Aided Tracking as Applied to High Accuracy Pointing Systems

Two basic concepts of rate aided tracking and position aided tracking are applied to a conventional pointing system in order to improve performance. The aided track signals are derived in an inertial space format and are generated from a Kalman filter algorithm. Computational results are included to show the interplay between the conventional pointing system and the aided track filter.     

Further results on radar pointing error reduction using discreteextended Kalman filtering

A recently proposed method of reducing target glint errors in radar systems using extended Kalman filtering is further extended with the inclusion of and compensation for clutter effects. A discrete target model and discrete Kalman filter (DKF) are used. Simulation results demonstrating the DKF are presented, and the limits on the effectiveness of the method are investigated. The major advantage of the DKF is that it can be implemented in software in the digital processor of the radar, offering flexibility over continuous time filters. The ability of the filter to reduce clutter effects further demonstrates the usefulness of this technique for radar pointing error reduction     

Radar Target Pointing Error Reduction Using Extended Kalman Filtering

A new method of reducing target glint errors in radar systems is presented. The target is modeled as n reflectors whose magnitudes and phases are known. The reflector positions are described by a dynamical model driven by white Gaussian noise. The resulting vibrations of the target reflectors produce glintlike pointing errors in the radar system. An extended Kalman filter is developed to estimate the positions of the target reflectors; this information is used to substantially reduce the pointing error due to glint. Data illustrating this glint reduction is given. The model is extended by the inclusion of clutter effects modeled in the same fashion as the glint phenomenon. The results presented indicate the limits of usefulness of this technique as a function of both receiver noise and relative clutter amplitude.     

星载红外探测阵列对导弹跟踪定位的研究

通过分析研究建立了前视红外探测阵列 (FL IR)对导弹进行精确跟踪、定位的数学模型 ,其中包括导弹的运动模型、大气干扰模型和探测阵列的观测模型。根据探测阵列的原始观测数据 ,利用扩展卡尔曼滤波器 (EKF)精确跟踪导弹目标。由于导弹与探测器的距离较远 ,故可视为点目标。导弹在探测阵列上投影的位置由两部分组成 :导弹真实运动位置和由于大气干扰造成的偏移。滤波器分别估计了这两种位移在探测阵列上的变化。最后用蒙特卡罗方法分析了滤波器的性能。     

GPS/INS integration on the Standoff Land Attack Missile (SLAM)

The Standoff Land Attack Missile (SLAM) is a worldwide, all-weather, precision-strike weapon system deployed from carrier-based aircraft. In the primary mode of operation, target location and other mission data are generated from intelligence sources available on the aircraft carrier and loaded into the missile prior to aircraft takeoff. After missile launch, the SLAM inertial navigation system (INS) guides the missile along the planned trajectory. Updating the missile INS from the Global Positioning System (GPS) during flight provides precise midcourse navigation and enhances target acquisition by accurate, on-target pointing of the SLAM Maverick seeker. The GPS/INS avionics and software integration used for SLAM are described in detail, along with some of the design tradeoffs that led to the approach. The avionics configuration integrates the Harpoon midcourse guidance unit, which includes a strapdown inertial sensor package and digital processor, with a Rockwell-Collins single-channel, sequential GPS receiver processor unit (RPU), a derivative of the GPS phase-III user equipment. In addition to the GPS receiver elements the RPU contains the navigation processor, which executes the SLAM navigation, Kalman filter algorithms, and other guidance algorithms including seeker pointing. Flight-test results of the SLAM GPS-aided INS are also included     

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。     

Interval Kalman filtering

The classical Kalman filtering technique is extended to interval linear systems with the same statistical assumptions on noise, for which the classical technique is no longer applicable. Necessary interval analysis, particularly the notion of interval expectation, is reviewed and introduced. The interval Kalman filter (IKF) is then derived, which has the same structure as the classical algorithm, using no additional analysis or computation from such as H/sup /spl infin//-mathematics. A suboptimal IKF is suggested next, for the purpose of real-time implementation. Finally, computer simulations are shown to compare the new interval Kalman filtering algorithm with the classical Kalman filtering scheme and some other existing robust Kalman filtering methods.     

利用卡尔曼滤波求取直升机气动导数的应用研究

本文主要介绍利用低通滤波、卡尔曼滤波及最小二乘法求取直升机气动导数的方法。本方法的特点是,通过低通滤波使旋翼高频成份的影响减至最小,同时求取试验数据的测量噪声和过程噪声,然后通过卡尔曼滤波使试验数据包含的随机噪声减至最小,最后用最小二乘法求得直升机的气动导数,为了提高卡尔曼滤波的准确度,用最小二乘法由试验数据求取直升机的气动导数作为卡尔曼滤波时的初始导数。计算结果表明,该方法可使试验数据中包含的噪声大大减小,误差带减少70％以上,而计算工作量又远远小于最大似然法。     

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.     

