Detection of Target Maneuver Onset

A classical maneuvering target tracking (MTT) problem (detection of the onset of a target maneuver) is presented in two parts. The first part reviews most traditional maneuver onset detectors and presents results from a comprehensive simulation study and comparison of their performance. Six algorithms for maneuver onset detection are examined: measurement residual chi-square, input estimate chi-square, input estimate significance test, generalized likelihood ratio (GLR), cumulative sum, and marginalized likelihood ratio (MLR) detectors. The second part proposes two novel maneuver onset detectors based on sequential statistical tests. Cumulative sums (CUSUM) type and Shiryayev sequential probability ratio (SSPRT) maneuver onset detectors are developed by using a likelihood marginalization technique to cope with the difficulty that the target maneuver accelerations are unknown. The proposed technique gives explicit solutions for Gaussian-mixture prior distributions, and can be applied to arbitrary prior distributions through Gaussian-mixture approximations. The approach essentially utilizes a~priori information about the maneuver accelerations in typical tracking engagements and thus allows to improve detection performance as compared with traditional maneuver detectors. Simulation results demonstrating the improved capabilities of the proposed onset maneuver detectors are presented.     

Adaptive nonlinear guidance law considering control loop dynamics

A new adaptive nonlinear guidance law is proposed here. The fourth order state equation for integrated guidance and control loop is formulated taking into consideration the target uncertainties and control loop dynamics. The state equation is further changed into the normal form by nonlinear coordinate transformation. Using the normal form of state equation, an adaptive nonlinear guidance law is proposed to compensate for the uncertainties in both target acceleration and control loop dynamics. The proposed law adopts the sliding mode control approach with adaptation for unknown bound of uncertainties. The present approach can effectively solve the existing guidance problem against target maneuver and the limited performance of control loop. We have provided the stability analyses and performed simulations comparing favorably our approach to the state of the art.     

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy.     

Incremental maneuver estimation model for target tracking

An incremental model for maneuver detection and estimation for use in target tracking with the Kalman filter is described. The approach is similar to the multiple Kalman filter bank, but with a memory for the maneuver status for the track under consideration. The advantage of this approach is that the target acceleration can be more accurately estimated. The maneuver-detection model has shown good maneuver-following capability. Moreover, it needs only a finite number of Kalman filters to handle all possible maneuver values and it responds quickly as maneuver occurs. When there is an abrupt maneuver change the model can still track the targets in short time     

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.     

Optimal Tracking of Maneuvering Targets

Maneuvering target motion is modeled by introducing a binary random variable in the target state equation. The optimal estimate is shown to be a weighted combination of two Kalman filter estimates with weights depending on the likelihood ratio for the detection of a maneuver. A tracking scheme is proposed for maneuvering target tracking and illustrated in an example.     

机动目标的模型与跟踪算法

 <正> 在机动目标的“当前”统计模型中,目标的加速度被描述为修正的瑞利—马尔科夫过程,对应的自适应跟踪算法呈现出较好的跟踪特性。文献[2]研究了该模型及其自适应算法在实际的机载雷达跟踪系统的应用;文献[3]进一步推广了基于“当前”模型的MPDAF算法。本文提出一个新的机动目标模型,即假定目标加速度为一高斯—马尔     

考虑信噪比影响的非线性H_∞制导律设计

针对导弹对目标拦截问题,考虑测量信号信噪比影响,以非线性H∞控制理论为基础,应用Lya-punov函数稳定性的分析方法设计了具有强鲁棒性的非线性制导律。首先,以弹-目相对距离和相对速度为变量,并将目标机动视为扰动,建立弹-目相对运动的非线性数学模型。其次,基于零化弹目相对距离的思想,针对拦截过程中测量噪声的影响问题,提出了非线性H∞稳定控制策略,得到连续的非线性末制导律,并分析了信噪比与制导律参数的关系,对测量信号信噪比提出了要求。同时,严格证明了制导系统的稳定性,且无需求解HJI偏微分方程。仿真结果表明,这种制导律对大机动目标具有较强的鲁棒性和适应性,同时能获得良好的制导精度。     

