基于ICSO的DGPS整周模糊度的求解方法

针对差分全球定位系统(DGPS)模糊度解算过程中效率低,搜索慢的问题,对鸡群优化算法(CSO)进行适应性改进,并将改进后的鸡群优化算法(ICSO)应用到整周模糊度的快速解算中,利用卡尔曼滤波求出双差模糊度的浮点解和协方差矩阵,采用Lenstra-Lenstra-Lovasz (LLL)降相关算法对模糊度的浮点解和方差协方差矩阵进行降相关处理,以降低模糊度各分量之间的相关性,在基线长度固定的情况下,利用ICSO搜索整周模糊度的最优解. 采用经典算例进行仿真,仿真结果表明,与已有文献相比在整周模糊度的解算过程中改进的鸡群优化算法能有效提高搜索速度和求解成功率.      

一种用于GPS整周模糊度OTF求解的整数白化滤波改进算法

提出一种用于整周模糊度OTF求解的整数白化滤波改进算法。该算法首先对整周模糊度的协方差矩阵进行整数白化滤波处理 ,以降低整周模糊度间的相关性 ,然后构造搜索空间来判定是否需要进行搜索。如果需要 ,则通过搜索来确定变换后的整周模糊度 ;如果不需要 ,则通过直接取整来确定整周模糊度 ,进而得到原始的整周模糊度和基线分量的固定解。初步试验结果显示 ,采用改进方法解算整周模糊度可以提高成功率和解算效率     

一种单频单历元BDS／GPS组合整周模糊度解算方法

针对单频单历元组合载波相位差分技术(RTK)定位过程中存在的秩亏及模糊度解算病态等问题,提出了一种模糊度降相关的新方法。该方法引入伪距观测值进行辅助解算。首先采用经验分权法对伪距与载波相位观测值分配权重,并通过加权最小二乘法获得整周模糊度浮点解及协方差。然后通过对整周模糊度浮点解的方差-协方差矩阵进行降序排列和剔除病态模糊度。最后利用修正后的浮点解迭代搜索模糊度的整数解。试验结果表明而且可以起到良好的模糊度降相关的效果定位。      

基于LAMBDA和DC算法的GPS单历元整周模糊度的快速确定

仅利用LAMBDA方法求解GPS单历元整周模糊度成功率不高,并且当接收卫星数较多时搜索空间较大。为此,采用TIKHONOV正则化方法削弱单历元模型法方程的病态性,并且基于协方差矩阵选择部分宽巷模糊度,先采用LAMBDA方法进行搜索,再利用高解算效率的DC算法解算剩余宽巷模糊度,最后通过两组不同线性组合的逆变换直接求取原始观测值L1和L2的整周模糊度。实验和计算表明,方法显著提高整周模糊度的搜索效率,并且提高模糊度搜索成功率。     

GPS/BDS组合定位对短基线模糊度搜索空间的影响

整周模糊度固定是GNSS高精度动态定位的关键问题,为研究GPS/BDS(北斗卫星导航系统)组合定位对短基线模糊度搜索效率和固定成功率的提高效果,利用基于模糊度方差-协方差阵特征值平方根的模糊度搜索空间对比方法分别对短基线条件下GPS单系统、BDS单系统和GPS/BDS组合系统下的模糊度搜索空间进行对比分析。实验数据表明:GPS/BDS组合定位对各单系统的方差-协方差阵均有影响,从整体上可以缩小GPS和BDS单系统的模糊度搜索空间。最后对模糊度固定成功率进行了统计,结果表明,在单、双频条件下组合系统均可显著提高模糊度固定成功率和搜索效率。     

误差椭圆搜索法EES固定GNSS整周模糊度

LAMBDA方法和改进的LAMBDA方法都使用整数矩阵进行降相关,使得变换后的模糊度方差阵更加对角化,但有时变换后的方差阵的对角线元素的数值量级相差很大,使得搜索空间有些扁长,为了避免这些情况的发生,本文提出一种更加理想的新的EES(error ellipse search,误差椭圆搜索)方法来进行整周模糊度的固定。由于实数矩阵可以使降相关达到各种理想的状态,在降相关方面比整数矩阵更有优势,因此EES方法利用实数矩阵进行降相关,搜索每两个模糊度之间的最佳误差椭圆,使得变换后的方差阵对角线元素趋于同一量级,搜索空间更加接近于球形。通过实验表明,随着基线长度的增加,EES方法固定模糊度的成功率远远高于LAMBDA方法,缩短了固定整周模糊度需要的历元数,是一种切实可行有效的固定整周模糊度的方法。     

