Hybrid derivative-free extended Kalman filter for unknown lever arm estimation in tightly coupled DGPS/INS integration

Differential carrier phase observations from GPS (Global Positioning System) integrated with high-rate sensor measurements, such as those from an inertial navigation system (INS) or an inertial measurement unit (IMU), in a tightly coupled approach can guarantee continuous and precise geo-location information by bridging short outages in GPS and providing a solution even when less than four satellites are visible. However, to be efficient, the integration requires precise knowledge of the lever arm, i.e. the position vector of the GPS antenna relative to the IMU. A previously determined lever arm by direct measurement is not always available in real applications; therefore, an efficient automatic estimation method can be very useful. We propose a new hybrid derivative-free extended Kalman filter for the estimation of the unknown lever arm in tightly coupled GPS/INS integration. The new approach takes advantage of both the linear time propagation of the Kalman filter and the nonlinear measurement propagation of the derivative-free extended Kalman filter. Compared to the unscented Kalman filter, which in recent years is typically used as a superior alternative to the extended Kalman filter for nonlinear estimation, the virtue of the new Kalman filter is equal estimation accuracy at a significantly reduced computational burden. The performance of the new lever arm estimation method is assessed with simulated and real data. Simulations show that the proposed technique can estimate the unknown lever arm correctly provided that maneuvers with attitude changes are performed during initialization. Field test results confirm the effectiveness of the new method.     

面向低成本车载IMU的安装姿态估计

车载IMU相对于车体的安装姿态信息是应用车辆非完整约束的必需条件,而车辆非完整约束可以有效解决GNSS信号长时间中断的情形下低成本INS+GNSS组合导航系统精度降低的问题。本文针对车载场景下的低成本消费级IMU,基于卡尔曼滤波和粒子滤波提出了一种估计IMU安装姿态的算法。该算法无需限制IMU相对于车体的姿态为小角度;随后,基于仿真平台对低成本消费级IMU进行建模,利用生成的若干组不同安装姿态的IMU数据对算法进行验证;最后进行车载测试。仿真结果和车载测试结果都表明,该算法可以准确地估计IMU相对于车体的安装姿态,对于低成本INS+GNSS组合导航系统精度的提高具有实际意义。     

一种新的车载低成本MEMS陀螺仪快速初始对准算法

车载导航系统常用惯性测量元件（IMU）与全球卫星导航系统（GNSS）技术组合以提高系统的稳定性。由于车载导航系统的应用场景限制,对初始对准速度有着较高要求。为了提高传统车载组合导航系统中低成本微机电系统（MEMS）陀螺仪的初始对准速度,降低初始对准过程中的计算量,本文提出了一种适用于任意失准角下的基于网络RTK辅助与无损Kalman滤波（UKF）的MEMS陀螺仪初始对准算法。同时针对车载系统的特点,简化了IMU系统误差方程,分析了简化带来的误差。在诺瓦泰ProPak6和诺瓦泰IMU-IGM-S1组成的导航系统中验证了本文提出的算法。试验结果表明,在以诺瓦泰双天线GNSS输出航向角为"真值"的情况下,本文提出的算法基本可以在5 s内完成陀螺仪的初始对准,对准精度达0.3°。     

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.     

GNSS/里程计组合定位抗差估计法

利用Kalman滤波进行GNSS/里程计组合定位难以实现两种传感器的最优利用。针对轮轨相对运动时里程计数误差增大的情况,提出了对GNSS位置导出里程、速度导出里程和里程计输出里程使用抗差最小二乘求取最优里程;并使用中位数求取抗差迭代初值。增加GNSS测速里程观测值,提高了观测冗余,有利于计算抗差初值。抗差等价权能够平衡3种观测值的比重,增强鲁棒性。计算结果表明,引入速度信息的抗差解法优于单一传感器定位解;也优于传统滤波解法。     

Optimization of two GPS/MEMS-IMU integration strategies with application to sports

