Velocity Estimation from Position Outputs of a Three-Dimensional Radar

Kalman filtering equations to obtain estimates of velocity from radar position information are defined. In a track-while-scan operation, a three-dimensional radar sensor measures range, bearing, and elevation (r, ?, ?) of an airborne target at uniform sampling intervals of time T. The noisy position measurements are converted to x, y, z coordinates and put through a Kalman filter to obtain x, y, z velocity components. The filtering equations together with steady-state error estimates are given.     

Optimum Steady State Position, Velocity, and Acceleration Estimation Using Noisy Sampled Position Data

A one-dimensional tracking filter based on the Kalman filtering techniques for tracking of a dynamic target such as an aircraft is discussed. The target is assumed to be moving with constant acceleration and is acted upon by a plant noise which perturbs its constant acceleration motion. The plant noise accounts for maneuvers and/or other random factors. Analytical results for estimating optimum steady state position, velocity, and acceleration of the target are obtained.     

Improved Carrier Tracking by Smoothing Estimators

Smoothing as a way to improve the carrier phase estimation is proposed and analyzed. The performance of first-and second-order Kalman optimum smoothers are investigated. This perfomance is evaluated in terms of steady-state covariance error computation, dynamic tracking, and noise response. It is shown that with practical amounts of memory, a second-order smoother can have a position error due to an acceleration or jerk step input less than any prescribed maximum. As an example of importance to the NASA deep space network (DSN), a second-order smoother can be used to track the Voyager spacecraft at Uranus and Neptune encounters with significantly better performance than a second-order phaselocked loop.     

Analytical Results for the x, y Kalman Tracking Filter

A two-dimensional x, y Kalman tracking filter is analyzed for a track-while-scan (TWS) operation when the radar sensor measures range and bearing (r, ?) at uniform sampling intervals T seconds apart. This development explicitly considers the coupling between the quantities measured by the sensor (r, ?) and the Cartesian x, y coordinate system selected for the tracking operation. The steadystate components of the gain and error covariance matrixes are analytically determined under the assumption of a white noise maneuver acceleration model in two dimensions. These results are verified by computer calculation of the Kalman filter matrix equations.     

基于火箭橇的北斗动态定位精度鉴定方法

介绍了几种现有的以GPS/北斗为主要代表的卫星导航系统的动态定位精度 鉴定方法,分析了这几种方法在模拟实际使用环境、鉴定精度等方面的优缺点,提出了 一种基于火箭橇试验平台的北斗导航定位系统动态定位精度鉴定方法。该方法能够很好 地模拟北斗导航定位系统实际工作环境（速度、加速度、振动和天线角度等）,解决高 动态、高精度北斗导航定位系统动态定位精度鉴定问题,并对该方法的原理和实施方法 作了详细的论述,最后分析了该方法的不确定度及误差,并提出了改进措施。     

气动FA补偿变结构Bang-Bang位置伺服算法

 首先提出了一种基于两位两通高速开关阀的无杆气缸脉宽调制（PWM）控制方案,为气缸提供了中位截止机能。然后针对Bang-Bang控制算法中存在的超调和振荡现象,提出了摩擦力和加速度（FA）补偿的变结构Bang-Bang算法。该算法综合位置和速度误差的影响构造了加速度方向切换评价函数,考虑了摩擦力对控制量设定的影响,分别依据阶跃函数、线性函数和反正切函数对活塞加速度值进行动态设定,实现了运动过程中对活塞摩擦力的补偿和加速度的调整。最后,利用该算法进行了无杆气缸的带载伺服定位试验,对3种加速度设定函数的控制效果做了比较。结果证明FA补偿变结构算法在提高气缸定位精度方面有显著效果。     

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.     

