GNSS模糊度降相关算法及其评价指标研究

针对Gauss、LDL和LLL算法构造整数阵存在的实数阵元素计算、实数至整数阵转换的排序问题,分别研究了相应的元素升序降相关算法和整逆型(先求逆后取整)降相关算法。分析了谱条件数、降相关系数和平均相关系数等降相关算法评价指标的优缺点,提出了等效相关系数评价指标。研究结果表明,等效相关系数较其他3种指标能更有效地评价不同维数方差阵,尤其是高维情况的降相关算法效果;逆整型优于整逆型降相关算法,升序(逆整型)降相关算法更佳,且优劣顺序为升序LDL、升序Gauss和升序LLL算法。     

单频GPS动态定位中整周模糊度的一种快速解算方法

针对单频GPS动态定位中常用模糊度求解方法存在的问题,提出一种整周模糊度快速解算方法。首先通过对双差观测方程中坐标参数的系数阵进行QR分解变换以消除坐标参数,从而仅对模糊度参数建立Kalman滤波方程进行估计,然后利用排序和双Cholesky分解对滤波得到的模糊度进行降相关处理,并结合收缩模糊度搜索空间的思想来搜索固定整周模糊度。以实测的动态数据为例对该方法进行测试。分析结果表明,该方法不但可以改善模糊度浮点解精度,而且具有良好的模糊度降相关效果,可正确有效地实现整周模糊度的快速解算。     

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.      

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.     

下三角Cholesky分解的整数高斯变换算法

针对全球导航卫星系统(GNSS)载波相位测量中,基于整数最小二乘估计准则解算整周模糊度问题。目前以LAMBDA降相关算法和Lenstra-Lenstra-Lovász(LLL)为代表的规约算法应用最为广泛。由于不同算法采用的模糊度方差-协方差阵的分解方式不同,导致难以合理地进行不同算法性能的比较。该文通过分析LAMBDA算法的降相关特点,从理论上推出基于下三角Cholesky分解多维情形下的整数高斯变换的降相关条件及相应公式,并与分解方式不同的LAMBDA和LLL算法作了对比。实验结果表明,降相关采用的分解方式将会直接影响计算复杂度和解算性能,因此该文推导的整数高斯变换算法便于今后基于下三角Cholesky分解的降相关算法间的合理比较。     

改进的GPS模糊度降相关LLL算法

模糊度降相关技术可以有效提高模糊度求解的效率及成功率,LLL(A.K.Lenstra,H.W.Lenstra,L.Lovasz)算法是新出现的模糊度降相关方法。详细分析LLL算法,针对该算法中存在的缺陷,提出逆整数乔勒斯基、整数高斯算法和升序调整矩阵辅助的改进LLL算法。利用谱条件数及平均相关系数为准则,以300个随机模拟的对称正定矩阵作为模糊度方差-协方差矩阵,对LLL算法和改进的LLL算法进行仿真计算。比较与分析结果表明,改进LLL算法模糊度降相关处理更加彻底,能有效地加速整周模糊度搜索及成功解算。     

GPS/陀螺组合测姿中整周模糊度的快速解算

采用方向余弦矩阵描述姿态,建立GPS/陀螺组合姿态确定系统模型,由矩阵Kalman滤波方法解算整周模糊度的浮点解,然后再利用MCLambda方法得到整周模糊度固定解。仿真实验结果表明,附加方向余弦矩阵约束的Kalman滤波方法可以有效地提高整周模糊度浮点解的精度,使得整周模糊度的固定成功率和效率均得到提高,尤其是在GPS观测条件较差的情况下。     

The least-squares ambiguity decorrelation adjustment: a method for fast GPS integer ambiguity estimation

The GPS double difference carrier phase measurements are ambiguous by an unknown integer number of cycles. High precision relative GPS positioning based on short observational timespan data, is possible, when reliable estimates of the integer double difference ambiguities can be determined in an efficient manner. In this contribution a new method is introduced that enables very fast integer least-squares estimation of the ambiguities. The method makes use of an ambiguity transformation that allows one to reformulate the original ambiguity estimation problem as a new problem that is much easier to solve. The transformation aims at decorrelating the least-squares ambiguities and is based on an integer approximation of the conditional least-squares transformation. This least-squares ambiguity decorrelation approach, flattens the typical discontinuity in the GPS-spectrum of ambiguity conditional variances and returns new ambiguities that show a dramatic improvement in correlation and precision. As a result, the search for the transformed integer least-squares ambiguities can be performed in a highly efficient manner.     