The application of low-cost L1 GPS receivers integrated with micro-electro-mechanical system (MEMS) inertial measurement units (IMU) allows the continuous observation of position, velocity and orientation which opens new possibilities for comparison of athletes’ performance throughout a racecourse. In this paper, we compare loosely and closely coupled integration strategies under realistic racing scenarios when GPS is partially or completely masked. The study reveals that both integration approaches have a similar performance when the satellite constellation is completed or the outages are short. However, for less than four satellites, the closely coupled strategy clearly outperforms the loosely coupled approach. The second part of the paper is devoted to the important problem of system initialization, because the conventional GPS/IMU alignment methods are no longer applicable when using MEMS-IMU. We introduce a modified coarse alignment method and a quaternion estimation method for the computation of the initial orientation. Simulations and practical experiments reveal that both methods are numerically stable for any initial orientation of the sensors with the error characteristics of MEMS-IMUs. Throughout the paper, our findings are supported by racing experiments with references provided in both, the measurement and the navigation domains.     

融合GNSS和加速度计监测数据的超高建筑动态特性分析

GNSS和加速度计是目前动态监测超高建筑环境载荷变形的主要手段。GNSS具有无需通视、可直接获取三维位移等优点,但受精度和采样率的限制,其对微变形及高频振动信息不敏感;而加速度计具有高精度和高采样率等优点,但无法监测超高建筑低频的似静态变形。为充分发挥这两种传感器的各自优势,提出利用多速率Kalman滤波和RTS平滑方法对超高建筑GNSS和加速度计监测数据进行融合处理。试验结果表明,与单一的GNSS监测技术相比,该方法有利于削弱GNSS高频噪声的影响,提高位移数据的采样率,可有效识别超高建筑的低频和高频振动频率,提高对微变形振动的监测能力;与单一的加速度计监测技术相比,该方法可以准确监测超高建筑的低频变形信息,具有良好的工程应用价值。     

一种基于渐消因子的区间卡尔曼滤波器方法

在噪声环境中,运动目标发生稳态突变会降低卡尔曼滤波器的滤波性能,进而导致组合导航的可靠性降低,导航系统抗干扰能力下降,影响导航的精确度。为了提高卡尔曼滤波器性能,提高抗干扰能力和导航精度,在采用基于卡尔曼滤波器的超紧耦合同时,提出一种新型的基于渐消因子的区间卡尔曼滤波器算法。该算法通过引入渐消因子和区间矩阵对滤波器增益矩阵进行实时调整,并利用区间运算中的交集运算将各种误差源约束到交集区间,进而保证在区间运算中保真集合映射的完备性并取得最优化。结果显示,该算法能够克服原有滤波器算法的缺陷,在噪声环境中提升对稳态突变目标的跟踪能力,且在噪声中滤波器效果提高,算法计算量没有明显增加。     

国产机载大视场三线阵CCD相机GNSS偏心矢量和IMU视轴偏心角标定技术

针对国内首台自主研制的机载三线阵CCD相机（以下简称GFXJ）,开展了国产GFXJ的GNSS偏心矢量和IMU视轴偏心角标定技术研究工作。首先介绍了GFXJ相机的成像特点,然后分析建立了GFXJ的GNSS偏心矢量标定模型和IMU视轴偏心角标定模型,并提出了GNSS偏心矢量和视轴偏心角循环两步法标定方案,最后在国家嵩山遥感综合实验场获得了多次飞行试验数据。通过进行区域网平差和标定处理,验证了本文建立的GNSS偏心矢量标定模型和IMU视轴偏心角标定模型的正确性和有效性,证实了本文提出的循环两步法标定方案可靠、可行,可显著提升GFXJ的几何定位精度。利用GNSS偏心矢量和IMU视轴偏心角标定值,大幅提高了GFXJ相机的无控定位精度。辅以少量控制点进行区域网平差,GFXJ影像平面定位精度可满足1：1000地形图测图的空中三角测量精度要求,高程精度距离指标要求略有差距。目前该款相机仍在校飞阶段,定型后几何性能有望进一步提高。同时,本文建立的GNSS偏心矢量标定模型和IMU视轴偏心角标定模型及提出的循环两步法标定方案,可为其他机载线阵CCD相机的标定处理提供借鉴。     

