一种多卫星导航系统快速选星算法研究与仿真

多卫星系统导航时,可见星数目大幅度增加,导致导航解算运算量成几十倍增长,加重了用户接收机尤其高动态接收机的负担。针对传统选星算法在卫星数目较多时耗时太长,无法满足导航实时性要求的问题,提出了一种适用于多卫星导航系统选取多颗次优星的快速选星算法,通过建立多卫星导航系统的几何精度因子（GDOP）分析模型,基于可见星仰角及方位角的分区、筛选,给出了被排除卫星的分布规律。仿真结果表明,在损失较少定位精度的情况下,可大大减少导航定位解算的运算量。     

GNSS/SINS紧耦合选星算法研究

通过分析全球导航卫星系统(Global Navigation Satellite System,GNSS)和捷联惯性导航系统(Strap-down Inertial Navigation System,SINS)紧耦合导航系统中最小GDOP法的现有问题,提出一种改进的GNSS/SINS紧耦合选星算法。该算法的主要步骤包括:首先将可见卫星进行伪距异常值检测,并剔除异常值卫星;然后根据卫星的分布特点,利用卫星仰角信息选取最大的一颗作为天顶星,最后将剩余可见卫星随机组合选取三颗卫星和天顶星组成四颗星,得到最终的选星方案。经过与最小GDOP法仿真对比,该算法计算量明显减小,且在保证定位精度的前提下,减少了计算的卫星数目,降低了运算量,具有较好的选星效果。     

GPS与DTMB组合定位系统的改进选星算法研究

针对GPS卫星信号在楼群密集的城市和室内存在定位盲区而无法单独完成定位的难题,提出GPS-DTMB组合导航定位方法。在多源信号组合导航定位系统中,为了解决导航定位精度与运算复杂度间的矛盾,研究改进的加权行列式选星算法在GPS与DTMB组合定位系统中的可行性,与传统最小GDOP选星算法相比较,改进的选星算法具有运算复杂度低、计算消耗时间短的优点,并且对比分析在不同的组合卫星数目中,该算法与直接利用传统最小GDOP选星算法相比较的偏差大小,仿真表明,在15种组合卫星中,偏差小于0.1的概率在90%左右,能够得到满意的性能,证明了此方法的实用性。     

GPS/北斗组合卫星导航系统快速选星算法

为实现GPS/北斗组合卫星导航系统的快速选星,提出一种基于几何布局的快速选星算法。根据最优选星方案的卫星分布特点,利用卫星高度角和方位角信息实现卫星的区域划分,应用代价函数法对中仰角区域的卫星进行筛选,得到最终的选星方案。与最优选星算法相比,该算法计算量明显减小;仿真结果表明,该算法能将几何精度因子(GDOP)控制在小于2.5的范围内,具有较好的选星效果。综合考虑算法复杂度和选星效果,基于几何分布的快速选星算法能够满足航空航天等对精度和实时性要求较高的领域的需求。     

基于哈里斯鹰优化算法的多GNSS快速选星方法

为提高多全球导航卫星系统(Global Navigation Satellite System, GNSS)组合导航选星的效率，提出了一种基于哈里斯鹰优化(Harris Hawks Optimization, HHO)算法的快速选星方法。该算法模仿哈里斯鹰捕食的特点，结合莱维(Levy)飞行实现对复杂多维度问题的求解，用该算法解决选星问题既能保证获得理想几何构型，又能大幅度减少接收机运算量，提高选星的实时性。通过仿真实验，调用卫星工具包(Satellite Tool Kit, STK)导出的导航卫星数据，分析不同的参数变化对HHO快速选星算法结果的影响。结果表明，在从17颗可见星中选择8颗进行定位时，HHO法与遍历法相比，几何精度因子(Geometric Dilution of Precision, GDOP)计算平均误差为0.318 9%,所节省的时间占遍历法耗时的74.830 6%,证明了该选星算法具有计算效率高、耗时短、精度高的优点，适用于多星座多选星的情况。     

一种新的卫星导航系统快速选星方法

 从几何精度因子(GDOP)与星座几何布局的关系出发,提出了用于多星座卫星导航系统的快速选星方法——几何布局选星法.该方法以满足定位要求为前提,按卫星在星座中均布为原则来进行选星.仿真结果表明,相对传统GDOP选星法,计算量减少99%以上,而GDOP满足要求,同时,选星数较少且简洁快速,有效满足了选星求解的实时性和精度要求.     

