动态测量数据的高保真容错Q-滤波算法

针对航天器遥测和跟踪数据非平稳、噪声干扰、数据散乱、野值多、真实信息往往被随机误差淹没等问题，提出一组新型滑动容错滤波算法，称Q-滤波容错算法。 采用原始数据滑动四分位滤波和滤波残差四分位过滤补偿相结合的方式，实现各种带野值、非平稳、强噪声干扰和超低信噪比的采样数据序列剔除野值、削弱扰动和提取信号。理论分析、仿真计算和飞行数据应用实例证实了该Q-滤波算法有强容错能力和提取采样数据本真变化的能力，可以从被噪声淹没和低信噪比的非平稳采样数据序列中或者在采样数据变化形态杂乱的情况下，高保真地准确提取测量对象的真实变化信息。

High Verisimilitude Outlier Tolerant Q Filtering Algorithm for Dynamic Measurement Data

In order to distill veritable signals hided in non-stationary spacecraft telemetry/tracking data series contaminated with noise, disorderly impurity, patchy outliers and stochastic errors, a series of new filtering algorithms composed of the outlier-tolerance quartile filters of dynamic data series situated in a series of moving windows and compensators of filtering residuals are proposed in this paper. This filtering algorithm is called the Q-filter. The Q-filter can be used to reject outliers, to weaken disturbance and to distill realistic signals from all kinds of complicated dynamic data series, even if there are patchy outliers, non-stationary components, strong disturbances as well as impurities in the dynamic data series. By use of theoretical analysis and simulation results as well as application examples in space-flight engineering, it is shown that the Q-filter algorithm is strong outlier-tolerant and high veritable one and that the Q-filter can be used to deal with all kinds of impure non-stationary data series which may be submerged in strong disturbance or low SNRs, or be metamorphosed with patchy outliers.