Kalman Filter Applications in Airborne Radar Tracking

This paper studies the application of Kalman filtering to single-target track systems in airborne radar. An angle channel Kalman filter is configured which incorporates measures of range, range rate, and on-board dynamics. Theoretical performance results are given and a discussion of methods for reducing the complexity of the Kalman gain computation is presented. A suboptimal antenna controller which operates on the outputs of the angle Kalman filter is also described. In addition, methodological improvements are shown to exist in the design of range and range-rate trackers using the Kalman filter configuration.     

再论联邦滤波在组合导航中的应用可行性

在联邦滤波算法中，针对子滤波器存在私有状态从而导致全局状态信息融合估计为次优估计的问题，通过一个简单的3维状态、2维量测系统的数值仿真以及速度+姿态传递对准组合导航仿真，验证了联邦滤波的次优估计结果误差太大，难以满足实际系统的高精度滤波需求。联邦滤波估计的次优程度没有经过严格证明，且难以证明，因而存在应用风险。对于以精度为主要性能指标的组合导航系统，不建议采用联邦滤波算法，否则其精度损失不足以弥补其带来的计算量降低和容错性提高的优势。     

Kalman filtering with state equality constraints

Kalman filters are commonly used to estimate the states of a dynamic system. However, in the application of Kalman filters there is often known model or signal information that is either ignored or dealt with heuristically. For instance, constraints on state values (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. A rigorous analytic method of incorporating state equality constraints in the Kalman filter is developed. The constraints may be time varying. At each time step the unconstrained Kalman filter solution is projected onto the state constraint surface. This significantly improves the prediction accuracy of the filter. The use of this algorithm is demonstrated on a simple nonlinear vehicle tracking problem     

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.     

Application of the Kalman-Levy Filter for Tracking Maneuvering Targets

Among target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an interacting multiple model (IMM) estimator). The oversimplification resulting from the above assumptions can cause degradation in tracking performance. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. This filter assumes a heavy-tailed noise distribution known as the Levy distribution. Due to the heavy-tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of the Kalman-Levy filter in the nonmaneuvering portion of track is worse than that of a Kalman filter. For this reason, an IMM with one Kalman and one Kalman-Levy module is developed here. Also, the superiority of the IMM with Kalman-Levy module over only Kalman-filter-based IMM for realistic maneuvers is shown by simulation results.     

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%。     

A new adaptive Kalman filter for navigation systems of carrier-based aircraft

The features of carrier-based aircraft’s navigation systems during the approach and landing phases are investigated. A new adaptive Kalman filter with unknown state noise statistics is proposed to improve the accuracy of the INS/GNSS integrated navigation system. The adaptive filtering algorithm aims to estimate and adapt the unknown state noise covariance Q in high dynamic conditions, when the measurement noise covariance R is assumed to be known empirically in advance. The new adaptive Kalman ...     

GPS/INS组合导航系统自适应滤波算法与仿真研究

随着组合导航系统应用环境的日趋复杂，给噪声统计特性的准确描述带来困难，这将造成Kalman滤波器不稳定甚至发散。首先对目前解决此问题常用的自适应滤波方法进行了总结和分析，在此基础上，给出了基于滤波收敛性判据，结合Sage-Husa自适应滤波和强跟踪Kalman滤波的改进自适应滤波算法。最后以GPS／INS组合导航系统为例进行了计算机仿真，结果表明：该算法可有效抑制滤波发散，具有较大范围的自适应能力。     

An alternate algorithm for discrete-time filtering

The discrete-time Kalman filter is an optimal estimator for the states of a linear, stochastic system. It assumes that measurements are linear combinations of the states, and all disturbances are Gaussian. The influence diagram, a decision analysis tool that provides an algorithm for discrete-time filtering equivalent to the Kalman filter when the influence diagram represents Gaussian random variables, is discussed. The influence diagram algorithm is a factored form of the Kalman filter, similar to other factored forms such as the U-D filter. Compared with the Kalman filter, it offers improved numerical properties. Compared with other factored forms, it offers a reduced computational load     

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。     

Passive Position Location Estimation Using the Extended Kalman Filter

Several papers have been published recently using the method ofleast squares for passive position location estimation. While the Kalman filter is mentioned as an alternative approach in most ofthese papers, none of the papers actually compare the performanceof the Kalman filter with the method of least squares. In this paper,the performances of the extended Kalman filter and the iteratedextended Kalman filter are compared with the method of leastsquares. Monte Carlo results are given showing how the a prioricovariance matrix influences the accuracy of the extended Kalmanfilter.     

