A New Gaussian Mixture Algorithm for GMTI Tracking Under a Minimum Detectable Velocity Constraint

This paper introduces a new methodology to account for Doppler blind zone constraints, arising, for example, in ground moving target indicator (GMTI) tracking applications. In such problems, target measurements are suppressed when the range rate (Doppler) of the target drops below a specified threshold in magnitude (the minimum detectable velocity). The proposed method, employing Gaussian mixture approximations to the filtering density, differs from earlier Gaussian mixture approaches in the way missed measurements are modelled. The distinctive feature of the algorithm, as compared with other Gaussian mixture filters, is that it is based on an exact calculation of the filtering density when a measurement is not recorded. Algorithms that result from applying this methodology are simple to implement and computationally undemanding. Simulation results indicate a uniform improvement in estimation accuracy over that of earlier proposed analytic techniques, and a tracking performance comparable to that of state-of-the-art particle filters.      

Motion/force tracking control of nonholonomic mechanical systems via combining cascaded design and backstepping

This paper considers motion/force tracking control of a class of Lagrange mechanical systems with classical nonholonomic constraints. A tracking control method is proposed by combining cascaded methods and backstepping techniques. The main results of this paper include three parts: (1) error dynamics between the kinematic system and the desired paths are transformed into a cascaded system consisting of two subsystems and an interconnection function; (2) under the framework of cascaded methods, virtual controllers for the subsystems are designed to stabilize the error dynamics; (3) the tracking controller is designed for the overall mechanical systems using backstepping techniques.     

Adaptive fuzzy IMM algorithm for uncertain target tracking

In real system application, the interacting multiple model (IMM)-based uncertain target tracking system operates with the following problems: it requires less computing resources as well as a robust performance with respect to the maneuvering such as a sub-model mismatched case, and further, it requires an easy design procedure related to its structures and parameters. To solve these problems, an adaptive fuzzy IMM (AFIMM) algorithm, which is based on well-defined basis sub-models and well-adjusted mode transition probabilities (MTPs), is proposed. The basis sub-models are defined by the detailed analysis in terms of kinematic models as well as the maneuvering property and the MTPs are adjusted by the fuzzy adaptor as well as the fuzzy decision maker. To verify the performance of the proposed algorithm, an airborne target tracking is performed. Simulation results show that the AFIMM effectively solves the problems experienced in the uncertain target tracking system online. Keywords: Adaptive fuzzy IMM, basis sub-models, mode transition probabilities, uncertain target.     

Recovering articulated pose: a comparison of two pre and postimposed constraint methods

We contrast the performance of two methods of imposing constraints during the tracking of articulated objects, the first method preimposing the kinematic constraints during tracking and, thus, using the minimum degrees of freedom, and the second imposing constraints after tracking and, hence, using the maximum. Despite their very different formulations, the methods recover the same pose change. Further comparisons are drawn in terms of computational speed and algorithmic simplicity and robustness, and it is the last area which is the most telling. The results suggest that using built-in constraints is well-suited to tracking individual articulated objects, whereas applying constraints afterward is most suited to problems involving contact and breakage between articulated (or rigid) objects, where the ability to test tracking performance quickly with constraints turned on or off is desirable.     

Smooth trajectory planning for a parallel manipulator with joint friction and jerk constraints

In order to achieve better tracking accuracy effectively, a new smooth and near time-optimal trajectory planning approach is proposed for a parallel manipulator subject to kinematic and dynamic constraints. The complete dynamic model is constructed with consideration of all joint frictions. The presented planning problem can be solved efficiently by formulating a new limitation curve for dynamic constraints and a reduced form for jerk constraints. The motion trajectory is planned with quartic and quintic polynomial splines in Cartesian space and septuple polynomial splines in joint space. Experimental results show that smaller tracking error can be obtained. The developed method can be applied to any robots with analytical inverse kinematic and dynamic solutions.     

Trajectory tracking control of farm vehicles in presence of sliding

In automatic guidance of agriculture vehicles, lateral control is not the only requirement. Much research work has been focused on trajectory tracking control which can provide high longitudinal-lateral control accuracy. Satisfactory results have been reported as soon as vehicles move without sliding. But unfortunately pure rolling constraints are not always satisfied especially in agriculture applications where working conditions are rough and not predictable. In this paper the problem of trajectory tracking control of autonomous farm vehicles in the presence of sliding is addressed. To take sliding effects into account, three variables which characterize sliding effects are introduced into the kinematic model based on geometric and velocity constraints. With a linearized approximation, a refined kinematic model is obtained in which sliding effects appear as additive unknown parameters to the ideal kinematic model. By an integrating parameter adaptation technique with a backstepping method, a stepwise procedure is proposed to design a robust adaptive controller in which time-invariant sliding is compensated for by parameter adaptation and time-varying sliding is corrected by a Variable Structure Controller (VSC). It is theoretically proven that for farm vehicles subjected to sliding, the longitudinal-lateral deviations can be stabilized near zero and the orientation errors converge into a neighborhood near the origin. To be more realistic for agriculture applications, an adaptive controller with projection mapping is also proposed. Both simulation and experimental results show that the proposed (robust) adaptive controllers can guarantee high trajectory tracking accuracy regardless of sliding.     

