MEMS IMU辅助的高性能GPS接收机设计

高性能稳健性的GPS卫星接收机仍然是当前研究和发展的热点。在高动态条件下,GNSS接收机设计总是涉及到跟踪动态性能所要求的环路带宽和噪声所要求的环路带宽一对矛盾体。以微惯性测量单元(MIMU)辅助的GPS接收机为实例,设计了MIMU辅助的GPS接收机搜索算法和跟踪算法,同时为减少GPS接收机对惯性器件的性能的依赖,设计了基于MIMU辅助的最优GPS接收机的环路带宽。通过仿真和车载试验对所设计的方法进行验证,仿真和试验结果表明,MIMU辅助的GPS接收机动态性能取决于MIMU的性能指标和环路的带宽,而抗干扰性能至少有13 dB的提高;跑车试验中,商用GPS接收机和研制的GPS接收机精度大体相当。同时系统还能够提供姿态角信息。     

基于GPS软件接收机的SINS／GPS紧组合系统仿真

为了系统验证SINS／GPS紧组合系统的性能,基于GPS软件接收机,进行了仿真系统构建。仿真系统由轨迹发生器、GPS中频信号模拟器、IMU信号模拟器、GPS软件接收机、SINS导航解算模块、组合滤波算法和导航性能分析模块等部分构成,其中详细设计了GPS软件接收机中的捕获和跟踪算法、SINS解算以及基于伪距和伪距率的组合滤波算法。仿真结果表明：紧组合导航系统收敛性较好,能够一定程度上抑制惯导系统误差的积累,有较好的导航性能。设计的该系统满足紧组合导航系统性能验证的需要,也为后续的超紧组合研究奠定了良好的基础。     

Application of Adaptive Kalman Filtering Algorithm in IMU/GPS Integrated Navigation System

The IMU(inertial measurement unit) error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering is applied in IMU/GPS integrated navigation system, in which the adaptive factor is replaced by the fading factor. A practical example is given. The results prove that the adaptive filter combined with the fading factor is valid and reliable when applied in IMU/GPS integrated navigation system.     

IMU/GPS组合导航系统自适应Kalman滤波算法

给出了IMU在地固坐标系中的误差方程，介绍并分析了自适应滤波和渐消Kalman滤波算法原理，然后将渐消因子引入到自适应滤波算法中。并将其应用到IMU／GPS松组合导航系统中，最后利用一个实际算例证明了该组合导航系统的有效性。     

一种SINS/GPS深组合导航系统技术问题分析

为了满足高动态用户及强干扰条件下的应用需求,提出了一种基于卫星信号矢量跟踪的SINS/GPS深组合导航方法,设计了基于FPGA硬件平台的实施方案。利用组合卡尔曼滤波器反馈回路取代了传统接收机中独立、并行的跟踪环路,能够同时完成所有可视卫星信号的跟踪和导航信息处理;通过矢量跟踪算法对所有可视卫星信号进行集中处理,能够增强跟踪通道对信号载噪比变化的适应能力,从而提高接收机在强干扰或信号中断条件下的跟踪性能;根据SINS导航参数和星历信息推测GPS伪码相位和多普勒频移等参数,用以辅助卫星信号的捕获和跟踪,能够大大缩短接收机的搜索捕获时间,并增强接收机在高动态条件下的跟踪性能。基于矢量跟踪的深组合方法不仅在GPS信号短暂中断期间,能够保证系统的导航精度和可靠性,而且在强干扰环境中能够维持较好的伪码相位和载波频率跟踪性能。     

