Application of nonlinear filtering to navigation system designusing passive sensors

The problem of navigation system design for autonomous aircraft landing is addressed. New nonlinear filter structures are introduced to estimate the position and velocity of an aircraft with respect to a possibly moving landing site, such as a naval vessel, based on measurements provided by airborne vision and inertial sensors. By exploring the geometry of the navigation problem, the navigation filter dynamics are cast in the framework of linear parametrically varying systems (LPVs). Using this set-up, filter performance and stability are studied in an H_∞ setting by resorting to the theory of linear matrix inequalities (LMIs). The design of nonlinear, regionally stable filters to meet adequate H_∞ performance measures is thus converted into that of determining the feasibility of a related set of LMIs and finding a solution to them, if it exists. This is done by using-widely available numerical tools that borrow from convex optimization techniques. The mathematical framework that is required for integrated vision/inertial navigation system design is developed and a design example for an air vehicle landing on an aircraft carrier is detailed     

基于UKF算法的航天器自主导航研究

针对卫星上配备的多种姿态敏感器，进行了导航方案的设计，利用姿态敏感器的测量信息实现轨道参数的估计。通过UKF(Unscented Kalman Filter)算法研究了卫星的自主导航问题，较传统的扩展卡尔曼滤波方法(EKF)简化了计算过程。数值仿真结果验证了该导航算法的优越性。     

Pseudo-linear inertial navigation algorithm

A new method is illustrated for processing the output of a set of triad orthogonal rate gyros and accelerometers to reconstruct vehicle navigation parameters (attitude, velocity, and position). The paper introduces two vectors with dimensions 4 × 1 as velocity and position quaternions. The navigation equations for strapdown systems are nonlinear but after using these parameters, the navigation equations are converted into a pseudo-linear system. The new set of navigation equations has an analytical solution and the state transition matrix is used to solve the linear time-varying differential equations through time series. The navigation parameters are updated using the new formulation for strapdown navigation equations. Finally, the quaternions of velocity and position are converted into the original position and velocity vectors. The combination of the coning motion and a translational oscillatory trajectory is used to evaluate the accuracy of the proposed algorithm. The simulations show significant improvement in the accuracy of the inertial navigation system, which is achieved through the mentioned algorithm.     

Linear parametrically varying systems with brief instabilities: an application to vision/inertial navigation

This paper addresses the problem of nonlinear filter design to estimate the relative position and velocity of an unmanned air vehicle (UAV) with respect to a point on a ship using infrared (IR) vision, inertial, and air data sensors. Sufficient conditions are derived for the existence of a particular type of complementary filters with guaranteed stability and performance in the presence of so-called out-of-frame events that arise when the vision system loses its target temporarily. The results obtained build upon new developments in the theory of linear parametrically varying systems (LPVs) with brief instabilities - also reported in the paper - and provide the proper framework to deal with out-of-frame events. Field tests with a prototype UAV illustrate the performance of the filter and the scope of applications of the new theory developed.     

高空长航时无人机高精度自主定位方法

针对传统基于轨道动力学模型及非线性滤波进行间接敏感地平天文导航定位方法在实际应用中的局限性,提出了一种适用于高空长航时无人机自主导航定位的快速间接敏感地平天文解析定位新方法。分析了星光折射视高度天文量测的机理,导出天文三维定位的解析表达式,详细阐述了利用最小二乘微分校正法代替非线性滤波的天文定位方法,通过直接求解非线性量测方程组即可得到飞行器的精确位置信息,并对这种定位方法的定位精度进行了理论分析。该天文定位方法利用了星光折射间接敏感地平精度高的特点,又不需要飞行器动力学模型也不需要任何先验知识,算法简单可靠,计算量小,而定位精度与传统方法相当。最后,通过计算机仿真,验证了这种天文定位方法的有效性。     

Analysis of upgraded GPS internal Kalman filter

GPS receivers with provisions for inertial navigation system (INS) aiding are designed with internal Kalman filters that model generic INSs and process the basic GPS pseudorange and deltarange (range-rate) data to produce an output of inertially-smoothed, “GPS-derived” position and velocity. These Kalman filters model only the basic nine INS errors (position, velocity, and tilt) and do not model any INS gyro or accelerometer errors. It was found that a significant performance improvement could be achieved under conditions of degraded GPS satellite availability by augmenting this type of filter with the six INS gyro and accelerometer bias errors. It is, therefore, recommended that serious consideration be given to incorporating these states into the design of the GPS internal Kalman filter     

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter(SVSF) is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy.This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters(EKF), nonlinear H1 and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system(SINS) and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width.Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle(UAV) position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.     