机载POS系统对地定位方法初探

高精度定位定向系统(Position&;OrientationSystem,简称POS系统)能够获取机载传感器的空间位置和三轴姿态信息,从而定量化反演遥感信息获取过程,实现机载遥感直接对地定位(DirectGeoreferencing)。本文首先介绍POS与航空摄影系统的集成方法与工作原理;然后初步分析了POS系统的主要误差来源,在此基础上,研究了POS系统数据处理及误差控制方法;最后,结合河南安阳飞行试验数据的分析处理结果,进行了精度和可行性分析。     

封闭环境下限制误差发散的高精度导航定位

基于GNSS系统的导航定位设备在封闭或受阻环境下导航精度受限,为此,提升地下空间或室内定位精度,摆脱对GNSS的依赖是当前的研究热点。针对该问题,本文研究了LiDAR+IMU+DMI多源传感器导航定位技术,通过将LiDAR控制标靶数据带入卡尔曼滤波方程,计算IMU+DMI组合的误差状态向量,限制其误差发散,从而获取设备的高精度位置。该技术能使移动检测设备完全摆脱对GNSS信号的依赖,实现地下封闭空间移动测量设备精确定位,便于地下空间检测。通过在武汉某地铁试验表明,本文算法适用于地下、室内空间封闭环境中无GNSS信号的移动测量设备高精度导航定位。     

相对定向的机载惯性测量装置视准轴误差求解方法

利用航摄像片存在的相对几何位置关系,将机载IMU视准轴误差引入立体像对相对定向模型中,提出了一种基于相对定向的机载IMU视准轴误差求解新方法。详细推导了基于单个立体模型和连续立体模型求解IMU视准轴误差的数学模型,并用三组带有IMU设备获取的实际航空影像数据进行了试验验证。结果表明,所推导的机载IMU视准轴误差求解方法是正确、可行的,利用三张以上相邻像片构成的连续立体模型即可求解出IMU的视准轴误差,避免了野外布设检校场和其他地面控制条件带来的诸多问题,有利于带机载IMU的航空遥感快速对地目标定位。     

改进Sage-Husa算法在飞机组合导航中的应用

针对机载组合导航系统,考虑不同飞行阶段的气压高度,提出一种改进的Sage-Husa自适应滤波算法,以提高组合导航系统定位精度.该算法通过引入气压高度,实时计算并修正滤波异常判定的调节因子,以满足飞机不同飞行阶段的滤波需求.通过捷联式惯性导航系统(SINS)、全球卫星导航系统(GNSS)定位误差特性仿真、卡尔曼滤波组合算...     

BDS RTK／MEMS-INS组合数学模型研究与性能分析

卫星导航系统和惯性导航系统（INS）具有极强的互补性,两者组合能有效提高导航定位结果的可用性、连续性和可靠性. 随着北斗卫星导航系统（BDS）的快速发展和低成本惯导元件（IMU）性能的不断提高,进行基于BDS和低成本IMU的组合导航系统相关理论和技术研究具有很强的研究意义和实用价值. 本文首先对BDS RTK／M-EMS INS组合理论模型进行推导,并利用实测车载数据对组合系统的性能进行分析. 实验结果表明,在BDS中引入低成本IMU,可以在不损失定位精度的同时有效改善测速精度. 组合后在车载动态中定位精度影响为mm级,而速度误差改善在北、东、地方向达到了75.8％、79.5％、66.7％. 此外,在BDS+INS紧组合中使用双频数据可以改善测速定姿精度,速度误差改善为18.2％、33.3％、33.3％,姿态误差改善为41.1％、26.7％、59.0％.      

