MSSS-UKF滤波在大方位失准角初始对准中的应用

针对大方位失准角初始对准中非线性滤波的实时性问题,在分析UKF滤波中的Sigma点采样策略基础上,将最小偏度单形采样策略应用于UKF滤波算法的Sigma点采样过程,并将基于最小偏度单形采样的UKF应用于大方位失准角的非线性初始对准过程。仿真结果显示,基于最小偏度单形采样策略(Minimal Skew Simplex Sampling)的UKF对姿态失准角估计的快速性要明显优于基于对称采样策略的UKF滤波算法,且可以满足惯导系统对对准精度和时间的要求。     

基于IUKF的单星对空间目标仅测角跟踪算法

针对普通UKF(无迹卡尔曼滤波)测量更新方法的非线性近似精度相对较低,导致目标跟踪滤波精度和稳定性较低的问题,在单星对空间目标的天基仅测角跟踪滤波过程中,提出一种基于迭代测量更新方法的IUKF(迭代UKF)算法。通过在测量更新过程中提高非线性系统状态估计的近似精度,进而提高目标跟踪滤波精度,并引入具有全局收敛性的阻尼Gauss-Newton(高斯-牛顿)法来改进IUKF的数值稳定性。理论分析与实验结果表明,该方法不仅避免了求解雅可比矩阵和Hessian矩阵,而且具有较高的滤波精度和数值稳定性。     

基于改进平方根无迹卡尔曼滤波方法的涡扇发动机气路状态监控

针对涡扇发动机气路状态监控存在模型未知或不准确导致滤波效果下降甚至发散的问题,研究了一种融入高斯过程回归（GPR）的改进平方根无迹卡尔曼滤波（UKF）方法.该方法利用GPR对训练数据进行学习,建立发动机气路部件状态监控的GPR模型,替代UKF方法中的非线性系统模型;采用超球体单形采样和平方根滤波方法来提高滤波的计算效率和数值稳定性.仿真结果表明:训练的GPR模型解决了UKF方法对发动机原系统模型和噪声协方差矩阵依赖性的问题;与扩展卡尔曼滤波（EKF）和平方根UKF方法相比较,改进平方根UKF方法精度更高,对健康参数的估计精度达到99.9%,实现了对涡扇发动机单个和多个气路部件健系参数的有效跟踪.      

大失准角下MIMU空中快速对准技术

 为了提高微小型无人机空中的反应速度和作业精度，提出将基于模型误差预测的扩展卡尔曼滤波(MEP-EKF)方法应用在大失准角下微惯性测量单元(MIMU)的空中对准中，通过不同机动飞行策略的仿真结果，证实MEP-EKF算法不仅能够实时估计出系统的模型误差，而且将其与扩展卡尔曼滤波(EKF)和Unscented卡尔曼滤波(UKF)方法进行了仿真比较，结果表明MEP-EKF算法在方位误差角的估计上，取得了比EKF和UKF精度高的仿真结果，使得方位失准角由30°快速下降到1°左右，而且MEP-EKF所需时间仅是UKF的17%。     

GHQF精度分析及其在组合导航中的应用

针对非线性滤波算法在组合导航系统中的应用问题,利用泰勒级数展开对无味卡尔曼滤波(UKF)、容积卡尔曼滤波(CKF)和高斯厄米特积分滤波(GHQF)三种非线性高斯滤波算法的性能进行了比较分析;基于泰勒展开的精度分析表明,UKF和CKF从四阶项开始出现截断误差,而GHQF可以逼近任意阶精度的非线性系统的后验均值;以CNS/SAR/SINS非线性组合导航系统为应用背景,对三种滤波算法的精度进行了仿真验证。数学仿真结果表明,与UKF和CKF相比,GHQF具有更高的滤波估计精度。     

基于联邦UKF的多机协同无源定位

为提高多机协同无源定位的精度和速度,同时满足应用性强的要求,提出了基于UKF(Unscented Kalman Filter)联邦滤波算法的纯方位无源定位技术。核心思想是利用联邦滤波算法去解决各子滤波器滤波结果的全局最优融合问题;利用UKF算法去消除非线性状态方程或量测方程中的高斯噪声,从而实现多机协同对目标辐射源的高精度无源定位。仿真结果表明,该算法可行性强,定位精度高、速度快,具有很高的应用价值。     