基于广义Kalman估计的机载雷达高精度控制系统

 研究了在某型现役机载雷达系统中, 采用广义Kalman 滤波器方法来预估目标机的俯仰角和方位角,产生跟踪目标用的雷达天线驱动信号, 替代传统的速率陀螺测量元件来补偿本机机动所造成的扰动的方法,同时对探测信号本身所具有的延迟起到了补偿作用。对目标的运动采用直角坐标系中的Singer 模型描述, 而对测量信号则是应用极坐标系中的描述, 采用广义Kalman 滤波器来完成估计, 即在每一步的估计和控制中对计算测量方程进行线性化结果, 实现两种坐标系的转换。通过应用Matlab/ Simulink 软件对整个系统的建模、设计及仿真研究, 得到了满意的结果。     

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。     

基于自适应联邦滤波的相对导航信息融合

针对基于GPS/MV组合导航方式的无人机空中加油问题,分析了对接阶段GPS及视觉传感器存在的条件约束。在建立导航传感器非线性相对位置测量模型的基础上,设计了基于扩展卡尔曼滤波的自适应联邦滤波器,并与集中式滤波进行了对比仿真。结果表明,提出的算法保证了部分传感器失效时导航数据输出的平稳性和容错性,滤波精度完全满足无人机空中加油相对导航系统要求。     

BP神经网络在惯导系统传递对准中的应用

针对系统阶次较高时卡尔曼滤波实时性较差的特点,将多层BP神经网络替代卡尔曼滤波应用于舰载机惯导系统的传递对准。利用卡尔曼滤波的输入、输出作为BP神经网络滤波的样本对值进行训练,得到了神经网络的输出值,实现了惯导传递对准中的滤波功能。仿真结果表明,将BP神经网络用于传递对准,既获得了与卡尔曼滤波相当的精度,又有效地降低了系统的解算时间,提高了系统的实时性。     

紊流风场下的无人机飞行状态卡尔曼滤波估计

为了模拟紊流风场下的无人机飞行状态响应,减小紊流风场对无人机飞行速度的影响,使用数值法建立了紊流风场有色噪声模型,生成了符合大气紊流相关特性的紊流风场并对其进行数值仿真。在此基础上,通过状态扩增处理将系统噪声白化,设计出一种基于有色噪声的无人机飞行速度卡尔曼滤波器,该滤波器解决了基本卡尔曼滤波仅适用于白噪声的情况。仿真结果表明,使用此滤波器可有效减小紊流风场对无人机飞行速度的影响,进而满足飞行速度控制输入的精度要求。     

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.     

机动目标的广义卡尔曼滤波信号模型的理论推导

卡尔曼滤波方法是一种应用广泛的估值方法,但其要求系统数学模型已知,而广义卡尔曼滤波与卡尔曼滤波相比并不需要系统数学模型已知,具有更强的现实意义。从泛函分析的角度讨论了连续函数的最佳逼近问题,并以此为基础提出了广义卡尔曼滤波的信号模型和递推公式。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。     

Systolic array implementation of second-order Kalman filter

The approach for design of parallel implementation of the second-order Kalman filter using systolic array processors is proposed. The original time complexity of more than 27n³ for sequential processing can be reduced to only 21n+10r-4 in the proposed parallel implementation. The improvement is gained from the utilization of interarray pipelining, concurrent processing and substitution forwarding techniques. The approach is especially suitable for real-time application of the Kalman filter in solving second-order linear system     

航空发动机ZP/LTR方法稳定性分析及改进

分析了零点配置/回路传函恢复(ZP/LTR)方法稳定性, 给出并证明了其稳定性条件.但该条件较为苛刻, 限制了ZP/LTR方法的应用.为了对ZP/LTR方法改进, 在设计过程中引入了Kal man滤波器, 通过选取特殊的参数, 使Kal man滤波器回路逼近并取代原目标回路, 从而在保留ZP/LTR方法性能的同时, 通过保证Kal man滤波器回路的稳定性保证了最后闭环系统的稳定性.给出并证明了改进后的ZP/LTR方法的稳定性条件, 指出改进后的ZP/LTR方法的稳定性条件较为宽松, 拓宽了原ZP/LTR方法的应用范围.