An alternative approach to calculate the posterior probability of GNSS integer ambiguity resolution

When precise positioning is carried out via GNSS carrier phases, it is important to make use of the property that every ambiguity should be an integer. With the known float solution, any integer vector, which has the same degree of freedom as the ambiguity vector, is the ambiguity vector in probability. For both integer aperture estimation and integer equivariant estimation, it is of great significance to know the posterior probabilities. However, to calculate the posterior probability, we have to face the thorny problem that the equation involves an infinite number of integer vectors. In this paper, using the float solution of ambiguity and its variance matrix, a new approach to rapidly and accurately calculate the posterior probability is proposed. The proposed approach consists of four steps. First, the ambiguity vector is transformed via decorrelation. Second, the range of the adopted integer of every component is directly obtained via formulas, and a finite number of integer vectors are obtained via combination. Third, using the integer vectors, the principal value of posterior probability and the correction factor are worked out. Finally, the posterior probability of every integer vector and its error upper bound can be obtained. In the paper, the detailed process to calculate the posterior probability and the derivations of the formulas are presented. The theory and numerical examples indicate that the proposed approach has the advantages of small amount of computations, high calculation accuracy and strong adaptability.     

On lattice reduction algorithms for solving weighted integer least squares problems: comparative study

Decorrelation or reduction theory deals with identifying appropriate lattice bases that aid in accelerating integer search to find the optimal integer solution of the weighted integer least squares problem. Orthogonality defect has been widely used to measure the degree of orthogonality of the reduced lattice bases for many years. This contribution presents an upper bound for the number of integer candidates in the integer search process. This upper bound is shown to be a product of three factors: (1) the orthogonality defect, (2) the absolute value of the determinant of the inverse of the generator matrix of the lattice, and (3) the radius of the search space raised to the power of the dimension of the integer ambiguity vector. Four well-known decorrelation algorithms, namely LLL, LAMBDA, MLAMBDA, and Seysen, are compared. Many simulated data with varying condition numbers and dimensions as well as real GPS data show that the Seysen reduction algorithm reduces the condition number much better than the other algorithms. Also, the number of integer candidates, before and after the reduction process, is counted for all algorithms. Comparing the number of integer candidates, condition numbers, and orthogonality defect reveals that reducing the condition number and the orthogonality defect may not necessarily result in decreasing the number of integer candidates in the search process. Therefore, contrary to the common belief, reducing the orthogonality defect and condition number do not always result in faster integer least squares estimation. The results indicate that LAMBDA and MLAMBDA perform much better in reducing the number of integer candidates than the other two algorithms, despite having a larger orthogonality defect and condition number in some cases. Therefore, these two algorithms can speed up the integer least squares estimation problem in general and the integer ambiguity resolution problem in particular.     

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。      

基于格论的GNSS模糊度解算

快速、准确地解算整周模糊度是实现GNSS载波相位实时高精度定位的关键,由于模糊度之间的强相关,基于整数最小二乘估计准则时,需要较长的时间才能搜索出最优的整周模糊度向量。为了提高模糊度的搜索效率,本文在扼要介绍格论的理论框架基础上,引入基于格论的模糊度解算方法,通过格基规约来降低模糊度之间的相关性,从而快速搜索出最优的整数模糊度向量。与此同时,将GNSS领域的主要降相关方法统一到格论框架下,探讨了并建议采用Bootstrapping成功率作为格基规约的性能指标之一。最后实验分析了三频多系统长基线相对定位情况下,不同格基规约可获得的性能。     