水下单信标导航航路规划和最优航路接近方法

相比于传统的长基线和超短基线等导航方式，水下单信标导航具有布放简单的优点，但其导航精度有待进一步提高。为此，提出了单信标导航的航路规划方案，通过泰勒级数展开推导了水平位置精度因子的表达式，并分析了导航点和声信标的相对几何位置关系对导航精度的影响，最终提出了航路规划方案。在此基础上，还提出了自主水下航行器(autonomous underwater vehicle，AUV)从其他位置接近最优航路的方法，包括两部分：一是建立以时延为观测量的滤波模型，利用滤波算法实时获取AUV的位置估计值；二是基于可观测度分析结果对最优航路接近过程的轨迹进行了设计。二者的结合使得AUV高效率且高精度地逼近最优航路。仿真证明了采用所提出的航路规划方案和最优航路接近方法可以提高导航精度，航路规划方案使得AUV位置估计的均方根误差近似为2 m。     

绕月探测器的自主光学导航研究

提出了一种利用高斯-马尔科夫过程和Unscented卡尔曼滤波的绕月探测器自主光学导航算法。针对很难事先确定精确地绕月探测器轨道动力学模型问题,提出利用高斯-马尔科夫过程来近似轨道动力学中的无模型加速度,进而提高了轨道动力学模型的精度;考虑到基于扩展卡尔曼滤波的轨道确定存在的问题,提出利用基于Unscented卡尔曼滤波来估计探测器的位置、速度及无模型加速度,提高了轨道估计精度和保证了算法的稳定性。最后,通过数学仿真验证了自主光学导航算法的有效性。     

相对测量在编队卫星自主定轨中的应用

针对编队卫星自主定轨问题进行了研究,设计了一种完全不依赖于地面站和GPS系统的自主导航方案。利用星间测量信息进行卫星编队相对轨道状态的自主确定;并在利用磁强计进行卫星绝对轨道自主确定的基础上,引入星间测量信息提高绝对定轨精度;设计扩展卡尔曼滤波器进行卫星编队轨道状态估计,数学仿真结果验证了这种导航方案和算法的有效性。     

航天器太阳圆面速度差/太阳视方向组合导航

针对航天器，尤其是深空探测器的自主导航问题，提出了一种新的太阳圆面速度差天文量测信息，该信息利用太阳的较差自转所造成的太阳圆面各点速度不同的特性，是探测器当前位置的函数，其几何本质是一个探测器的位置圆锥。在此基础上，基于太阳圆面速度差和太阳视方向互补的特性，提出了一种太阳圆面速度差/太阳视方向组合导航新方法，将太阳圆面速度差与太阳视方向2种量测量结合起来，实现了量测量之间的优势互补，进一步提高了导航性能。以太阳探测器为例进行了仿真，仿真结果表明相比较于单独用太阳圆面速度差或太阳视方向的导航方法，基于太阳圆面速度差/太阳视方向的组合导航方法精度分别提升了10.2%和16.0%。此外，还分析了光谱仪精度、采样周期和光谱仪数量对导航性能的影响，为深空探测自主导航提供了新的理论与方法。     

火星探测光学自主导航半物理仿真系统设计

针对火星探测中的捕获段和环火段,探讨火星探测光学自主导航半物理仿真系统的设计方案.研究了轨道动力学模型和光学相机导航系统图像处理方法,建立了自主导航算法模块的观测模型和输出显示模块的模型,利用Unscented卡尔曼滤波算法对探测器的位置和速度滤波.仿真结果表明,自主导航精度较高.     

高超声速飞行器组合导航算法

以高超声速飞行器为对象,建立了卫星/惯性组合导航模型。选择组合系统的状态量并建立状态方程,推导位置观测方程和速度观测方程,通过位置、速度交替组合实现卫星/惯性系统的组合。通过仿真验证算法的有效性,仿真结果表明卫星/惯性组合导航可以得到较好的位置和速度信息,适中的姿态精度信息。     

Susan: A New Doppler Radionavigation System

SUSAN is a novel electronic Doppler navigation system which can be used with moving vehicles including aircraft, missiles, satellites, and ships. The system uses a technique of time delays and frequency multiplication to determine the rates of change of the Doppler frequency component present on a signal received from a moving vehicle. From this information the range and velocity of the vehicle can be determined with respect to a transmitting station whose position or ephemeris is known.     