交互作用的双自适应模型估计器

 通过对目标的不确定机动分析和对不确定机动的模式分类(非机动、临界机动、弱机动、强机动),建立了一种新的目标状态自适应估计器——交互作用的双自适应模型估计器。它通过具有机动识别特性的二阶自适应模型和具有机动水平特性的三阶自适应模型,以及它们之间交互作用自适应组合方式的结合,达到了跟踪估计目标各种运动的“全面”自适应能力。应用新估计器对目标的5种基本运动进行了Monto-carlo仿真。仿真表明,它具有所期望的良好性能。     

高超声速滑翔目标自适应跟踪方法

针对机动模式复杂多变的高超声速滑翔目标跟踪问题,提出了一种机动频率自适应跟踪方法。采用介于常速度和常加速度模型之间的Singer模型来表征目标气动力加速度的变化,从而建立跟踪系统的状态方程。根据地基雷达量测量获得系统的量测方程,鉴于距离和角度信息的量级相差较大将其由球形量测量转换为位置量测量。为了适应高超声速滑翔目标灵活多样的机动模式,基于正交性原理和无迹卡尔曼滤波算法实现了Singer模型中机动频率参数的自适应。利用滤波信息计算得到能够反映状态模型误差大小的调整因子,用于放大Singer模型中的机动频率,进而调整状态方程的过程噪声以降低模型误差。通过对2种典型机动轨迹的跟踪仿真,并与交互式多模型等方法进行比较,结果表明所提方法的跟踪精度高、计算量小,能够较好地适应阶跃机动和连续幅值变化的机动。     

基于超静定配平的机动载荷控制风洞试验

介绍了俯仰机动载荷减缓(MLA)在某运输类飞机缩比风洞试验模型上的应用,旨在通过风洞试验研究一种基于超静定配平原理的机动载荷控制方法。首先,对模型飞机纵向超静定配平方法进行了研究并从理论上揭示通过其减缓机动载荷的基本原理;然后,依据超静定配平原理设计了MLA控制律,通过反馈模型飞机等效过载驱动副翼偏转减小机翼载荷,同时偏转升降舵来保持飞机的俯仰机动性能;最后,依次实施了超静定配平试验,气动伺服弹性稳定性试验以及机动载荷减缓试验,分别用以确定MLA控制律参数,检查控制系统稳定性以及获取俯仰机动时的系统响应。试验结果表明:在MLA控制律作用下,机翼根部弯矩增量比MLA控制律关闭时减小了10%以上,而模型飞机的俯仰机动性能基本保持不变;MLA控制律的加入使控制增稳系统稳定性略有下降;通过超静定配平试验确定MLA控制参数的方法有效提升了MLA控制律设计可靠性,使翼根弯矩减缓量接近目标值。研究工作为运输类飞机的机动载荷控制设计与试验提供了一种可行途径。     

Influence of non-equilibrium reactions on the optimization of aerothrust aeroassisted maneuver with orbital change

The spaceplane is perspective vehicle due to wide maneuverability in comparison with a space capsule. Its maneuverability is expressed by the larger flight range and also by a possibility to rotate orbital inclination in the atmosphere by the aerodynamic and thrust forces. Orbital plane atmospheric rotation maneuvers can significantly reduce fuel costs compared to rocket-dynamic non-coplanar maneuver. However, this maneuver occurs at Mach numbers about 25, and such velocities lead to non-equilibrium chemical reactions in the shock wave. Such reactions change a physicochemical air property, and it affects aerodynamic coefficients. This paper investigates the influence of non-equilibrium reactions on the aerothrust aeroassisted maneuver with orbital change. The approach is to solve an optimization problem using the differential evolution algorithm with a temperature limitation. The spaceplane aerodynamic coefficients are determined by the numerical solution of the Reynolds-averaged Navier-Stokes equations. The aerodynamic calculations are conducted for the cases of perfect and non-equilibrium gases. A comparison of optimal trajectories, control laws, and fuel costs is made between models of perfect and non-equilibrium gases. The effect of a chemically reacting gas on the finite parameters is also evaluated using control laws obtained for a perfect gas.     

