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.

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.

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.

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.