强跟踪滤波在高超声速滑翔目标跟踪中的应用

高超声速滑翔目标(HGT)机动模式复杂多样、轨迹形态灵活多变,增加了跟踪模型建模的不确定性,导致目标跟踪的精度低。为了提高跟踪精度,提出了一种基于强跟踪滤波的高超声速滑翔目标跟踪方法。首先,在地基雷达坐标系下建立目标运动模型和量测模型,利用维纳随机过程来表征运动模型中未知项的变化特性。其次,采用强跟踪无迹卡尔曼滤波(UKF)算法对目标运动状态进行估计,提高模型不确定性存在时滤波器的状态跟踪能力。最后,利用目标常用的基于标准轨迹的制导方法生成了一条可行飞行轨迹。仿真结果表明,该方法的跟踪精度高,强跟踪滤波能够有效降低模型不确定性存在时的状态估计误差。     

一种基于模型误差预测的UKF方法

UnscentedKalman滤波器（UKF）对本质非线性系统具有估计精度高、收敛速度快和容易实现等优点,但是对系统的模型误差比较敏感。针对这一问题,提出了一种基于模型误差预测的UKF方法,称为PUKF（PredictiveUnscentedKalmanFilter）。它利用非线性预测滤波器（NPF）的模型误差预测过程,能够对不准确的系统模型进行实时修正,弥补了UKF方法的不足。仿真结果表明,相对于原始的UKF方法,新方法从滤波精度、收敛速度和收敛的稳定性等几个方面,显著提高了非线性滤波的性能。PUKF可适用于模型不确定、非线性较强系统的滤波。     

七阶正交容积卡尔曼滤波算法

在高斯滤波框架下,阶次越高,近似精度越高。为提高滤波精度,通过提高阶次,提出了七阶正交容积卡尔曼滤波(CQKF)算法。在传统CQKF算法的基础上,该算法扩展了线性积分的近似阶次,提出了七阶球面积分的确定性采样方法;进而扩展了球-半径准则,提高了滤波估计精度。飞行器目标跟踪的仿真实验证明了该算法的有效性,证明了七阶CQKF比五阶CQKF、三阶容积卡尔曼滤波器(CKF)和无迹卡尔曼滤波器(UKF)有更高的滤波精度。     

基于联邦UKF的多源测控数据融合处理方法

为确保载人航天上升段测控实时外弹道跟踪的精度和可靠性,针对目前测控资源冗余、测量信息利用率低的缺点,结合航天测控的特点提出基于联邦UKF(无迹卡尔曼滤波)的测控数据融合处理方法,并对需要解决的关键技术问题进行了分析。仿真结果表明:采用基于联邦UKF的数据融合方法,外弹道实时跟踪精度提高约30%,解决了弹道优选方法存在阶跳的问题,算法的收敛速度可以满足实时导引的要求。     

Tracking a ballistic target: comparison of several nonlinear filters

This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer-Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and standard deviation; consistency test) of the following nonlinear filters is compared: the extended Kalman filter (EKF), the. statistical linearization, the particle filtering, and the unscented Kalman filter (UKF). The simulation results favor the EKF; it combines the statistical efficiency with a modest computational load. This conclusion is valid when the target ballistic coefficient is a priori known.     

Unscented Kalman滤波用于再入飞行器跟踪

本文引入Unscented Kalman滤波用于再入飞行器跟踪。Unscented Kalman滤波通过设计少量的σ点，并计算这些σ点经由非线性函数的传播，从而获得滤波值基于非线性状态方程的更新。在应用中，由于Un-scented Kalman滤波无需象广义Kalman滤波那样求动力学系统函数和观测系统函数关于状态向量的导数。给计算带来了极大的方便。     

Unscented Kalman滤波在空间飞行器被动测距中的应用

介绍了Unscented Kalman滤波及其空间飞行器被动测距中的应用。Unscented Kalman滤波通过设计少量的sigma(抽样)点,计算这些点经过非线性函数的传播,获得滤波值基于非线性方程的更新。Unscented Kalman滤波避免了广义Kalman滤波等线性化方法的缺点,并提高了滤波精度。     

Design of a practical tracking algorithm with radar measurements

The extended Kalman filter (EKF) has been widely used as a nonlinear filtering method for radar tracking problems. However, it has been found that if cross-range measurement errors of the target position are large, the performance of the conventional EKF degrades considerably due to nonnegligible nonlinear effects. A new filtering algorithm for improving the tracking performance with radar measurements is developed based on the fact that correct evaluation of the measurement error covariance is possible in the Cartesian coordinate system. The proposed algorithm may be viewed as a modification of the EKF in which the variance of the range measurement errors is evaluated in an adaptive manner. The filter structure facilitates the incorporation of the sequential measurement processing scheme, and this makes the resulting algorithm favorable to both estimation accuracy and computational efficiency. Computer simulation results show that the proposed method offers superior performance in comparison to previous methods. Moreover, our developed algorithm provides some useful insight into the radar tracking problem     