基于对偶四元数的INS/GPS组合导航算法研究

在基于对偶四元数的捷联惯导解算方法的基础上,推导了以惯性系作为导航系的惯导误差方程,在此基础上设计了卡尔曼滤波组合导航算法。通过激光惯导跑车采集数据,进行了仿真分析,试验结果表明,该组合导航算法能有效的消除惯导累积的速度误差和位置误差,相比于目前广泛应用的INS/GPS组合导航算法,本文描述了INS/GPS组合导航的另一种实现方式,获得了相当的精度,具有一定的工程应用价值。     

视觉辅助惯性定位定姿技术研究

在利用视觉系统辅助捷联惯性导航系统( SINS)进行定位定姿时,由于两者输出的位置表示方法不同,在信息融合时会存在匹配问题。以相对地理坐标系作为定位定姿系统的导航坐标系,重新对SINS进行了力学编排,分析了其误差传递特性,建立了相对地理坐标系下的状态方程模型,并利用Kalman滤波器实现了视觉辅助惯性定位定姿算法。仿真结果表明,方法避免了信息融合时位置匹配问题,同时降低了系统的计算量,满足了定位定姿系统的精度要求。     

Robust extended Kalman filter with input estimation for maneuver tracking

This study investigates the problem of tracking a satellite performing unknown continuous maneuvers. A new method is proposed for estimating both the state and maneuver acceleration of the satellite. The estimation of the maneuver acceleration is obtained by the combination of an unbiased minimum-variance input and state estimation method and a low-pass filter. Then a threshold-based maneuver detection approach is developed to determinate the start and end time of the unknown maneuvers. During the maneuvering period, the estimation error of the maneuver acceleration is modeled as the sum of a fluctuation error and a sudden change error. A robust extended Kalman filter is developed for dealing with the acceleration estimate error and providing state estimation. Simulation results show that, compared with the Unbiased Minimum-variance Input and State Estimation (UMISE) method, the proposed method has the same position estimation accuracy, and the velocity estimation error is reduced by about 5 times during the maneuver period. Besides, the acceleration detection and estimation accuracy of the proposed method is much higher than that of the UMISE method.     

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。     

一种提高捷联惯性/里程计组合导航系统定位精度的后处理方法

测绘用捷联惯性/里程计组合导航系统多采用离线后处理技术.此类系统利用里程计位移微分获得的速度作为观测量,采用速度匹配,并通过待测路径中预置的Mark点校正航位推算的位置信息.本文建立了基于速度匹配的16维Kalman滤波模型,对全程采样数据进行正反向导航和滤波处理,以估计惯性器件和里程计的误差;在补偿相关误差后,再次进...     

Analytical solution for continuous-time Kalman tracking filterswith colored measurement noise in frequency domain

New expressions are given for analytical solutions to the steady-state Kalman gains of the two-state exponentially correlated velocity (ECV) and the three-state exponentially correlated acceleration (ECA) tracking filters with position measurements by using spectral factorization method. The measurement colored noise model is characterized by a correlation time 1/λ. The vehicle oscillations such as wind-induced-bending is also considered in the modeling of the system which leads to the most generalized state transition matrix     

基于最大一致容积Kalman滤波器的多机器人协同定位CSCD

机器人位置信息是多机器人系统执行任务的前提,单个机器人因传感器载荷和作用范围的限制,难以完成复杂环境中的定位任务,多个机器人通过协作可实现大范围下的位置确定。将配置多个传感器的同构机器人群替换为配置单个或少量传感器的异构机器人可降低硬件成本,并且通过设计协同算法,不会降低定位精度。提出了一种将容积Kalman滤波器与最大一致思想融合后的新型滤波算法,并将该算法应用于麦克纳姆轮机器人系统。通过仿真和实物验证了最大一致容积Kalman滤波器的协同定位效果。     

基于边缘采样的简化高阶CKF在非线性快速传递对准中的应用

针对舰载机惯导系统非线性传递对准问题中误差模型不完善的问题，同时考虑了挠曲运动和动态杆臂的影响，提出了一种新的适用于大方位失准角情形下的挠曲变形和杆臂效应加速度一体化误差模型。采用高阶容积卡尔曼滤波（HCKF）算法对状态进行滤波估计，考虑到HCKF具有较大的计算量，分析了传递对准模型的状态方程与量测方程结构，设计了一种基于边缘采样的简化高阶容积卡尔曼滤波（M-RHCKF）算法，其在时间更新中使用边缘采样算法，在量测更新过程中使用简化量测更新过程，并给出了该算法的证明过程。采用"速度+姿态"组合匹配方式，对提出的误差模型进行仿真实验。结果表明，该模型可以满足对准精度和对准时间的要求，相比于未考虑动态杆臂的传递对准模型具有更高的对准精度。     

