CNS+GNSS+INS船载高精度实时定位定姿算法改进研究

天文导航（CNS）、卫星导航（GNSS）和惯性导航（INS）3种系统组合可提供高精度的定位定姿结果。实际工程中因INS长时间误差累积，以及系统硬件传输存在不可忽略的时间延迟，导致INS提供给CNS的预报粗姿态误差较大，恶劣海况下难以保障快速搜星，造成天文导航可靠性下降、姿态测量精度较低的问题。为此，本文提出了一种CNS+GNSS+INS高精度信息融合实时定位定姿框架，引入了等角速度外推措施，有效地解决了惯导信息延迟问题。通过高精度转台模拟恶劣海况下载体大角速度摇摆，验证了本文提出的改进算法的有效性。试验结果表明，该算法架构简单，性能可靠，显著提高了恶劣环境下星敏感器的快速、准确搜星能力，保障了三组合姿态测量的精度和可用性。     

Using Allan variance to improve stochastic modeling for accurate GNSS/INS integrated navigation

MEMS-based integrated system of a global navigation satellite system (GNSS) and an inertial navigation system (INS) has been widely used in various navigation applications. However, such integration encounters some major limitations. On the one hand, the noisy MEMS-based INS undermines the accuracy with time during the frequently occurring GNSS outages caused by signal blockage or attenuation in certain situations such as urban canyon, tunnels, and high trees. On the other hand, the model mismatch between actual GNSS error and the assumed one would also degrade the obtained accuracy even with continuous GNSS aiding. To improve the overall performance for GNSS/MEMS-INS, better error models can be obtained using Allan variance (AV) analysis technique for modeling inertial sensor errors instead of the commonly recommended auto-regressive processes, and on the other hand, the measurement update in Kalman filter is improved using innovation filtering and AV calculation. The performance of each method and the combined algorithm is evaluated by a field test with either differential GNSS (DGNSS) or single-point positioning (SPP) as external aid. In addition to the considerable navigation enhancement brought by each method, the experimental results show the combined algorithm accomplishes overall accuracy improvements by about 18% (position), 8% (velocity), and 38% (attitude) for integration with DGNSS, and by about 15% (position), 75% (velocity), and 77% (attitude) for that with SPP, compared with corresponding traditional counterparts.     

PPP／INS组合系统研究进展与展望

随着GNSS多系统的建设,精密单点定位(PPP)技术向着实时固定解方向发展,PPP／INS组合系统因无需布设基准站,在导航测绘领域有着广泛应用前景。本文系统总结了GNSS多系统、PPP模糊度固定、INS辅助对PPP／INS组合系统的贡献,以及组合系统在滤波方法、平滑算法、随机模型建立等方面的工作,并展望了PPP／INS组合系统的发展趋势。      

附加约束条件对GNSS/INS组合导航结果的影响分析

针对车载全球导航卫星系统/惯性导航系统（global navigation satellite system/inertial navigation system,GNSS/INS）组合导航中卫星信号中断,惯性导航系统单独导航误差积累较大的问题,提出了附加载体运动条件约束的卡尔曼（Kalman）滤波解算方法。通过利用载体固有的运动约束,包括近似高程约束、近似速度约束和近似姿态约束,减少载体自由度和模型参数;通过引入新的观测类型,增加观测冗余,可以加强Kalman滤波解,提高在GNSS信号中断时组合导航系统的定位精度,实现无缝导航。     

基于抗差EKF的GNSS/INS紧组合算法研究

提出了GNSS/INS紧组合导航的抗差EKF算法,采用21状态GNSS/INS紧组合状态方程,根据多余观测分量及预测残差统计构造抗差等价增益矩阵,建立抗差EKF算法,通过迭代给出GNSS/INS组合导航的抗差解,并开发GNSS/INS紧组合导航模拟平台,通过对观测值加入单粗差、多粗差及缓慢增长三类误差,测试本文算法对不同粗差的抑制能力。分析表明,抗差EKF可以将三类粗差抑制在相应观测值的残差中,达到削弱其对状态参数估计的影响。本文算例证明,抗差EKF算法可将导航解的误差精度从dm级提高为cm级甚至mm级,导航精度及可靠性得到明显提高。     

