基于变分贝叶斯的星载雷达非线性滤波

星载雷达由于其探测范围广、距离远、全天候等优点,在预警防御系统中占有十分重要的地位。然而,由于观测平台的高速运动以及摄动干扰、传感器观测非线性等问题,使得星载雷达目标高精度跟踪带来严峻挑战。针对星载雷达非线性状态估计问题,采用一种基于变分贝叶斯的非线性滤波方法,该方法通过将非线性状态估计问题转化为优化问题,通过迭代优化获得了闭环解析解。此外,针对坐标变换中俯仰角量测缺失问题,提出了一种基于先验目标高度的俯仰角估计方法。通过数值仿真,验证了所提方法较传统非线性滤波方法,如扩展卡尔曼滤波、不敏卡尔曼滤波、转换量测卡尔曼滤波,具有更好的估计精度。     

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。     

Target tracking in maneuver-centered coordinates

Aircraft targets normally maneuver on circular paths, which has led to tracking filters based on circular turns. A coordinate system to track circular maneuvers with a simple Kalman filter is introduced. This system is a polar coordinate system located at the center of the maneuver. It leads to a tracking filter with range, angle, and angular velocity in the state vector. Simulation results are presented, showing that the algorithm displays improved performance over methods based on constant x-y acceleration when tracking circular turns     

Unscented粒子滤波在静基座捷联惯导系统大方位失准角初始对准中的应用研究

 静基座大方位失准角的捷联惯导系统误差方程是非线性的,Unscented粒子滤波从非线性系统状态向量概率分布出发,结合Unscented卡尔曼滤波和粒子滤波的特点,无需对非线性系统模型进行处理而能达到较高的滤波精度。本文对Unscented粒子滤波进行了研究,并结合重采样算法,运用于捷联惯导系统静基座大方位失准角初始对准中,计算结果验证了该方法的有效性与优越性。     

卡尔曼滤波在自旋卫星章动控制中的应用

细长体结构卫星的自旋运动是不稳定的,章动有发散趋势,须进行实时的章动监视,且章动角的测量值包含有误差.通过引入一个离散控制过程的系统,介绍了卡尔曼滤波器的基本原理,建立了自旋卫星自旋运动简化的动力学模型,给出了运动方程和观测方程,详细地推导了适用于自旋卫星章动控制的卡尔曼滤波处理过程,利用该模型对某自旋卫星章动进行仿真计算,并对姿态章动联合控制和应急章动控制情况下的章动控制应用情况进行分析,得出结论：基于卡尔曼滤波的卫星章动角计算的方法正确、结果准确.     

一种基于非线性跟踪-微分器的角加速度估计方法

针对仅能获取角度信息的角加速度估计问题,基于卡尔曼滤波和非线性跟踪-微分器设计了一种角加速度估计算法。该算法利用卡尔曼滤波得到角速度的估计值,并以此为基础采用非线性跟踪-微分器对角加速度进行估计,通过对卡尔曼滤波与跟踪-微分器角加速度估计进行合理融合获得最终的角加速度输出。仿真结果表明,所设计的估计方法能满足视线角加速度的估计精度要求,具有一定的工程应用价值。     

捷联制导旋转弹制导信息提取研究CSCD

以捷联式半主动激光导引头为研究对象,研究其应用在旋转弹上制导信息的提取方法。根据坐标转换关系得到旋转弹惯性系视线角解耦模型,由于导引头和速率陀螺仪具有测量误差特性,直接解耦得到的制导信息会产生较大的误差。基于视线角解耦模型的非线性,采用扩展卡尔曼滤波(EKF)的方法对测量信息进行滤波处理,估计出目标的位置,从而得到捷联式半主动激光导引旋转弹的制导信息。将扩展卡尔曼滤波方法与α-β滤波方法进行对比分析,得到扩展卡尔曼滤波方法对捷联式半主动激光导引旋转弹制导信息的估计精度更高,收敛更快。     

飞机迎角在垂直阵风干扰中的卡尔曼滤波估计

在阵风条件下,为了抑制飞机的附加载荷并改善乘坐品质,需要得到飞机的迎角状态。以某大型运输机为例,通过卡尔曼滤波方法,将飞机法向过载和俯仰角速度综合进飞机的操纵输入来估计垂直阵风条件下的飞机迎角,并进行了仿真。结果表明,在垂直阵风干扰条件下,采用卡尔曼滤波方法能获得迎角状态的良好估计。     