基于速度积分匹配的旋转调制惯导系统动基座对准方法

卫星导航信息辅助动基座对准过程中,速度噪声会影响对准精度和快速性,制约了旋转调制惯导角秒级高精度快速对准的实现.针对这一问题,提出了一种基于旋转调制惯导速度积分匹配的快速动基座对准方法,通过建立旋转调制惯导动基座对准误差方程和卡尔曼滤波观测模型,以消除动基座对准对载机特殊运动的要求.最后,在实验室静态环境和车载环境下,分别开展了速度积分匹配和速度十位置组合导航动基座对准仿真实验.仿真结果表明,提出的速度积分匹配方法具有误差估计量收敛速度快的特点,在对准精度不降低的情况下相对组合导航匹配方式能有效缩短动基座对准时间,并能基于旋转调制惯导取消动基座对准对载机的机动需求.     

机动目标“当前”统计模型与自适应跟踪算法

本文提出机动目标“当前”统计模型的概念并建议用修正的瑞利-马尔科夫过程描述目标随机加速机动的统计特性。文中指出了在机动目标运动模型中状态（机动加速度）估值与状态噪声之间的内在联系。在此基础上提出了具有机动加速度均值及方差自适应的卡尔曼滤波算法。对一维和三维的情形进行了计算机模拟。计算结果表明,在仅对目标位置进行观测的情况下,这类自适应估值算法无论对高度机动或无机动的目标均可绘出较好的位置、速度及加速度估值。     

基于IMMKF算法的ADS-B监视应用目标跟踪

目标跟踪是机载广播式自动相关监视（ADS-B）应用的基础功能,对提升航空器周边的弱机动民航飞机目标跟踪性能具有重要意义。提出一种基于交互式多模型卡尔曼滤波（IMMKF）算法的ADS-B 监视应用目标跟踪方法。首先,针对弱机动背景下的民航飞机的飞行特点,建立包含匀速模型和标准协同转弯模型的运动模型集,并对模型进行线性化近似;然后,将模型预测和ADS-B 状态矢量量测数据作为IMMKF 算法中多个并行卡尔曼滤波器的输入,进行并行滤波;最后,计算得到目标状态矢量的估计和模型近似概率,并作为下一次迭代的输入。结果表明：相比于基于匀速模型的卡尔曼滤波目标跟踪方法,IMMKF 方法的位置跟踪误差降低了59%,速度跟踪误差降低了77%,显著提升了状态估计性能,具备较高的跟踪精度、稳健性与计算效率,在ADS-B 监视应用中具有实际应用价值与借鉴意义。     

单点弹道精度与轨道半长轴远地点高度计算精度的映射关系

在仅使用单点位置、速度信息计算轨道的奈件下,针对轨道半长轴、远地点高度的精度问题,在轨道面内,应用活力公式和二体运动学理论推导得出了轨道计算精度与弹道测量精度间映射关系的解析表达式,并采用数值分析方法给出了不同的位置、速度误差与半长轴、远地点高度最大误差之间的数值关系.仿真结果表明,对于位置误差和速度误差大小分别为100 m和1 m/s的算例,半长轴最大误差和远地点高度最大误差分别约为2 km和4 km.基于此方法,可以将弹道误差传递至轨道参数误差,进一步分析故障误判和漏判概率;也可根据轨道参数精度要求反算弹道测量精度要求,以作为地面测量系统建设的技术依据.     

皮卫星姿态估计及粗太阳敏感器标定算法研究

粗太阳敏感器是一种由多个光电池片配置组成的模拟式太阳敏感器,它结构简单、资源要求少,在皮卫星平台应用较为广泛。光电池光伏输出特性是粗太阳敏感器测量的基础,使用余弦关系近似等效会引入很大的误差。通过实际测试和深入分析,建立了更高精度的粗太阳敏感器输出特性等效数值模型,并以此为基础,推导了卫星姿态估计及粗太阳敏感器标定的联合在轨实时算法。算法采用多级耦合结构,由1个扩展卡尔曼滤波器(EKF)和6个卡尔曼滤波器组成,同时估计卫星姿态、卫星角速度以及卫星6个面共30个标定参数。仿真表明,和常规的EKF姿态估计算法相比,联合算法的运算量只增加了一半,而估计精度却提高了一个量级。