An enhanced MEMS-INS/GNSS integrated system with fault detection and exclusion capability for land vehicle navigation in urban areas

We describe an enhanced quality control algorithm for the MEMS-INS/GNSS integrated navigation system. It aims to maintain the system’s reliability and availability during global navigation satellite system (GNSS) partial and complete data loss and disturbance, and hence to improve the system’s performance in urban environments with signal obstructions, tunnels, bridges, and signal reflections. To reduce the inertial navigation system (INS) error during GNSS outages, the stochastic model of the integration Kalman filter (KF) is informed by Allan variance analysis and the application of a non-holonomic constraint. A KF with a fault detection and exclusion capability is applied in the loosely and tightly coupled integration modes to reduce the adverse influence of abnormal GNSS data. In order to evaluate the performance of the proposed navigation system, road tests have been conducted in an urban area and the system’s reliability and integrity is discussed. The results demonstrate the effectiveness of different algorithms for reducing the growth of INS error.     

惯导辅助的无电离层与宽巷组合周跳探测与修复方法

由于卫星信号被遮挡、低信噪比或接收机运动等原因,载波相位观测值较正常值会发生周跳。为解决这一问题,基于精密单点定位与惯导组合系统,提出了一种有效的惯导辅助周跳探测与修复方法。该方法基于无电离层（ionospheric free,IF）组合与宽巷（wide lane,WL）组合,利用惯导短时高精度信息代替伪距消除站星几何距离,结合历元间差、星间差等建立惯导辅助的IF组合模型和惯导辅助的WL组合模型。惯导辅助的IF组合模型不受电离层延迟影响,但无法探测特殊比例周跳,惯导辅助的WL组合模型波长较长,却无法探测双频等周周跳,两者的综合使用实现了优势互补。实验结果表明,该方法不仅能有效探测出各种大、小、双频等周和特殊比例周跳,而且在一定卫星信号中断时间内能实现周跳瞬时校正。     

GNSS/INS组合导航系统定位精度分析

GNSS/INS组合导航系统近年来得到了快速发展,应用领域越来越广泛。组合导航的定位精度是一个重要的研究方向,本文将应用于航空遥感领域的高精度GNSS/INS组合导航系统放置在地面平台上,采集试验数据,通过与NRTK定位结果比较,对组合导航系统定位精度进行分析,得出GNSS/INS组合导航系统的定位精度可达到厘米级的试验结论。     

INS辅助的实时动态GNSS单频周跳探测

采用全球卫星导航系统（Global Navigation Satellite System，GNSS）模糊度固定解可提高GNSS/惯性导航系统（inertial navigation system，INS）组合导航定位精度，而在复杂环境下，单频GNSS难以实现完善的实时动态周跳探测，影响GNSS模糊度保持。研究了星间单差与站星双差的INS辅助GNSS单频周跳探测检验量，重点分析检验量的误差特性。分析得出检验量误差主要与INS增量误差有关，受接收机至待检星与参考星之间星地矢量夹角的影响。提出了选取两颗参考星并优选探测检验量的方法，降低方位角因素的影响，提高周跳探测性能。周跳探测的阈值在滑动窗口内估计，对INS误差被GNSS误差淹没的部分进行抑制，充分反映INS误差影响，阈值估计具有较强的自适应性。     

视觉辅助下的室内惯导位姿修正

惯性导航系统可以短期内提供连续的高精度信息，但是误差会随时间增大，不能长期独立工作。而在大型仓库、地下停车场等室内卫星信号薄弱的场景中，传统的惯导+卫星组合方法也不再适用。针对该问题，本文提出了一种视觉与惯导组合定位的方法。本文研究的惯导+视觉组合的定位方法中，采用基于合作目标的单目视觉定位方法对惯导误差进行修正。对于惯导误差的修正方法，本文利用视觉定位的位姿信息建立量测方程，进行卡尔曼滤波，并选取合适的试验设备，通过实际试验对比验证了该算法对惯导系统误差的修正具有良好的效果。     