GPS模糊度降相关LLL算法的一种改进

针对LLL(Lenstra,Lenstra,Lovasz algorithm)算法的不足,提出了具有自适应性的整数正交变换算法,并采用此算法和升序排序调整矩阵对LLL算法进行了改进。通过LLL算法和改进的LLL算法对随机模拟的600个对称正定矩阵的模糊度方差-协方差阵和30组实测数据进行处理分析,发现改进的LLL算法能够更有效地降低协方差阵的条件数,减小备选模糊度组合数,更有利于整周模糊度的搜索和解算。     

顾及基线先验信息的GPS模糊度快速解算

采用GPS相位观测值进行快速定位时,其解算模型严重病态,最小二乘解得的浮点模糊度精度差且相关性大,导致整周模糊度搜索空间过大,难以正确固定。本文提出一种顾及基线先验信息和模糊度线性约束的整数条件的GPS模糊度快速解算方法,先用顾及基线先验信息的正则化算法解得精度较高且相关性较小的浮点模糊度,以减小整周模糊度的搜索空间;再综合利用整周模糊度间的线性约束的整数条件和基线先验信息,进一步有效地减小模糊度搜索空间,提高搜索效率。算例表明:顾及基线先验信息的正则化算法有效地改善了模糊度浮点解,模糊度线性约束的整数条件有效地提高搜索效率和成功率。     

GPS观测量先验方差-协方差矩阵实时估计

GPS观测量的先验方差－协方差矩阵的可靠性直接关系到GPS定位结果和可靠性，关系到模糊度初始化时间、模糊度搜索的可靠性及成功率。本文提出了一种GPS观测量的先验方差－协方差矩阵的实时估计方法。其特点是直接利用伪距和载波相位观测值，来实时估计先验方差－协方差矩阵，而且可广泛应用于各种测量型接收机的各种测量模式。该方法应用于模糊度解算中，并与其他方法进行比较，以检验其效果。     

一种GPS整周模糊度单历元解算方法

仅利用单历元的载波相位观测值进行整周模糊度解算,观测方程秩亏,给单历元模糊度解算带来很大困难.因此,本文提出一种单历元确定GPS整周模糊度的方法.利用单历元测码伪距观测值和双频载波相位观测值组成双差观测方程,根据方差矩阵对宽巷模糊度进行分组,采用基于LABMDA方法的逐步解算方法来确定双差相位观测值的宽巷模糊度.确定宽...     

Orthogonality defect and reduced search-space size for solving integer least-squares problems

In the context of ambiguity resolution (AR) of global navigation satellite systems (GNSS), decorrelation among entries of an ambiguity vector, integer ambiguity search, and ambiguity validations are three standard procedures for solving integer least-squares problems. This paper contributes to AR issues from three aspects. Firstly, the orthogonality defect is introduced as a new measure of the performance of ambiguity decorrelation methods and compared with the decorrelation number and with the condition number, which are currently used as the judging criterion to measure the correlation of ambiguity variance–covariance matrix. Numerically, the orthogonality defect demonstrates slightly better performance as a measure of the correlation between decorrelation impact and computational efficiency than the condition number measure. Secondly, the paper examines the relationship of the decorrelation number, the condition number, the orthogonality defect, and the size of the ambiguity search space with the ambiguity search candidates and search nodes. The size of the ambiguity search space can be properly estimated if the ambiguity matrix is decorrelated well, which is shown to be a significant parameter in the ambiguity search progress. Thirdly, a new ambiguity resolution scheme is proposed to improve ambiguity search efficiency through the control of the size of the ambiguity search space. The new AR scheme combines the LAMBDA search and validation procedures together, which results in a much smaller size of the search space and higher computational efficiency while retaining the same AR validation outcomes. In fact, the new scheme can deal with the case there are only one candidate, while the existing search methods require at least two candidates. If there are more than one candidate, the new scheme turns to the usual ratio-test procedure. Experimental results indicate that this combined method can indeed improve ambiguity search efficiency for both the single constellation and dual constellations, respectively, showing the potential for processing high-dimension integer parameters in multi-GNSS environment.     