Tracking a maneuvering target using input estimation versus theinteracting multiple model algorithm

Two maneuvering-target tracking techniques are compared. The first, called input estimation, models the maneuver as constant unknown input, estimates its magnitude and onset time, and then corrects the state estimate accordingly. The second models the maneuver as a switching of the target state model, where the various state models can be of different dimension and driven by process noises of different intensities, and estimates the state according to the interacting multiple model (IMM) algorithm. While the first requires around twenty parallel filters, it is shown that the latter, implemented in the form of the IMM, performs equally well or better with two or three filters     

单天基平台测向的空间非合作目标跟踪

针对仅含角度测量信息的单个天基平台可观测性较弱的问题,提出了一种含脉冲机动检测的空间非合作目标跟踪算法,并设计了非合作目标实时跟踪数据处理流程.该算法利用抗差估计技术和UKF（Unscented Kalman Filter,无迹卡尔曼滤波）算法构造目标跟踪滤波器,并综合残差多项式拟合和新息分布特征等方法实现目标机动检测,在天基平台观测信息类型有限和观测几何较差的情况下,可以同时排除孤立野值和成片测量野值的影响,实现非合作机动目标的连续稳定跟踪.数值实验验证了算法的可行性和有效性,也表明了跟踪精度和可靠性与测量精度密切相关.     

Generalized input-estimation technique for tracking maneuveringtargets

A new input estimation technique for target tracking problem is proposed. Conventional input estimation techniques assume that the target maneuver level is constant within the detection window, which has been the major drawback of the techniques. The proposed technique is developed to overcome this drawback by modeling the target maneuver as a linear combination of some basic time functions. The resulting algorithm has a generalized formulation including earlier works on input estimation. A detection performance of the proposed algorithm is analyzed by investigating the detection sensitivity according to the selection of maneuver models and other design parameters such as the detection window size, measurement noise level, and sampling step size. A computer simulation study shows that the estimation performance of the proposed algorithm is comparable to Bogler's input estimation method while the computation time is greatly reduced     

A tracking algorithm for maneuvering targets

A methodology for the tracking of maneuvering targets is presented. A quickest-detection scheme based on the innovation sequence is developed for a prompt detection of target maneuvers. The optimal length of a sliding window that minimizes the maneuver detection delay for a given false-alarm rate is determined. After maneuver detection, the system model is modified by adding a maneuver term. A recursive algorithm is proposed to estimate the maneuver magnitude. With this estimate, a modified Kalman filter is used for tracking. Simulation results demonstrate the superior performance of the algorithm, especially during target maneuvers     

基于飞行器图像的目标跟踪方法研究

提出了一种利用飞行器图像进行目标跟踪的方法，建立了飞行器图像跟踪系统。该系统利用一种云台控制算法搜索飞行器的图像，通过对飞行器图像的边缘检测、图像中心点提取等处理，实现了飞行器图像定位。理论计算和实际应用表明，该系统可以对低马赫数和高马赫数飞行器进行图像跟踪。     

敏捷卫星的联合执行机构控制策略

 针对对地观测敏捷卫星大角度快速机动、高控制精度的任务需求,提出了联合推力器与飞轮作为执行机构的控制策略。该控制策略综合利用2种执行机构的优点:推力器以前馈的形式提供机动过程中所需的主要力矩以实现航天器大角度的快速机动,而飞轮以反馈的形式提供精准的控制力矩以提高机动过程中的姿态控制精度。为补偿由于初始状态偏差和推力器输出力矩不准确所带来的控制误差,采用变结构控制设计了2种姿态跟踪控制器,使航天器能够渐进地跟踪上参考轨迹。并对姿态机动控制过程中,飞轮力矩及转速可能出现的饱和问题作了相应的修正。仿真结果表明了所提控制策略及所设计控制算法的可行性和有效性。     

Optimization of observer trajectories for bearings-only targetlocalization

The problem of bearings-only target localization is to estimate the location of a fixed target from a sequence of noisy bearing measurements. Although, in theory, this process is observable even without an observer maneuver, estimation performance (i.e., accuracy, stability and convergence rate) can be greatly enhanced by properly exploiting observer motion to increase observability. This work addresses the optimization of observer trajectories for bearings-only fixed-target localization. The approach presented herein is based on maximizing the determinant of the Fisher information matrix (FIM), subject to state constraints imposed on the observer trajectory (e.g., by the target defense system). Direct optimal control numerical schemes, including the recently introduced differential inclusion (DI) method, are used to solve the resulting optimal control problem. Computer simulations, utilizing the familiar Stansfield and maximum likelihood (ML) estimators, demonstrate the enhancement to target position estimability using the optimal observer trajectories