Design of a low-cost attitude determination GPS/INS integrated navigation system

Due to their complementary features of GPS and INS, the GPS/INS integrated navigation system is increasingly being used for a variety of commercial and military applications. An attitude determination GPS (ADGPS) receiver, with multiple antennas, can be more effectively integrated with a low-cost IMU since the receiver gives not only position and velocity data but also attitude data. This paper proposes a low-cost attitude determination GPS/INS integrated navigation system. The proposed navigation system comprises an ADGPS receiver, a navigation computer unit (NCU), and a low-cost commercial MEMS IMU. The navigation software includes a fault detection and isolation (FDI) algorithm for integrity. In order to evaluate the performance of the proposed navigation system, two flight tests have been performed using a small aircraft. The first flight test confirmed the fundamental operation of the proposed navigation system and the effectiveness of the FDI algorithm. The second flight test evaluated the performance of the proposed navigation system and demonstrated the benefit of GPS attitude information in a high dynamic environment. The flight test results show that the proposed ADGPS/INS integrated navigation unit gives reliable navigation performance even when anomalous GPS data is provided and gives better navigation performance than a conventional GPS/INS unit.     

基于矢量跟踪的GNSS／SINS相干深组合系统研究

为满足组合导航系统在高动态环境下的性能要求,设计基于矢量跟踪的GNSS／SINS相干深组合导航方法。利用矢量跟踪环路将所有可视卫星的跟踪和导航解算融为一体,增强通道间的辅助;高动态对载波跟踪影响更大,在通道预滤波中将码环载波环分别用独立的滤波器处理,组合滤波中采用通道间差分降低滤波状态维数,提高计算效率。引入惯导的加速度辅助本地信号参数预测,较精确地测量卫星视线方向的加速度,减小接收机在高动态时段的剩余动态,提高本地信号参数的预测精度。基于矢量跟踪软件接收机搭建相干深组合仿真系统,实验表明该方法在高动态等环境下能提高信号跟踪性能,改善系统的精度、可靠性。      

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。     

一种车辆GPS/DR/DMAP组合导航系统

车辆导航系统常采用GPS/DR组合导航方式,在实际使用中若采用联邦滤波器,由于设计结构复杂,各种参数选取对滤波器的性能影响较大,选取不当反而引起导航精度的降低。本文设计了一种GPS/DR滤波器,这种滤波器当GPS接收机在失锁时仍能够为用户导航,并且该滤波器结构简单,设计简易。本文采用了DMAP,纠正了车辆相对于道路的偏差。实测导航实验验证了该滤波器能够满足导航精度要求,适用于城市中车辆导航应用。     

一种低动态高抖动环境下的GPS/INS紧组合方法

针对低动态高抖动环境下,影响GPS/INS紧组合精度的重要因素——惯性测量单元(IMU)数据中的噪声,该文提出利用小波降噪方法分离IMU数据中的噪声和有用信号以提高GPS/INS紧组合的精度。首先对IMU数据进行小波分解后得到的高频系数进行阈值量化处理,然后将GPS观测数据与降噪后的IMU数据进行GPS/INS紧组合解算,最终得到载体的导航信息。实例结果表明,该方法可以大幅提升GPS/INS紧组合的精度和稳定可靠性。     

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.     

Adaptive GPS/INS integration for relative navigation

Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.     

Ultra-tight GPS/INS/PL integration: a system concept and performance analysis

The architecture of the ultra-tight GPS/INS/PL integration is the key to its successful performance; the main feature of this architecture is the Doppler feedback to the GPS receiver tracking loops. This Doppler derived from INS, when integrated with the carrier tracking loops, removes the Doppler due to vehicle dynamics from the GPS/PL signal thereby achieving a significant reduction in the carrier tracking loop bandwidth. The bandwidth reduction provides several advantages such as: improvement in anti-jamming performance, and increase in post correlated signal strength which in turn increases the dynamic range and accuracy of measurements. Therefore, any degradation in the derived Doppler estimates will directly affect the tracking loop bandwidth and hence its performance. The quadrature signals from the receiver correlator, I (in-phase) and Q (quadrature), form the measurements, whereas the inertial sensor errors, position, velocity and attitude errors form the states of the complementary Kalman filter. To specify a reliable measurement model of the filter for this type of integrated system, a good understanding of GPS/PL signal characteristics is essential. It is shown in this paper that phase and frequency errors are the variables that relate the measurements and the states in the Kalman filter. The main focus of this paper is to establish the fundamental mathematical relationships that form the measurement model, and to show explicitly how the system error states are related to the GPS/PL signals. The derived mathematical relationships encapsulated in a Kalman filter, are tested by simulation and shown to be valid.

