GNSS模糊度降相关性能的条件方差平稳度评价法

GNSS模糊度降相关通过整数变换优化条件方差的排列顺序,提高搜索效率。降相关和条件方差的关系及其评价是关键问题之一。针对这一问题,本文从理论上分析了排序后模糊度降相关与条件方差之间的数值关系,发现降相关性能与条件方差数值序列的平稳性有关,降相关性能越强,条件方差数值序列越平稳。基于这一理论关系,给出了"条件方差平稳度"定义,并将其作为评价降相关性能的指标。通过模拟和实测数据验证,并采用条件方差变化趋势图和搜索时间来定性和定量评价降相关性能,用以判定条件方差平稳度的合理性。试验结果表明,条件方差平稳度可以较精确直观地衡量模糊度的降相关性能。本文定义的指标揭示了模糊度降相关的本质。

Conditional variance stationarity evaluation method for GNSS ambiguity decorrelation

GNSS ambiguity decorrelation is to optimize the permutation order of conditional variance by integer transformation, so as to improve the search efficiency. One of the key problems is how to evaluate the relationship between decorrelation and conditional variance. Aiming at this problem, this paper theoretically analyzes the numerical relationship between decorrelation and conditional variance after sorting. It is found that the decorrelation performance is related to the stationarity of the conditional variance sequence. The stronger the decorrelation performance, the more stable the conditional variance sequence. So based on this theoretical basis, the conditional variance stationarity is proposed as an index to evaluate the performance of decorrelation. The results are verified by both simulation and actual test experiments, and the conditional variance trend graph as well as search time are also used to qualitatively and quantitatively evaluate the performance of decorrelation, to determine the rationality of the conditional variance stationarity. The experimental results show that the conditional variance stationarity proposed in this paper can more accurately and intuitively measure the performance of ambiguity decorrelation. The index defined in this paper reveal the essence of GNSS ambiguity decorrelation.