基于MMPHDF的多机动目标联合检测、跟踪与分类算法

针对场景中存在新目标出现、旧目标消失(即目标数目变化)和密集杂波的复杂情形,利用多模型概率假设密度滤波器(MMPHDF)在多机动目标联合检测与跟踪上的优势,加入类别辅助信息,提出了一种多机动目标联合检测、跟踪与分类算法.该算法的基本思想是在MMPHDF中用属性向量扩展单目标状态向量,用位置和属性的组合测量似然函数代替单目标位置及杂波位置测量似然函数,提高了不同类目标与杂波测量间的鉴别能力,从而改善了目标数目及状态的估计精度;在更新目标状态后,对目标属性信息进行更新,更为精确的目标数目及状态估计又保证了目标分类性能.本文给出了该算法的粒子实现方法.仿真结果验证了上述结论.     

具有空间调整和稀疏约束的相关滤波跟踪算法

因频繁遮挡、尺度变化、边界效应等因素的影响,进行目标跟踪时,时常难以达到较好的预期 效果。再有,采用传统特征提取策略也会影响目标跟踪的鲁棒性。针对上述问题,提出一种具有空间调整和稀 疏约束的相关跟踪算法。利用传统特征与深度特征的有效融合,适应目标表观变化;基于峰值旁瓣比判别目标 在跟踪过程中是否被遮挡,若发生遮挡,则对滤波器进行稀疏正则化约束,提高模型对遮挡问题的鲁棒性;若 未发生遮挡,则通过高斯空间调整惩罚滤波器系数,抑制边界效应的影响。实验利用 OTB 数据集中 5 组涵盖 了严重遮挡和尺度变化等挑战因素的标准视频序列进行测试,定性和定量对比了算法与 4 种热点算法的跟踪效 果。定性分析中基于视频序列的主要挑战因素进行比较,定量分析通过中心点位置误差和重叠率比较跟踪算法 的性能。实验结果表明,算法对上述挑战因素更具鲁棒性。     

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a prior information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the classification process. The two designed estimators are compared and evaluated over rather complex target scenarios. The results demonstrate the usefulness of the proposed scheme for the incorporation of additional speed information. Both filters illustrate the opportunity of the particle filtering and mixture Kalman filtering to incorporate constraints in a natural way, providing reliable tracking and correct classification. Future observations contain valuable information about the current state of the dynamic systems. In the framework of the MKF, an algorithm for delayed estimation is designed for improving the current modal state estimate. It is used as an additional, more reliable information in resolving complicated classification situations.     

Constrained mechanical systems modeling and control: A free-floating space manipulator case as a multi-constrained system

The paper presents a development of a modeling framework for constrained multibody systems, for which constraints may arise either as kinematic, task-based, design, control or origin from conservation laws. The framework is control oriented since its dynamic modules yield nonlinear system dynamics in reduced state forms, i.e., ready to employ control algorithms. It encompasses systems subjected to any equality first order constraints and in this regard it unifies modeling for control applications. The framework properties support numerical computation and simulation studies for nonlinear system models due to the dynamic modeling unification and the reduction procedure built into it. The framework application is illustrated by an example of dynamics modeling and designing a tracking controller for a free-floating space manipulator, which is underactuated, subjected to conservation laws and task-based constraints.     

Intelligent tracking control of a dual‐arm wheeled mobile manipulator with dynamic uncertainties

This paper presents an intelligent control approach that incorporates sliding mode control (SMC) and fuzzy neural network (FNN) into the implementation of back‐stepping control for a path tracking problem of a dual‐arm wheeled mobile manipulator subject to dynamic uncertainties and nonholonomic constraints. By using the back‐stepping technique, the system equations are reformulated into two levels: the kinematic level and the dynamic level. A sliding manifold is constructed by considering the disturbance free kinematic level equations only. With all the system uncertainties concentrated in the dynamic level, an FNN controller associated with a switching type of control law is employed to enforce sliding mode on the prescribed manifold. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that uniform ultimate boundedness for both the tracking error and the FNN weighting updates is ensured. A simulation study, which compares different control design approaches, is included to illustrate the promise of the proposed SMC–FNN method. Copyright © 2012 John Wiley & Sons, Ltd.     

应用于离散模糊系统的最优模糊追踪器设计

针对二阶离散式模糊追踪系统,提出用于构造一种最优模糊追踪器的设计方式, 并用于追踪一类时变目标或任意模型目标.为了能让所提出的设计方法完整并达到总体最小 化,提出了一种近似线性化的离散模糊动态系统的表示法.另外,为了简化计算量,一个多 层分解方式被用于本文所提出的最优设计程序中,模拟结果显示,本文提出的最优模糊追踪 设计方式确能达到预期效果.     