靶场试验BD/GPS测量系统组合选星算法

针对靶场试验多星测量系统接收机的应用,为了提高导航系统的定位精确度,将北斗(BD)与全球定位系统(GPS)进行组网增加可见星。在分析几何精度因子(GDOP)与定位精确度关系基础上,提出一种模糊选星方法,对组网卫星星座进行优化组合。仿真结果表明,不论是单一系统还是双系统,模糊选星算法精度因子接近当下时刻最优,且组合系统在可见星和精确度方面优于单一定位系统,其研究结果对北斗系统的精确度验证和多星测量系统接收机在靶场的应用具有重要参考意义。     

复杂环境下的卫星导航选星算法

针对当前选星算法基于理性环境设计和在多系统兼容接收机中运算量巨大的问题,从复杂使用环境模型出发,提出了具有运算量小、各种使用环境下均能保持较好星座构型的复杂环境选星算法。仿真分析结果表明该方法在运算量方面大幅度优于其他算法,全局几何精度因子(Global Dilution of Precision,GDOP)在理性环境下与其他方法接近,在复杂环境下优于其他方法。     

卫星导航系统基四选星算法

随着卫星导航系统的发展,可见星数大大增多,为了减少接收机运算量,需选择可见卫星的一个子类。本文分析了卫星导航系统中N个4星最优分布与某4星最优分布中的k(k≤3)个卫星(共4N+k颗卫星)的联合分布GDOP值,并以此为基础提出了一类低复杂度近似最优的基四选星算法。该算法以经典的4星选择算法(最大体积法和四步选择法)为基础,通过多次迭代与部分执行,实现任意多于4星的卫星选择。仿真结果表明该算法与准优算法相比运算复杂度和GDOP值都更低。     

一种GPS/BDS双模导航系统的选星算法

为解决传统选星算法中定位精度与计算GDOP复杂度之间的矛盾,提出一种改进的快速卫星选择算法。通过利用卫星的高度角和方位角信息划分区域,剔除信噪比小于一定门限的卫星,应用牛顿恒等式的GDOP计算方法,最终确定选择6颗卫星进行定位。用于较少周期计算的最佳卫星几何分布划分区域可以极大减少运算量,理论分析和实验结果表明,与最小GDOP算法相比,在略微牺牲定位精度的情况下,所提算法计算量显著降低,而定位精度优于Quasi-Optimal算法,证明了该算法的有效性。     

Research of Fast Satellite Selection Algorithm for Multi-constellation

We propose a fast satellite selection algorithm for multi-constellation,which is based on both Newton's identities for Geometric dilution of precision (GDOP)fast computation and optimal satellite geometric distribution for less cycle participation.An effective closed-form formula is proposed for GDOP approximation,avoiding conventional matrix inversion and complexity.We reduce computational cycles with auxiliary optimal satellite geometric distribution,which improves the efficiency of realtime positioning under the combined action of both.Simulation results indicate that we can save more than 44％ and 99％ computational complexity when selecting 7 satellites from 10 and 30 visible satellites respectively.The GDOP of proposed method is very close to optimal result,better than that of Quasi-Optimal algorithm.     

“北斗”卫星导航系统仿真与全球覆盖分析

为了解“北斗”卫星导航系统( BDS)的星座分布及其正式运行后卫星在全球范围内的覆盖情况,依据BDS卫星轨道参数,使用卫星工具包( STK)构建了完整的BDS空间星座,对BDS卫星轨道信息、可见性以及对地覆盖品质进行分析。在对可见性及覆盖品质的分析中,分别仿真分析了在4个不同的城市和全球范围进行观测得到的几何精度因子( GDOP)和导航精度等参数性能。研究结果对分析BDS系统的服务性能以及提高BDS的导航精度有一定的参考意义。     