基于UKF的大失准角船舶行进间对准技术研究

由于标准卡尔曼滤波只适用于线性系统,通常在SINS/GPS组合导航初始对准过程中,先通过基于惯性系的粗对准方法,将失准角转化为小量,然后再进行卡尔曼滤波精对准。由于杆臂效应,使用的基准信息存在一定误差,导致初始对准精度降低。因此,首先设计UKF的大失准角初始对准算法;其次将基准信息杆臂在UKF方程中建模,对杆臂误差进行补偿;最后通过仿真验证算法的可行性,并利用海试实验数据对UKF算法与传统动基座算法进行对比,实验结果表明该方法具有明显优势。     

Analytical Steady State Solution for a Kalman Tracking Filter

Analytical expressions are given for the steady state solution to a Kalman tracking filter used in a track-while-scan radar system. The radar sensor measures range and range rate, and both these measurements are utilized in the filter. The solution for range measurements only is obtained as a special case. Graphs are also given which show how the solution depends on different parameters.     

Coordinate Conversion and Tracking for Very Long Range Radars

The problem of tracking with very long range radars is studied in this paper. First, the measurement conversion from a radar's r-u-v coordinate system to the Cartesian coordinate system is discussed. Although the nonlinearity of this coordinate transformation appears insignificant based on the evaluation of the bias of the converted measurements, it is shown that this nonlinearity can cause significant covariance inconsistency in the conventionally converted measurements (CM1). Since data association depends critically on filter consistency, this issue is very important. Following this, it is shown that a suitably corrected conversion (CM2) eliminates the inconsistency. Then, initialized with the converted measurements (using CM2), four Cartesian filters are evaluated. It is shown that, among these filters, the converted measurement Kalman filter with second order Taylor expansion (CM2KF) is the only one that is consistent for very long range tracking scenarios. Another two approaches, the range-direction-cosine extended Kalman filter (ruvEKF) and the unscented Kalman filter (UKF) are also evaluated and shown to suffer from consistency problems. However, the CM2KF has the disadvantage of reduced accuracy in the range direction. To fix this problem, a consistency-based modification for the standard extended Kalman filter (E1KF) is proposed. This leads to a new filtering approach, designated as measurement covariance adaptive extended Kalman filter (MCAEKF). For very long range tracking scenarios, the MCAEKF is shown to produce consistent filtering results and be able to avoid the loss of accuracy in the range direction. It is also shown that the MCAEKF meets the posterior Carmer-Rao lower bound for the scenarios considered.     

A Tracking Filter in Spherical Coordinates Enhanced by De-noising of Converted Doppler Measurements

Tracking problem in spherical coordinates with range rate (Doppler) measurements, which would have errors correlated to the range measurement errors, is investigated in this paper. The converted Doppler measurements, constructed by the product of the Doppler measurements and range measurements, are used to replace the original Doppler measurements. A de-noising method based on an unbiased Kalman filter (KF) is proposed to reduce the converted Doppler measurement errors before updating the target states for the constant velocity (CV) model. The states from the de-noising filter are then combined with the Cartesian states from the converted measurement Kalman filter (CMKF) to produce final state estimates. The nonlinearity of the de-noising filter states are handled by expanding them around the Cartesian states from the CMKF in a Taylor series up to the second order term. In the mean time, the correlation between the two filters caused by the common range measurements is handled by a minimum mean squared error (MMSE) estimation-based method. These result in a new tracking filter, CMDN-EKF2. Monte Carlo simulations demonstrate that the proposed tracking filter can provide efficient and robust performance with a modest computational cost.     

Efficient Approximation of Kalman Filter for Target Tracking

A Kalman filter in the Cartesian coordinates is described for a maneuvering target when the radar sensor measures range, bearing, and elevation angles in the polar coordinates at high data rates. An approximate gain computation algorithm is developed to determine the filter gains for on-line microprocessor implementation. In this approach, gains are computed for three uncoupled filters and multiplied by a Jacobian transformation determined from the measured target position and orientation. The algorithm is compared with the extended Kalman filter for a typical target trajectory in a naval gun fire control system. The filter gains and the tracking errors for the proposed algorithm are nearly identical to the extended Kalman filter, while the computation requirements are reduced by a factor of four.     