Adaptive Tracking Filter for Maneuvering Targets

A general method of continually restructuring an optimum Bayes-Kalman tracking filter is proposed by conceptualizing a growing tree of filters to maintain optimality on a target exhibiting maneuver variables. This tree concept is then constrained from growth by quantizing the continuously sensed maneuver variables and restricting these to a small value from which an average maneuver is calculated. Kalman filters are calculated and carried in parallel for each quantized variable. This constrained tree of several parallel Kalman filters demands only modest om; puter time, yet provides very good performance. This concept is implemented for a Doppler tracking system and the performance is compared to an extended Kalman filter. Simulation results are presented which show dramatic tracking improvement when using the adaptive tracking filter.     

Characterization of Kalman filter residuals in the presence ofmismodeling

The mean and covariance of a Kalman filter residual are computed for specific cases in which the Kalman filter model differs from a linear model that accurately represents the true system (the truth model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a different internal model, and a hypothesis testing algorithm that uses the residuals from this bank of Kalman filters to estimate the true system model. At most, only one Kalman filter model will exactly match the truth model and will produce a residual whose mean and standard deviation have already been analyzed. All of the other filters use internal models that mismodel the true system. We compute the effects of a mismodeled input matrix, output matrix, and state transition matrix on these residuals. The computed mean and covariance are compared with simulation results of flight control failures that correspond to mismodeled input matrices and output matrices     

Confidence Limits for the Pointing Error of Gimbaled Sensors

The confidence limits for the pointing error of a gimbaled sensor relative to an off-gimbal reference are derived in this paper. The various misalignment angles and errors that lead to discrepancies between the indicated and the actual pointing direction are discussed in detail. A mathematical model is developed from which the statistics of the pointing error are derived. The confidence limits are obtained for a variety of cases.     

基于改进容积卡尔曼滤波的惯性/光流组合自主测速方法

针对容积卡尔曼滤波算法在惯性/光流组合测速数据融合时出现由于各系统输出数据频率不一致导致融合精度有限的问题,提出了一种基于多速率残差校正的改进容积卡尔曼滤波算法.通过当前时刻误差估算组合导航系统残差,再使用估算后的残差对速度估计值进行补偿,最终实现惯性/光流组合系统速度测量值的数据融合.实验结果表明,通过提出的改进容积...     

Observer-type Kalman innovation filter for uncertain linear systems

An observer-type of Kalman innovation filtering algorithm to find a practically implementable "best" Kalman filter, and such an algorithm based on the evolutionary programming (EP) optima-search technique, are proposed, for linear discrete-time systems with time-invariant unknown-but-hounded plant and noise uncertainties. The worst-case parameter set from the stochastic uncertain system represented by the interval form with respect to the implemented "best" filter is also found in this work for demonstrating the effectiveness of the proposed filtering scheme. The new EP-based algorithm utilizes the global optima-searching capability of EP to find the optimal Kalman filter and state estimates at every iteration, which include both the best possible worst case Interval and the optimal nominal trajectory of the Kalman filtering estimates of the system state vectors. Simulation results are included to show that the new algorithm yields more accurate estimates and is less conservative as compared with other related robust filtering schemes     

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%。     

Convergence analysis for inexact mechanization of Kalman filtering

A computational aspect of real-time estimation is considered, in which the estimation algorithm to be used has the standard optimal Kalman filtering structure, but the actual inverse matrix within the Kalman gain is replaced by an expedient approximation at each instant. In real-time applications, most Kalman filtering schemes are approximate to a degree as a consequence of numerical roundoff matrix inversion. The convergence properties and error estimates of such schemes are obtained to provide a theoretical basis for gauging the utility of using the above approximations of the Kalman gain matrix at each time instant. A new exponentially convergent scheme is also suggested for approximating the inverse matrix within the Kalman gain. Conditions are determined under which online approximate matrix inversion can be eliminated as the cause of Kalman filter divergence in real-time implementations     

Interrelationship of single-filter and multiple-model adaptivealgorithms

An equivalent filter bank structure for multiple model adaptive estimation (MMAE) is developed that uses the residual and state estimates from a single Kalman filter and linear transforms to produce equivalent residuals of a complete Kalman filter bank. The linear transforms, which are a function of the differences between the system models used by the various Kalman filters, are developed for modeling differences in the system input matrix, the output matrix, and the state transition matrix. The computational cost of this new structure is compared with the cost of the standard Kalman filter bank (SKFB) for each of these modeling differences. This structure is quite similar to the generalized likelihood ratio (GLR) structure, where the linear transforms can be used to compute the matched filters used in the GLR approach. This approach produces the best matched filters in the sense that they truly represent the time history of the residuals caused by a physically motivated failure model     

广义卡尔曼滤波递推公式的证明

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。在广义卡尔曼滤波方法的状态模型和测量模型的基础上讨论了其递推公式的证明问题。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。     

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。     

自适应扩展增量 Kalman 滤波方法

基于扩展增量Kalman滤波方法(EIKF)和自适应增量Kalman滤波(AIKF),建立自适应扩展增量Kalman(AEIKF)模型及其分析方法,给出递推算法.在许多实际情况(如深空探测),由于环境因素的影响、测量设备的不稳定性等原因,量测方程往往存在未知的系统误差,并且模型参数也具有不确定性,结果导致较大的Kalman滤波误差,影响滤波的收敛性.提出的AEIKF方法能够成功消除这种未知的系统误差,并能够实时估计变化的噪声统计量,提高Kalman滤波精度.该方法计算简单,便于工程应用.