小波包单子带重构提取GPS序列特征信息

GPS观测环境愈来愈复杂，动态观测值包含的影响因素较多，函数关系复杂，影响特征信息的提取和参数模型的解释能力.小波包具有良好的时频分析能力,利用小波包理论对GPS数据序列进行分解与重构过程中有三个基本运算：与小波滤波器卷积、隔点采样、隔点插零,该三项运算产生频率交错和频率折叠等频率混淆现象.为消除频率混淆现象，分解与重构时，每作一次信号与小波卷积后，将其结果作一次快速傅立叶变换，频谱中多余的频率成分的谱值置零，再对置零后的频谱进行傅立叶逆变换，然后继续进行小波包的分解与重构，从而实现单子带重构提取GPS数据序列特征项.通过实例验证了小波包单子带重构提取GPS特征信息的有效性.

Feature extraction of GPS sequence based by single sub-band reconstruction of wavelet packet

Environments of GPS observation are becoming more complex. Dynamic measurements include more impact factors.Relations of functions are complicated. They influence the extraction of feature information and the ability of explanation of parameter models. Wavelet packet has an advanced time-frequency analysis capabilty. During the progress of decomposition and reconstruction of GPS data sequence, there are three basic operations, that are convolution, downsampling of keeping one sample out of two, and upsampling of putting one zero between each sample, and these operations produce frequency aliasing such as frequency folding and frequency interleaving .In order to eliminate the aliasing, at each step of decomposition or reconstruction, fast Fourier transform is performed the result obtained by convolving the input signal with filters, setting frequency spectrum of the redunctant frequency components to be zero, and inversely fast Fourier transforms the result after zeroing. The result after processing is the input signal of next step,and single sub-band reconstruction is implemented to extract the feature item of GPS data sequence. The availability of this algorithm is proved by a test example.