本文研究了在输入输出观测数据均含有噪声的情况下如何有效地进行鲁棒自适应滤波的问题。以总体均方误差（TMSE）最小为准则，基于最速下降原理，通过对总体均方误差梯度进行修正，提出了一种鲁棒的总体均方最小自适应滤波算法。通过与已有算法的对比分析表明，该算法能够有效地降低权向量的每步调整量对噪声的敏感程度.。仿真实验的结果进一步表明，该算法的鲁棒抗噪性能和稳态收敛精度明显地高于其它同类方法，而且可以使用较大的学习因子，在高噪声环境下仍然保持良好的收敛性。

传统Snake模型存在的缺点是，其初始轮廓必须靠近图像中感兴趣目标的真实边缘，否则会得到错误结果，且由于Snake模型的非凸性，结果不能进入感兴趣目标的深凹部分，很容易陷入局部极小点。由此该文提出一种基于力场分析的主动轮廓模型，详细分析了基于欧氏距离变换的距离势能力场分布,归纳出感兴趣目标上真轮廓点与假轮廓点的判别标准。建立了由曲线能量到最终结果的有效方法，避免了Snake陷入局部极小点。实验结果表明，该模型具有较大的捕获区域，能够进入感兴趣目标的深凹部分，准确提取感兴趣目标的轮廓。与GVFSnake模型相比，该模型具有很小的计算量。

基于混沌现象普遍存在于各类非线性系统中这一事实，针对工业过程中的混沌运动行为，给出了混沌稳态的数学定义，证明了具有非线性机制的工业过程混沌稳态的存在性定理，并对一个化工过程的混沌稳态进行了仿真计算。

综述多传感信息融合技术的概念、发展的起因、主要研究内容、现有的理论和方法，同时还就其在工业机器人、汽车自动导航与驾驶、身份识别、多目标跟踪、人工智能等自动化领域的应用进行了讨论。作者对于这一新兴研究方向未来的发展趋势作了展望，并就信息融合的一般理论、主要方法论、存在的问题等进行了探讨。

This Letter considers the robust adaptive tracking control problem of a class of uncertain chaotic systems with time-varying unknown but bounded parameters. A novel controller is designed based on the Lyapunov stability theory. The proposed controller ensures that the states of the controlled chaotic systems globally asymptotically track the desired bounded trajectories. Simulation results verify the effectiveness of the proposed controller.

This paper deals with data (or information) fusion for the purpose of estimation. Three estimation fusion architectures are considered: centralized, distributed, and hybrid. A unified linear model and a general framework for these three architectures are established. Optimal fusion rules based on the best linear unbiased estimation (BLUE), the weighted least squares (WLS), and their generalized versions are presented for cases with complete, incomplete, or no prior information. These rules are more general and flexible, and have wider applicability than previous results. For example, they are in a unified form that is optimal for all of the three fusion architectures with arbitrary correlation of local estimates or observation errors across sensors or across time. They are also in explicit forms convenient for implementation. The optimal fusion rules presented are not limited to linear data models. Illustrative numerical results are provided to verify the fusion rules and demonstrate how these fusion rules can be used in cases with complete, incomplete, or no prior information.

Active control is an effective method for making two identical Rossler and Chen systems be synchronized. However, this method works only for a certain class of chaotic systems with known parameters both in drive systems and response systems. Modification based on Lyapunov stability theory is proposed in order to overcome this limitation. An adaptive synchronization controller, which can make the states of two identical Rossler and Chen systems globally asymptotically synchronized in the presence of system's unknown constant parameters, is derived. Especially, when some unknown parameters are positive, we can make the controller more simple, besides, the controller is independent of those positive uncertain parameters. At last, when the condition that arbitrary unknown parameters in two systems are identical constants is cancelled, we demonstrate that it is possible to synchronize two chaotic systems. All results are proved using a well-known Lyapunov stability theorem. Numerical simulations are given to validate the proposed synchronization approach.

基于Volterra级数理论，计算出M IMO双线性系统的n阶冲激响应函数矩阵（或n阶Volterra核矩阵）。通过研究它的n阶冲激响应函数矩阵的性质，给出了一些简单的开环稳定性判据；并深入研究了它们的闭环稳定性，给出了直接利用其子系统的开环稳定性来判别其闭环系统稳定性的判据；最后，用仿真实例来验证其有效性。

The controllability of both continuous and discrete periodic systems are evaluated. The controllability for continuous periodic systems is extended to unit period control, and these results are then directly extended to discrete periodic systems. An assessment is made on retaining periodic system controllability during discretisation, resulting in the conclusion that there is no loss in the controllability of linear periodic systems during discretisation when using a nonequidistant sampling pattern.

Track initiation and termination is an important function in multi-target tracking (MTT), it is a decision part for establishing the records of the new targets and terminating the unwanted records of the inexistent targets when they leave the surveillance region. But in the heavy cluttered environment, there exists so called "incomplete data problem", due to the unknown state of the target and the sequence of measurements which originate from the target. Because the EM algorithm is an efficient means to solve the "incomplete data problem", a fast track initiation and termination approach applicable to engineering is presented, which combines the sequential probabilistic ratio test (SPRT) with recursive expectation maximization (EM). Here, track initiation and termination is modeled as a hypothesis testing problem. And aiming at the computational difficulty of the likelihood function due to the unknown target state and the sequence of measurements which originate from the target, the recursive EM-based formula of likelihood function is introduced. It gives an efficient solution to track initiation and termination by processing the measurement data sequentially, and its recursive form can meet the real-time demand. The simulation results indicate that the proposed method performs well for track initiation and termination, especially in heavy cluttered environment.