降相关对模糊度解算中搜索效率的影响分析

首先理论分析了条件数、正交缺陷度、S(A)等降相关评价指标所表示的几何意义,然后采用LAMBDA算法、LLL规约算法和Seysen规约算法通过模拟和实际数据对模糊度的搜索效果和不同评价指标之间的关系进行了深入计算分析。进一步验证得出"降低模糊度方差分量间的相关性实现最大程度地压缩椭球可以提高搜索效率"的观点是片面的,并通过结果分析表明提高搜索效率的本质在于尽可能地促使基向量按照一定方向排序。     

GPS随机模型最优不变二次无偏估计算法及实现

目前GPS基线解算通常采用简化的等权随机模型,在复杂观测环境下采用该模型将影响定位结果的精度和可靠性。本文结合方差-协方差分量估计理论,论述了利用最优不变二次无偏估计(BIQUE)方法建立GPS精密随机模型的迭代算法,并进行了相应的软件模块设计。算例分析结果表明,该算法用于GPS随机模型精化是可行的,与等权随机模型比较,所建立的GPS双差观测值方差-协方差阵更符合实际,能有效地提高整周模糊度的可靠性和定位精度。     

一种快速解算高维模糊度的LLL分块处理算法

由于多频多模GNSS观测数据解算的模糊度具有较高的维数和精度,当采用常规的LLL算法进行模糊度整数估计时,规约耗时显著大于搜索耗时,成为限制高维模糊度解算计算效率的主要因素。针对这一问题,通过分析规约耗时与模糊度维数和精度之间的关系,提出了一种LLL分块处理算法。该算法通过对模糊度方差协方差阵进行分块处理,降低单个规约矩阵的维数,以减少规约耗时,从而提高模糊度解算计算效率。通过两组实测高维模糊度数据对本文提出的分块处理算法进行了效果验证。结果显示,当分块选择合理时,本文提出的算法相对于LLL算法的解算效率分别可提高65.2%和60.2%。     

The least-squares ambiguity decorrelation adjustment: its performance on short GPS baselines and short observation spans

The least-squares ambiguity decorrelation adjustment is a method for fast GPS double-difference (DD) integer ambiguity estimation. The performance of the method will be discussed, and although it is stressed that the method is generally applicable, attention is restricted to short-baseline applications in the present contribution. With reference to the size and shape of the ambiguity search space, the volume of the search space will be introduced as a measure for the number of candidate grid points, and the signature of the spectrum of conditional variances will be used to identify the difficulty one has in computing the integer DD ambiguities. It is shown that the search for the integer least-squares ambiguities performs poorly when it takes place in the space of original DD ambiguities. This poor performance is explained by means of the discontinuity in the spectrum of conditional variances. It is shown that through a decorrelation of the ambiguities, transformed ambiguities are obtained which generally have a flat and lower spectrum, thereby enabling a fast and efficient search. It is also shown how the high precision and low correlation of the transformed ambiguities can be used to scale the search space so as to avoid an abundance of unnecessary candidate grid points. Numerical results are presented on the spectra of conditional variances and on the statistics of both the original and transformed ambiguities. Apart from presenting numerical results which can typically be achieved, the contribution also emphasizes and explains the impact on the method's performance of different measurement scenarios, such as satellite redundancy, single vs dual-frequency data, the inclusion of code data and the length of the observation time span. Received: 31 October 1995 / Accepted: 21 March 1997     

Zero-correlation transformation and threshold for efficient GNSS carrier phase ambiguity resolution

The key point of accurate and precise applications of Global Navigation Satellite Systems lies in knowing how to efficiently obtain correct integer ambiguity. One of the methods in solving the ambiguity resolution problem is applying the ambiguity searching technique coupled with an ambiguity decorrelation technique. Traditionally, an integer-valued limitation of the transformation matrix ensures that the integer characteristic of candidates exists after the inverse transformation, but this also makes the decorrelation imperfect. In this research, the float transformation matrix will be considered. To ensure both the integer characteristic and perfect decorrelation can be reached, the float transformation is used indirectly. To solve the ambiguity resolution problem, the problem is transformed by integer and float transformation matrices. The objective of integer transformation is reducing the number of candidates. The target of float transformation is validating these reduced candidates. A zero correlation domain or a near complete diagonalization covariance matrix can be obtained via the float transformation. A space in this domain will be used as the threshold; hence the zero correlation domain is called the threshold domain. The number of ambiguity candidates based on integer transformation can be reduced once again through the proposed method. The experiments in this paper prove that the method can make the ambiguity resolution become more efficient without any drop in the accuracy.