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原子钟辅助GPS定位的研究

铷钟可以用来预测接收机的时钟偏差,通过对时钟偏差估计的改善就可以提高定位的精度,特别是可以改善垂直精度。由于铷钟的漂移非常缓慢,基于自适应低通滤波器,设计了一种导航算法用于铷钟约束的GPS。通过一个例子对所提出的算法进行的验证。     

Modeling and verifying the impact of time delay on INS-aided GNSS PLLs

The timing error between global navigation satellite system (GNSS) and inertial navigation system (INS) processes limits the integration performance in GNSS/INS integrated systems. In a deeply coupled system, this timing error affects not only the integrated navigation solution, but also the GNSS signal tracking. We propose a time-domain model of INS-aided second-order phase-locked loops (PLLs) in consideration of the INS aiding delay, and analyze the effect of INS aiding delay on the tracking errors in details. In addition, an integrated hardware deeply coupled system platform was developed to verify the impact of time delay on INS-aided PLLs. Simulation and field vehicles testing results demonstrate that the tracking error of the INS-aided PLL caused by aiding delay increases with the lengthening of the delay time, the compression of the bandwidth, and the increase in the acceleration. Testing results verify the proposed model.     

自适应卡尔曼滤波在GPS/DR组合导航中的应用

针对在观测条件较差的情况下卡尔曼滤波的鲁棒性较差,本文设计了一种自适应卡尔曼滤波模型。通过实际车载GPS/DR组合导航试验,结果表明该模型在观测质量较差的情况下能够抑制较大的偏差,相对于标准卡尔曼滤波模型,其平面定位精度提高了近一倍,达到2～3m。因此,在观测环境较差的情况下建议采用渐消自适应卡尔曼滤波模式进行组合导航。     

两种滤波方法在SINS／GPS组合导航中的应用与比较

针对SINS／GPS组合导航系统中卡尔曼滤波发散的情况,引入了自适应滤波和H∞滤波,分析了它们各自的特性,最后进行仿真计算,验证了这两种滤波用于SINS／GPS组合导航系统的可行性和有效性,对实际应用中组合导航系统滤波器的设计具有一定的指导意义。     

Adaptive estimation of multiple fading factors in Kalman filter for navigation applications

Kalman filter is the most frequently used algorithm in navigation applications. A conventional Kalman filter (CKF) assumes that the statistics of the system noise are given. As long as the noise characteristics are correctly known, the filter will produce optimal estimates for system states. However, the system noise characteristics are not always exactly known, leading to degradation in filter performance. Under some extreme conditions, incorrectly specified system noise characteristics may even cause instability and divergence. Many researchers have proposed to introduce a fading factor into the Kalman filtering to keep the filter stable. Accordingly various adaptive Kalman filters are developed to estimate the fading factor. However, the estimation of multiple fading factors is a very complicated, and yet still open problem. A new approach to adaptive estimation of multiple fading factors in the Kalman filter for navigation applications is presented in this paper. The proposed approach is based on the assumption that, under optimal estimation conditions, the residuals of the Kalman filter are Gaussian white noises with a zero mean. The fading factors are computed and then applied to the predicted covariance matrix, along with the statistical evaluation of the filter residuals using a Chi-square test. The approach is tested using both GPS standalone and integrated GPS/INS navigation systems. The results show that the proposed approach can significantly improve the filter performance and has the ability to restrain the filtering divergence even when system noise attributes are inaccurate.     

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°。     

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.