出,该方法比较简单,实用性好,仿真结果表明该方法能够有效的对外测数据的随机误差进行分离。

In the field of external trajectory measurement error separation, aiming at building up the model for extracting trend part from measurement data, the method of error separating based on multi-scale decomposition of wavelet transform is proposed. At first the trend part is extracted with multi-scale decomposition of wavelet transform, and then subtracts the measurement value with it, in order to get the difference as random error. Determining the decomposing level of wavelet transform is the crucial problem in this method because the decomposing level has a direct influence on the result of error separating. The calculation of optimal levels of wavelet transforms is given, and then the optimal levels for the radar-measured data of low degree AR model is workout with it. The method is simple to implement, and high in performance, according to the simulation result, the random error of range measuring data is separated effectively.