整周模糊度去相关的两种实现方法

整周模糊度搜索一直是GPS快速精确定位的关键问题。短时间的观测会导致观测方程和整周模糊度方差、协方差矩阵的高相关性，因而急剧增大整周模糊度的搜索空间，对整周模糊度未知数方差、协方差矩阵进行去相关性处理，可以有效地压缩搜索空间。本文对整周模糊度去相关的迭代法和联合变换法从原理上进行了阐述，并结合实际算倒进行了分析和比较。     

Reliability of partial ambiguity fixing with multiple GNSS constellations

Reliable ambiguity resolution (AR) is essential to real-time kinematic (RTK) positioning and its applications, since incorrect ambiguity fixing can lead to largely biased positioning solutions. A partial ambiguity fixing technique is developed to improve the reliability of AR, involving partial ambiguity decorrelation (PAD) and partial ambiguity resolution (PAR). Decorrelation transformation could substantially amplify the biases in the phase measurements. The purpose of PAD is to find the optimum trade-off between decorrelation and worst-case bias amplification. The concept of PAR refers to the case where only a subset of the ambiguities can be fixed correctly to their integers in the integer least squares (ILS) estimation system at high success rates. As a result, RTK solutions can be derived from these integer-fixed phase measurements. This is meaningful provided that the number of reliably resolved phase measurements is sufficiently large for least-square estimation of RTK solutions as well. Considering the GPS constellation alone, partially fixed measurements are often insufficient for positioning. The AR reliability is usually characterised by the AR success rate. In this contribution, an AR validation decision matrix is firstly introduced to understand the impact of success rate. Moreover the AR risk probability is included into a more complete evaluation of the AR reliability. We use 16 ambiguity variance–covariance matrices with different levels of success rate to analyse the relation between success rate and AR risk probability. Next, the paper examines during the PAD process, how a bias in one measurement is propagated and amplified onto many others, leading to more than one wrong integer and to affect the success probability. Furthermore, the paper proposes a partial ambiguity fixing procedure with a predefined success rate criterion and ratio test in the ambiguity validation process. In this paper, the Galileo constellation data is tested with simulated observations. Numerical results from our experiment clearly demonstrate that only when the computed success rate is very high, the AR validation can provide decisions about the correctness of AR which are close to real world, with both low AR risk and false alarm probabilities. The results also indicate that the PAR procedure can automatically chose adequate number of ambiguities to fix at given high-success rate from the multiple constellations instead of fixing all the ambiguities. This is a benefit that multiple GNSS constellations can offer.     

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。     

A new approach to GPS ambiguity decorrelation

Ambiguity decorrelation is a useful technique for rapid integer ambiguity fixing. It plays an important role in the least-squares ambiguity decorrelation adjustment (Lambda) method. An approach to multi-dimension ambiguity decorrelation is proposed by the introduction of a new concept: united ambiguity decorrelation. It is found that united ambiguity decorrelation can provide a rapid and effective route to ambiguity decorrelation. An approach to united ambiguity decorrelation, the HL process, is described in detail. The HL process performs very well in high-dimension ambiguity decorrelation tests. Received: 9 March 1998 / Accepted: 1 June 1999     

一种改进的SEVB整数模糊度搜索算法

针对浮点模糊度精度较差时SEVB算法存在搜索耗时较大的问题,提出一种改进的SEVB算法。该算法通过限制初始搜索空间大小和优化计算过程,能够有效减少模糊度搜索候选点个数和不必要的冗余计算,进而提高搜索效率。试验结果分析表明,当浮点模糊度解算精度较低时,改进算法的搜索效率比SEVB算法明显提高,且其搜索耗时不易受模糊度维数及精度的影响,具有更好的稳定性。     

网络RTK参考站间模糊度动态解算的卡尔曼滤波算法研究

提出一种适用于参考站网络的站间模糊度解算方法,该方法使用CA码与相位的电离层无关组合解算宽巷模糊度,利用多路径效应的周期性削弱CA码多路径效应。在宽巷模糊度得到固定后,利用卡尔曼滤波对L1模糊度进行估计,并使用模糊度失相关搜索算法,动态地确定模糊度。这种方法已经应用在自主开发的网络RTK软件上,并以四川GPS综合服务网络SIGN(Sichuan Integrated GPS Network)作为试验网络,进行了试验和分析。