组合导航技术在出租车计价系统中的应用

针对目前出租车计价器在计时和计程方面存在的问题,提出了利用GNSS+INS+Odometer组合导航技术进行出租车计价的方法。运用组合导航技术对出租车载客过程中里程和时间进行计算,一方面可以避免传统计价器在使用过程中由于计价器本身以及人为因素等原因,改变实际的行驶里程,影响计价的准确性,从而提高计价的准确性;另一方面,可以避免单一的里程计计价过程中一旦出现故障,里程结果出现较大误差或无法计价等情况,增加出租车计价系统的可靠性。通过跑车试验证明：将组合导航技术应用于出租车计价系统中,能够对出租车计程、计时以及低速计价等关键问题作出很大改进,在计价的精度和可靠性方面都有显著提高;避免因计价器不准等原因造成的乘客、出租车司机之间的矛盾,维护双方的合法权益;对组合导航技术的发展起到了很好的推动作用。     

抗差估计在RTK／INS紧组合中的应用研究

针对动态环境下GNSS／INS导航定位结果常受粗差影响的问题,提出了一种基于抗差卡尔曼滤波的GPS／BDS双系统RTK／INS紧组合导航定位算法,根据方差膨胀模型,建立抗差卡尔曼算法,得到GNSS／INS紧组合抗差解,并通过两个不同区域的实测车载实验进行了算法验证. 实验结果表明:本方法相较于传统方法，在N、E、D三个方向的导航精度分别提高1.4～4.6 cm,0.7～9 cm,1.5～2 cm,模糊度固定成功率提高10.3％～25.6％,导航精度及可靠性得到显著提高,对动态环境下车载或自动驾驶等应用具有一定的理论参考和实用价值.      

INS/GNSS/ODO嵌入式系统的容错技术研究

车载低成本嵌入式组合导航系统的可靠性容易受到多种传感器故障和环境的影响,基于全球卫星导航系统(GNSS)状态的惯性导航系统(INS)/GNSS/里程计(ODO)抗差组合导航算法,提出了一种两级故障检测处理方法. 其中,第一级检测使用了基于解析冗余的残差卡方检验法,第二级检测使用了改进的双状态传播卡方检验算法. 利用自主研制的GN310低成本嵌入式系统采集路测数据. 结果表明:相对于传统算法,水平定位精度提升了39.7%;另外在半实物仿真下,水平定位误差保持在3 m以内,表明该容错方法能够有效地处理ODO、INS故障和GNSS软硬故障.      

基于预积分的IMU/ODO外参估计

里程计通常被用于辅助车载GNSS/INS组合导航系统,以解决当遇到高楼、密林、隧道等信号干扰和遮蔽严重情景时导致精度下降的问题,而里程计辅助需要获取准确的里程计杆臂和安装角。鉴于此,本文提出了一种基于预积分的IMU/ODO外参估计算法,使用由里程计观测和GNSS/INS组合导航解算得到的一段时间内的里程增量差异构建代价函数,通过非线性优化器进行标定参数求解。仿真与实际测试均表明了本文标定方法的有效性,里程计观测在经过标定外参补偿后,可为车载GNSS/INS组合导航系统提供厘米级的精度辅助。     

GNSS/INS紧组合导航系统自主完好性监测分析

可靠性和对故障的可区分性是评价系统完好性的两个重要因素。本文将多GNSS系统与不同精度的INS系统进行组合,由此分析不同因素对组合系统内部的可靠性和故障探测与隔离能力的影响。仿真结果表明,集成多GNSS系统可以改善卫星星座的几何分布结构,从而提高系统的内部可靠性和对故障的区分能力;当GNSS系统与INS系统相结合时,也能大幅度提高系统的可靠性和区分性;相较于低精度的INS系统,采用高精度的INS系统能够进一步的提高系统的可靠性,并增强对故障的区分能力。     

Use of a reduced IMU to aid a GPS receiver with adaptive tracking loops for land vehicle navigation