合作环境下基于惯性/里程计/Lidar融合的UGV导航方法

近年来，无人车在巡检、探测等方面的应用愈发广泛,且应用环境愈发复杂。在这些应用中，无人车必须对自身的位姿进行准确估计，以确保作业安全、高效完成。其中，可在复杂环境下适用的自主导航能力是核心关键技术。提出了一种基于惯性/里程计/激光雷达的地面无人车导航方法，区别于传统的激光雷达SLAM方法，该方法根据已知的几何结构特征进行定位，避免了因有效点数量稀少而导致的匹配误差。同时对惯性/里程计/激光雷达的融合算法进行了研究，提高了自主导航系统的鲁棒性和准确性。最后，在Gazebo中搭建了相应的仿真环境，并进行了算法验证。仿真结果表明，该方法能够实现无人车在巡检过程中实时可靠的自主导航，具有较好的工程应用价值。     

航空制导炸弹挠性SINS/GPS组合导航系统研究

针对航空制导炸弹对低成本、高精度、高可靠性导航系统的需求,利用小型化挠性惯性测量组合(IMU)与高精度、高数据更新率的GPS-OEM板组成高精度、低成本的SINS/GPS组合导航系统。SINS和GPS采用速度、位置综合模式,利用Kalman滤波进行数据融合。最后对该组合导航系统进行了数学仿真试验和车载试验。研究表明,此低成本的组合导航系统具有精度高、成本低、可靠性好、体积小等优良指标。     

Impact time control using biased proportional navigation for missiles with varying velocity

A feasible guidance scheme with impact time constraint is proposed for attacking a stationary target by missiles with time-varying velocity. The main idea is to replace the constant velocity with the future mean velocity; therefore, the existing time-to-go estimation algorithm of the proportional navigation guidance law can be improved to adapt to varying conditions. In order to obtain the prediction of the velocity profile, the velocity differential equation to the downrange is derived, which can be numerically integrated between the current downrange and the target position by the on-board computer. Then, a third-order polynomial is introduced to fit the velocity profile in order to calculate the future mean velocity. At the beginning of each guidance loop, the future mean velocity is predicted and the time-to-go information is updated, based on which a novel biased proportional navigation guidance law is established to achieve the impact time constraint. Finally, numerical simulation results verified the effectiveness of the time-to-go estimation algorithm and the proposed law.     

基于矢量跟踪的MEMS-SINS/GNSS深组合导航系统

针对在载体高动态运动时，由Doppler频移剧烈变化而导致的MEMS-SINS/GNSS组合导航系统精度降低的问题，修正了GNSS卫星信号模型，设计了一种深组合导航通道滤波器，并形成了完整的深组合导航方案。基于矢量跟踪结构的深组合导航方案，通过滤波器对载体的导航参数进行估计，将相互独立的各通道信息结合起来，并利用各卫星位置之间的关系，提高了高动态环境中的跟踪能力。仿真结果表明，基于矢量跟踪结构的深组合导航方案可以有效减少高动态对导航精度的影响，提高组合导航系统的输出精度。     

高精度陆用惯导零速修正技术研究

零速修正技术是提高陆用惯导定位精度的一项有效手段。在周期性停车过程中,惯导系统除了将速度误差清零外,还能将由陀螺漂移、平台误差角等因素引起的位置误差进行补偿。本文采用离散线性卡尔曼滤波方法,通过连续三个停车时刻的速度误差构造观测向量,对惯导速度误差、加速度误差和陀螺漂移进行估计,进而补偿位置误差。车载试验表明,该零速修正方法位置精度高,且停车时间短,能够满足地面车快速响应的要求。     

智能航迹推算系统的研究

提出了一种新模糊滤波法用于补偿磁罗盘测量值中的低频干扰。在此基础上 ,进一步提出了一种新的滤波方法 :模糊 -补偿滤波法 ,以消除磁罗盘测量值中各种干扰的影响。仿真表明 :在较长的时间内 ,采用模糊 -补偿滤波法的航迹推算系统的航向估值误差和位置误差均较小。