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。     

SINS/BDS紧组合系统中自适应 两阶段扩展卡尔曼滤波

在实际应用中，以伪距/伪距率为观测量的SINS/BDS紧组合导航系统，存在量测噪声的统计特性与实际不相符的情况，传统扩展卡尔曼滤波（EKF）方法无法有效解决这一问题，从而引起滤波误差增大。提出了一种SINS/BDS紧组合导航系统的GDOP估算及在线估计量测噪声的自适应两阶段EKF（ATEKF）方法，该方法使用经过紧组合修正后的SINS输出的位置，并结合星历数据中提供的卫星位置求解GDOP。在此基础上，利用GDOP值以及新息，实现了紧组合导航系统的量测噪声方差阵（Rk）的在线实时估计，从而达到自适应滤波的效果，改善导航精度。     

捷联激光制导的视线信号提取方法

提出了一种弹目视线角信号的提取方法,该方法采用UKF解耦,针对捷联激光制导炸弹需要攻击移动目标却不能测量弹目相对距离和速度的情况,建立了弹目相对运动模型,解耦过程不需要相对距离信息,能够更方便地应用于工程实践.经仿真验证,此方法可以较好地估计出弹目视线角以及角速率信息,能够满足捷联激光制导炸弹工程应用的需要.     

A track filter for reentry objects with uncertain drag

A filter was developed for maintaining track on ballistic missiles whose drag profiles are unknown or deviate significantly from prior predictions. The filter employs an innovative form of a seven-state Kalman filter in which object drag is included as a state to be estimated. Using measurements of range, azimuth, and elevation, the filter can track endo- and exo-atmospheric targets on a wide variety of trajectories without requiring a priori tuning to account for variations in reentry angle, drag history, measurement signal-to-noise ratio, etc. The filter was designed to be implemented at the millimeter wave (MMW) radar (a high-range-resolution, narrow beamwidth, Ka-band radar) located at Kwajalein Missile Range (KMR) in the Marshall Islands. Extensive testing and comparisons using a high fidelity simulation showed the new filter to be robust to a wide variety of trajectories and substantially better than track filters presently used at KMR. The filter was coded to run efficiently in real time, installed at the MMW radar, and successfully used to track an intercontinental ballistic missile (ICBM) with varying drag characteristics through exo-atmospheric and reentry phases. The filter yielded a more accurate and responsive track than possible with the previously used filter on a similar trajectory     

基于模糊自适应卡尔曼滤波的大气数据辅助姿态算法

 针对中低精度航姿参考系统(AHRS)在机体机动时不能利用加速度计修正水平姿态,以及噪声统计特性随实际工作情况变化的问题,提出了一种基于模糊自适应卡尔曼滤波的大气数据辅助姿态解算的方法。首先,考虑大气数据系统和航姿参考系统的优势,利用真空速、攻角和侧滑角等大气数据信息对非重力加速度进行补偿,以辅助水平姿态解算;其次,基于模糊自适应卡尔曼滤波原理,对观测模型的参数进行估计和修正,以实现水平姿态的最优估计;最后,选取某型飞机的试飞数据进行仿真验证。仿真结果表明,该方法可使飞机的水平姿态估计精度达到1.3°,且在偏差较大时有明显的纠偏作用。因此,相对于无机动加速度补偿和常规卡尔曼滤波来说,该方法能够更好地进行姿态估计,具有一定的实用价值。     

A three-state Kalman tracker using position and rate measurements

A three-state Kalman tracker is described for tracking a moving target, such as an aircraft, making use of the position and rate measurements obtained by a track-white-scan radar sensor which employs pulsed Doppler processing, such as the moving target detector providing unambiguous Doppler data. The steady-state filter parameters have been analytically obtained under the assumption of white noise maneuver capability. The numerical computations of these parameters are in excellent agreement with those obtained from the recursive Kalman filter matrix equations. The solution for the case when only the range measurements are available is obtained as a special case of this model. Graphs of normalized covariances and gains are presented to illustrate how the solution depends on different parameters     

广义卡尔曼滤波均方误差最小性的研究

卡尔曼滤波方法是一种应用广泛的估值方法，但其在应用时，要求系统数学模型已知。而广义卡尔曼滤波在应用时不需要系统数学模型是已知的，具有广泛的适用性。在广义卡尔曼滤波方法的消息模型和测量模型的基础上，讨论了其滤波模型的均方误差最小性问题，即最优滤波器的选取问题。最后通过广义卡尔曼滤波与经典卡尔曼滤波仿真曲线的比较，验证了广义卡尔曼滤波的有效性。