A reduced inertial measurement unit (IMU) consisting of only one vertical gyro and two horizontal accelerometers or three orthogonal accelerometers can be used in land vehicle navigation systems to reduce volume and cost. In this paper, a reduced IMU is integrated with a Global Positioning System (GPS) receiver whose phase lock loops (PLLs) are aided with the Doppler shift from the integrated system. This approach is called tight integration with loop aiding (TLA). With Doppler aiding, the noise bandwidth of the PLL loop filters can be narrowed more than in the GPS-only case, which results in improved noise suppression within the receiver. In this paper, first the formulae to calculate the PLL noise bandwidth in a TLA GPS/reduced IMU are derived and used to design an adaptive PLL loop filter. Using a series of vehicle tests, results show that the noise bandwidth calculation formulae are valid and the adaptive loop filter can improve the performance of the TLA GPS/reduced IMU in both navigation performance and PLL tracking ability.     

陆地导航中GNSS/陀螺仪组合实时测姿方法

在陆地导航系统中使用GNSS/INS组合导航会增加系统成本,多天线GNSS测姿精度受基线长度影响,且存在的模糊度固定问题。本文提出仅利用一个陀螺仪和单天线GNSS组合来进行实时测姿。先由单天线GNSS计算姿态角3参数,航向角为陆地导航的关键参数,为此将陀螺信息与GNSS导出的航向角进行融合。分析了单天线测姿在载体静止或低速运动时精度很差的原因,提出了在组合滤波中进行解决的方案。推导了GNSS和陀螺信息融合的滤波模型,将陀螺仪信息作为状态模型的控制输入,以GNSS航向为滤波观测值。实验结果表明,GNSS/陀螺仪组合计算的航向角精度和可靠性相对GNSS测姿结果均有很大提升。     

全张量地磁梯度基准图构建及其组合导航方法

为实现利用地磁梯度数据的惯性/地磁梯度辅助导航,给出移相 二维余弦变换时域微分公式,推导出全张量地磁梯度计算公式,实现了高精度地磁梯度导航基准图的构建。并将改进的粒子滤波算法应用于地磁梯度辅助导航系统中,能很好地抑制纯惯性导航系统随时间累积的误差。通过仿真实验分析,验证地磁梯度数据计算方法以及辅助导航滤波算法的有效性和鲁棒性,地磁梯度辅助导航系统的定位精度在100 m以内     

GNSS+INS组合定位在矿山无人驾驶卡车的应用

面向矿山无人驾驶卡车场景,针对GNSS定位不连续且容易被干扰、INS存在累计误差的缺点,本文提出了一种基于GNSS+INS组合的导航算法,该算法融合了两种算法的优点,提高了定位的精度和可靠性。分别将RTK算法和组合导航算法结果与开源软件RTKLIB和NovAtel板载输出结果对比。试验结果表明,本文算法在精度上与NovAtel板载输出结果基本持平,明显优于RTKLIB软件。本文算法平面和高程误差均值及STD均优于5 cm,姿态误差均值和STD优于1°,可以满足矿用无人驾驶卡车的定位精度需求。     

A two-dimensional pedestrian navigation solution aided with a visual gyroscope and a visual odometer

Integration of different positioning systems such as wireless local area networks (WLAN) and INS has proven to be the most promising approach for navigation in global navigation satellite system challenging environments. However, the integrated solution suffers from the errors induced by the sensors in the INS and the low availability and infrastructure dependency of the WLAN. Visual aiding is a complementary method for augmenting these systems, because it suffers from errors and availability problems of a different nature. We introduce the concept of a “visual gyroscope” and a “visual odometer,” based on recovering user information by tracking the feature motion between consecutive images. All calculations are of sufficiently low complexity to be adoptable for navigation with current smartphones. The camera orientation is observed using the vanishing point locations, and this information is transformed into user heading change information and also used for visual odometer calculations. The visual odometer retrieves the camera translation information based on a special camera configuration and the motion of the matched scale-invariant feature transform features. The performance of both of these tools is evaluated. Experiments in selected environments have proven that visual aiding with a visual gyroscope and visual odometer improves the two-dimensional navigation solution significantly.