GPS-BASED LEO SATELLITE POSITION AND ATTITUDE DETERMINATION AUGMENTED BY PSEUDOLITES ON EARTH

GPS-based navigation and attitude determination of LEO satellites is presently considered as an alternative to the conventional systems which utilize earth sensors and magnetometers. The onboard GPS receiver determines the orbit position of the LEO satellite by the conventional system of linearized navigation equations, requiring the simultaneous reception of ranging signals from four GPS satellites by a single antenna. For attitude determination, pairs of antennae, suitably mounted on the satellite and feeding a common receiver, form several interferometric baselines. The baselines vectors, defined in a given coordinate system, determine the attitude of the satellite. For each baseline and each GPS satellite, the difference in phase of the received signal carriers is measured. The differencing operation eliminates the receiver clock bias. Solutions for the baseline vectors can be obtained with signals received from only three GPS satellites. If the coverage of a receive antenna is restricted to less than the hemisphere it will not have four GPS satellites in view all the time. It is demonstrated that a GPS pseudolite transmitter located on earth supplements the system, which then provides a usable geometric dilution of precision (GDOP) for position determination and an improved position dilution of precision (PDOP) for attitude determination. Pseudolites can be co-located with the gateways which provide access to the public switched telephone networks (PSTNs) for the LEO communication satellites.     

成熟因子映射的双系统选星方法

传统的选星方法通常以遍历为手段,在可见星较多的情形下往往计算量很大。常规的遗传算法通常固定交叉和变异概率,产生不必要的时间消耗。针对这些问题,提出了引入成熟因子映射交叉概率和变异概率的双系统遗传选星算法,目的在于快速地找到最优解或可接受的次优解。该方法以几何精度因子(Geometric Dilution of Precision, GDOP)为适应度,构造单染色体种群,定义成熟度来指导交叉变异操作,再经过每代精英保留策略和隔代种群数量控制,最终搜索得到符合门限的可接受解。实验结果表明,在进化200代的条件下,成熟因子映射遗传算法比常规遗传算法的搜索时间平均节省约24.75%,引入种群数量控制机制后搜索时间进一步节省了约55.32%。该方法可以快速获得稳定数学期望的可用选星集合。     

A Fast Satellite Selection Algorithm: Beyond Four Satellites

Satellite selection is an important executive logic to prevent unnecessary navigation signals in the first place for Global Positioning System (GPS) receivers with limited number of channels and real-time processing power, such as those used in mobile phones, cars, and space crafts. In this paper, we propose a fast satellite selection algorithm to select more than four satellites based on the optimal geometries, which can obtain the smallest geometric dilution of precision (GDOP) values. The main idea of this fast algorithm is to select a subset of all satellites in view whose geometry is the most similar to the optimal geometry. Computer simulation shows that the consumed time of this algorithm is very close to that of the quasi-optimal satellite selection algorithm and obviously lower than that of the traditional optimal satellite selection algorithm to minimize GDOP factor, but the increased GDOP values relative to the minimal GDOP values are much smaller than those of the quasi-optimal satellite selection algorithm.     

BD-2几何精度因子仿真分析

＂北斗二号＂卫星导航定位系统（简称BD-2）是我国自主研制的新一代卫星导航系统。基于STK软件建立了BD-2的星座模型,并着重从星座的整体覆盖性能、对于我国境内及我主要战略方向的覆盖情况、星座中三种卫星对于GDOP的影响情况和星座的冗余性四个方面进行了仿真分析。针对仿真分析中发现的问题和不足,提出了完善BD-2的几点建议。     

Rapid and Precise GLONASS GDOP Approximation using Neural Networks

Russian global navigation satellite system (GLONASS) provides civilian and military users three-dimensional position determination and navigation services as same as US global positioning system. Geometric dilution of precision (GDOP) provides a simple interpretation of positioning precision. Usual method for GLONASS GDOP calculation is matrix inversion. However this process imposes a huge calculation load on receiver, especially when large number of visible satellites exists. To overcome this problem, artificial neural network is used. Different configurations and training methods are simulated on a data base obtained by a GLONASS receiver. Then navigation precision and execution times are explored and compared. Results show that recurrent neural network has 0.00024 RMS error, which is the best against other focused tools including feed forward back propagation and radial basis function neural network with usual training and with genetic algorithm adopted weights and biases.