IMU/DGPS辅助车载CCD及激光扫描仪三维数据采集与建模

三维信息快速采集是真实场景建模与三维虚拟现实技术的关键。本文提出了一种基于激光扫描仪、线/面阵CCD相机及GPS与IMU等多种传感器融合的车载移动式数据快速采集系统。各传感器安置在车内稳定平台上并随车保持一致的运动姿态。通过对GPS和IMU数据进行卡尔曼(Kalman)滤波,可推测出整个系统及各传感器的位置和最佳姿态估计;从扫描仪点云数据可提取出街道场景中事物的三维几何信息;线阵CCD相机用于获取路面带状地物等线性特征;面阵CCD采集街道两侧面状纹理信息,从而快速获得城市目标的地理坐标和三维建模信息,由此可重建城市路面街道的三维真实场景。     

GNSS/INS紧组合最大熵卡尔曼滤波算法

针对实际环境中量测噪声易被野值污染而呈现非高斯分布,进而导致传统卡尔曼滤波(KF)算法性能降低的问题,提出了最大熵卡尔曼滤波(MCKF)算法. 该算法基于最大熵准则(MCC)和M估计的思想推导得到. 与KF相比,所提算法能够给异常量测值分配较小的权重以减轻其对于状态估计的影响,与基于Huber函数的卡尔曼滤波(HKF)算法相比,其能够更有效地利用量测信息,因此所提算法相比于KF和HKF而言更加鲁棒. 在全球卫星导航系统(GNSS)与惯性导航系统(INS)的紧组合模式下进行车载实测实验,由于GNSS的伪距与伪距率等原始量测信息质量不佳,因此KF和HKF的性能均受到影响,而所提MCKF算法能够有效地抑制异常量测值的影响,能够更快地收敛且得到更高的估计精度.      

New optimal smoothing scheme for improving relative and absolute accuracy of tightly coupled GNSS/SINS integration

For mobile surveying and mapping applications, tightly coupled integration of global navigation satellite system (GNSS) and Strap down Inertial Navigation System is usually recommended for direct georeferencing since it can provide position, velocity, and attitude information at higher accuracy and better reliability in a self-contained manner. A post-mission smoothing method is applied to optimally use observation information of both systems and to overcome the shortcomings of Kalman filter in GNSS degraded environments. We propose the revised Rauch–Tung–Streibel Smoother (RTSS) and Forward–Backward combination (FBC) smoothing algorithms for tightly coupled integration. From the analysis and field test, it is found that RTSS smoothing mainly improves the relative accuracy, while FBC mainly contributes to the absolute accuracy. With the complementary characteristics of both smoothing algorithms, an optimal new smoothing scheme combining RTSS with FBC is built. The performance of these three smoothing algorithms is evaluated through a real vehicular test. Compared with RTSS and FBC smoothing algorithms, the new smoothing scheme improves the mean 3D position RMS and the mean 3D attitude RMS by 65.7 and 70%, respectively. It provides better accuracy and smoothness for the position, velocity, and attitude at the same time.     

微小型IMU/GPS组合定位定向系统研究

介绍了一种低成本微小型惯性测量组件(inertialmeasurementunit,IMU)和双天线GPS构成的组合定位定向系统。为确保组合系统的实时性和滤波稳定性,提出了一种基于UD分解的快速卡尔曼滤波算法,给出了IMU/GPS组合系统的软硬件设计和实验结果。该组合系统应用于炮兵测地车,具有成本低、精度高等优点,能够提高炮兵测地保障的精度和速度。     

GNSS系统间钟差辅助定位研究

GNSS（global navigation satellite systems）系统时使用原子钟作为时间基准,相比使用晶振的GNSS接收机,其稳定度高出几个量级。GNSS系统间钟差相比接收机钟差具有更高的稳定度,如果可以充分利用此先验信息将有助于优化多GNSS系统的定位结果。分析如何充分利用系统间钟差更稳定这一先验信息,并测试引入这一先验信息对多系统单点定位结果的影响,推导了基于两种不同钟差估计方法的定位解算模型,给出了最小二乘和扩展卡尔曼滤波两种参数估计算法。通过比较不同模型和估计算法在静态和动态定位的实验结果,最小二乘法无法利用系统间钟差更稳定的特点改善定位精度;静态实验结果表明,扩展卡尔曼滤波自身有一定的降噪效果,若引入系统间钟差更稳定的先验信息,将更有利于减小噪声;动态实验结果表明,引入系统间钟差更稳定的先验信息可减弱扩展卡尔曼滤波的发散现象。