Streamlining measurement iteration for EKF target tracking

The estimation problem is defined, and a review of how the linear estimation approach of Kalman filtering is extrapolated to form an extended Kalman filter (EKF), applicable for state estimation in nonlinear systems is presented. A mechanization of an EKF variation known as an iterated EKF, offering improved tracking performance, is treated. A streamlined version of an iterated EKF that has a lesser computational burden (fewer operations per cycle or time step) than prior formulations is offered. A nonlinear filtering application example, to be used as a testbed for this new approach, is described, and the detailed modeling considerations as needed for exoatmospheric random-variable radar target tracking are discussed. The performance of the streamlined mechanization is illustrated in this radar target tracking example, and comparisons are made with the performance of an EKF without measurement iteration     

一种基于三维到达角的无偏伪线性卡尔曼滤波

为了解决大场景下基于三维到达角的目标跟踪问题，提出了一种具有无偏性的伪线性卡尔曼滤波。首先，基于三维到达角信息对目标运动模型与量测模型进行建模；之后，对量测模型进行了伪线性化处理，得到了线性形式的目标量测模型。为了解决伪线性卡尔曼滤波存在的有偏性问题，提出了一种结合EKF（extend Kalman filter）的三维伪线性无偏卡尔曼滤波。仿真实验表明，该模型能够对非机动目标与机动目标有效跟踪，对于百公里级别的目标，当角测量误差从0.1°变化到0.5°，算法在仿真时间结束时均能将绝对位置误差降低至10 km以内，且算法的运行速度与EKF为同一个量级，同时兼顾了抗干扰能力、定位跟踪精度、运行效率的要求，能够为大场景下的目标跟踪提供有效方法。     

无源定位跟踪滤波算法研究及仿真分析

首先介绍了几种无源定位跟踪滤波算法原理,包括扩展卡尔曼滤波（EKF）,无迹卡尔曼滤波器（EKF）,交互多模型滤波器（IMM）;然后通过建立几种不同模型来对每一种滤波算法进行仿真,依据仿真图形和误差结果对滤波算法进行分析,从而实现不同滤波模型根据目标运动状态进行监视和切换,这对无源定位跟踪算法精度的提高和实际应用有很大的...     

捷联惯导系统大失准角下的初始对准研究

针对捷联惯导系统初始对准过程中的大失准角情况,建立了基于欧拉平台误差角概念的捷联惯导系统(SINS)非线性误差模型,对于具有加性噪声的动态方程,当状态方程为非线性而观测方程为线性时,将一种简化的UKF滤波方法运用到捷联惯导系统初始对准中,并在静基座下对捷联惯导系统大失准角初始对准进行了仿真。仿真结果表明,随着失准角的增大,简化的UKF比EKF估计精度更高,是一种在进行捷联惯导系统大失准角条件下的初始对准时实用方法。     

Two-step optimal estimator for three dimensional target tracking

This study presents an adaptation of a novel estimation methodology to the general nonlinear three-dimensional problem of tracking a maneuvering target. The two-step optimal estimator (TSE) suggests an attractive alternative to the standard extended Kalman filter (EKF). A superior performance is accomplished by dividing the estimation problem into two steps: a linear first step and a nonlinear second step. The target tracking performance of the TSE is shown to be better than an EKF implemented in either inertial or modified spherical coordinates. In the passive case, where bearing/elevation angles only are measured, the TSE yields excellent range and target acceleration estimates. In the active case, where range measurement is available as well, a homing missile employing closed-loop optimal guidance based on the TSE state estimates obtains smaller miss distances than with either versions of the EKF.     

Tracking an incoming ballistic missile using an extended interval Kalman filter

The important tracking problem by radar of an incoming ballistic missile system, which contains uncertainty in modeling and noise in both dynamics and measurements, is studied. The classical extended Kalman filter (EKF) is no longer applicable to such an uncertain system, and so a new extended interval Kalman filter (EIKF) is developed for tracking the missile system. Computer simulation is presented to show the effectiveness of the EIKF algorithm for this uncertain and nonlinear ballistic missile tracking problem.