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This paper studies the distributed dimensionality reduction fusion estimation problem for cyber-physical systems with limited bandwidth in presence of eavesdroppers. Since wireless communication is implemented by broadcasting, the eavesdroppers can collude to collect the data through anther communication networks. To protect data privacy, based on the physical processes and local estimation error covariance (EEC) matrix, an insertion method of artificial noise (AN) is developed such that only eavesdroppers’ fusion EEC becomes worse. Meanwhile, the fusion center needs to decode the received signal due to the noise interference, while the successful decoding probability varies with signal to noise ratio. Subsequently, some criteria for the selection probabilities and the successful decoding probabilities are given to guarantee the effectiveness of the AN insertion strategy. Moreover, a sufficient condition of the designed AN power is derived to guarantee the confidentiality. Simulation examples are given to show the effectiveness of the proposed methods.

With the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter. The high-degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm.

This paper is concerned with remote state estimation problem with bandwidth and energy constrained wireless sensor networks(WSNs). To improve the estimation quality under these constrains in addressed system, measurement quantization and event-triggered communication strategies are adopted in WSN. Specifically, quantization strategy and event-triggering mechanism are introduced to describe the set region of original measurements, and a closest ellipsoid approximation of measurement sets method is presented. Subsequently, set-valued Kalman filter based on quantization and event is designed by utilizing the quantizer and trigger information. Finally, an illustrative example is employed to demonstrate the advantages and effectiveness of the proposed methods.

Neural network is an important tool to solve the problem of nonlinear system prediction and control. It has been widely concerned by scholars. However, the existing neural network cannot adaptively allocate the weight of mixed kernel function according to the sample characteristics when it is applied to electric load forecasting. Aiming at this problem, short-term load forecasting algorithm based on adaptive fusion of mixed kernel function is proposed. Firstly, kernel functions are selected from the standard local kernel function and the global kernel function library to form a mixed kernel function. The weight variables and parameters of the kernel function are combined to form a new parameter state vector. Then a nonlinear parameter estimation model is established. Based on this model, the high-order cubature Kalman filter is used to estimate the parameter state, so that the local kernel function and the global kernel function can be adaptively fused. Moreover, the trained neural network is used to predict the load. Finally, the experimental analysis is given based on the actual grid data, and the effectiveness of the adaptive fusion of mixed function algorithm is proved.

This paper studies distributed secure fusion estimation problem in presence of eavesdroppers, where an eavesdropper can collect the messages from sensors to the fusion center through communication networks. To prevent eavesdropping, an insertion method of artificial noises is developed based on the fusion estimation error covariance matrix such that: i) The local/fusion estimation error covariance of the eavesdropper is unbounded; ii) The fusion estimation error covariance of the defender is bounded. Moreover, a sufficient condition is derived to guarantee the effective noise insertion strategy. Simulations are given to show the effectiveness of the proposed methods.

This paper considers optimal control data scheduling for finite-horizon linear quadratic regulation (LQR) control of scalar systems with limited controller-plant communication. Both the single-system and multiple-system scenarios are studied. For the first scenario, we derive the necessary and sufficient condition for a comparison function to be positive. Using this condition, the optimality of an explicit schedule is extended from unstable systems in the existing work to general systems. For the second scenario, we are able to construct explicit optimal scheduling policies for three particular classes of problems. Numerical examples are provided to illustrate the proposed results.

In this work, we study finite-horizon multiple-sensor scheduling for general scalar Gauss-Markov systems, extending previous results where only a class of systems are considered. The scheduling objective is to minimize the terminal estimation error covariance. Only one sensor can transmit its measurement per time instant and each sensor has limited energy. Through building a comparison function and solving its monotone intervals, an efficient algorithm is designed to construct the optimal schedule.

Wireless sensor networks (WSNs) fusion systems are usually faced with the communication bandwidth constraints, so it is necessary to adopt quantization. The existing quantization is usually under the assumption of known distribution function of data. However, obtaining the accurate distribution function of data is impossible. Therefore, in this paper, based on the obtained measurements, a kind of non-uniform quantization strategy under the principle of least square sum of quantization error is proposed firstly. Then, the solving method of non-uniform quantizing points based on adaptive quantization interval is presented. Next, we further provide a recursive updating method of quantizing points which include semi real-time update and real-time update. Finally, on the basis of the new method, we establish the Kalman Filter based on the new idea of Non-uniform Quantization and give some analysis of performance, which includes the theoretical part and experiment simulation.