基于ROI特征匹配融合的图像多目标跟踪算法

在图像多目标跟踪问题中,针对图像匹配无法辨别同类别目标以及状态滤波难以对目标突发机动建模两个难点,提出了一种多特征匹配融合跟踪算法。该算法在基于局部方差图(standard deviation map,STDM)的目标检测结果的基础上,首先利用目标感兴趣区域(region of interest,ROI)的图像匹配来克服目标状态匹配误差的影响;然后利用状态特征匹配消除图像匹配识别的模糊性;最后在关联代价全局最优化框架下,将两者匹配结果融合,以提高多目标跟踪的正确率。     

通信受限下网络化多传感器系统序贯卡尔曼滤波加权融合

针对通信受限下网络化多传感器系统难以实时滤波的问题,提出实时序贯滤波融合方法和故障诊断方法.首先基于周期性分组传输通信策略,采用序贯卡尔曼滤波方法,对当前时刻访问融合中心的传感器组进行局部滤波,并导出剩余传感器组的最优局部估计,进而得到线性最小方差意义下的最优融合估计.利用残差加权平方和方法对发生故障的传感器进行定位,仿真结果验证了所提出算法的有效性.     

Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators

In this paper, force/motion tracking control is investigated for nonholonomic mobile manipulators with unknown parameters and disturbances under uncertain holonomic constraints. The nonholonomic mobile manipulator is transformed into a reduced chained form, and then, robust adaptive force/motion control with hybrid variable signals is proposed to compensate for parametric uncertainties and suppress bounded disturbances. The control scheme guarantees that the outputs of the dynamic system track some bounded auxiliary signals, which subsequently drive the kinematic system to the desired trajectory/force. Simulation studies on the control of a wheeled mobile manipulator are used to show the effectiveness of the proposed scheme.     

Robotic interception of moving objects using ideal proportional navigation guidance technique

This paper presents a novel approach to on-line, robot-motion planning for moving-object interception. The proposed approach utilizes a navigation-based technique, which is robust and computationally efficient for the interception of fast-maneuvering objects. Navigation-based techniques were originally developed for the control of missiles tracking free-flying targets. Unlike a missile, however, the end-effector of a robotic arm is connected to the ground, via a number of links and joints, subject to kinematic and dynamic constraints. Also, unlike a missile, the velocity of the robot and the moving object must be matched for a smooth grasp, thus, a hybrid interception scheme, which combines a navigation-based interception technique with a conventional trajectory tracking method is proposed herein for intercepting fast-maneuvering objects. The implementation of the proposed technique is discussed via numerous simulation examples.     

采用序贯滤波的红外/ 雷达机动目标跟踪

当机动目标状态是非平稳和非线性时,红外传感器和雷达的目标状态方程和量测方程都是非线性和非高斯的,为了解决经典跟踪算法的残差较大或发散的问题,提出一种新的融合跟踪算法:在对红外传感器和雷达的量测数据进行时间对准和同步融合后,将融合后的量测数据送入重抽样粒子滤波器进行处理以预测和跟踪机动目标.最后给出了一个仿真跟踪实例,并与同类多雷达跟踪的效果进行了比较,说明了异类融合跟踪优于同类多雷达融合跟踪.     

带二次约束的容积卡尔曼目标跟踪算法

为了提高对带约束目标的跟踪精度,提出了一种带二次约束的容积卡尔曼目标跟踪算法。使用容积卡尔曼滤波得到当前时刻后验估计,通过将系统二次约束作为测量方程引入系统,对后验估计进行修正,最后采用仿真实验对算法性能进行测试。实验结果表明,相对于传统的约束卡尔曼滤波算法,在同等条件下,该算法提高了目标跟踪精度,并且稳定性更强。     

Two and a half degrees of freedom LQG controller and application towind turbines

The 2½ degrees of freedom structure considered has been used in real applications for many years but it has not been shown to follow from the solution of an H² optimal control problem. A Kucera type of polynomial solution is presented which provides a theoretical basis for the structure adopted. The proposed controller structure has the advantage that low frequency tracking properties can be achieved, as in single degree-of-freedom designs, but no constraints then apply to the high frequency tracking behaviour, since it is effectively determined by a two degrees-of-freedom structure. A wind turbine example is presented     

Tracking control design for nonholonomic mechanical systems with affine constraints

The trajectory tracking control is considered for nonholonomic mechanical systems with affine constraints and dynamic friction. A new state transformation is proposed to deal with affine constraints, and then an integral feedback compensation strategy is used to identify the dynamic friction. The proposed controller ensures that the output tracking errors converge to zero as t →∞.As an application, a detailed example is presented to illustrate the effectiveness of the control scheme.