MRSVD敏感分量在单频周跳探测修复中的应用

准确探测微小周跳历元是周跳修复的关键，精准修复周跳是高质量北斗卫星导航系统（BeiDou Navigation Satellite System，BDS）定位的难点，为此提出一种基于多分辨奇异值分解（Multi-Resolution Singular Value decomposition，MRSVD）的敏感分量预测模型，用于周跳探测与修复。该方法对探测信号构造Hankle矩阵，并对其进行MRSVD分析，得到一组具有不同分辨率的分量信号。然后对分量信号进行敏感因子评估分析，选取包含周跳特征的分量，构成敏感特征向量，凸显信号的周跳信息，进而通过模极大值谱精准检测出信号中奇异点的位置，实现对微小周跳的准确探测。以筛选出的MRSVD敏感特征向量为基础，构建最小二乘支持向量机（Least Square Support Vector Machine，LS-SVM）预测模型，进行周跳修复。实验结果表明，该方法能够对载波相位中出现的微小周跳进行准确探测与修复，证明了该方法的可行性和有效性。

Study on cycle-slip detection and repair algorithm in MRSVD method

It is critical for repairing cycle slips by accurately detecting epochs of small cycle slips, while it is difficult for high accuracy Chinese BeiDou Navigation Satellite System（BDS） to precisely repair cycle slips. Thus, a sensitive component prediction model based on Multi Resolution Singular Value Decomposition（MRSVD） is proposed to detect and repair cycle slips. In using this method, Hankle matrices are constructed for detected signals, which are analyzed via MRSVD to obtain a group of component signals. Subsequently, the component signals are evaluated and analyzed from the perspective of sensitive factors to select components with characteristics of cycle slips and thereby compose sensitive feature vectors to highlight information about cycle slips of signals, in order to precisely detect singular points of signals through maximum modulus and accurately detect small cycle slips. At last, sensitive feature vectors are used as training samples to construct a predictive LS-SVM（Least Square Support Vector Machine） model for repairing cycle slips. Experimental results suggest that the method proposed in this paper can accurately detect and repair small cycle slips appearing in carrier phases, which prove that this method is feasible and effective.