Li Chai

Distributed state estimation based on quantized observations in a bandwidth constrained sensor network

A distributed state estimation scheme based on quantized observations in wireless sensor network (WSN) is proposed. Unlike the SOI-KF approach, we address the state estimation problem in two steps: firstly, local sensors and the fusion center apply the decentralized estimation scheme (DES) to design the quantized message function and the fusion function; secondly, the fusion center constructs a linear filter to obtain the state estimation by minimizing an upper bound of the error power norm. The optimal filter is computed by the linear matrix inequality (LMI) solver. An example is presented to demonstrate the efficiency of our method.

On-line burning state recognition for sintering process using SSIM index of flame images

Recognition of burning state based on flame images has been an important issue in sintering process of rotary kiln. Existing methods usually adopt techniques of image segmentation, pattern recognition and machine learning, which have high demands on the quality of samples and computational power. It is challenging to the on-line recognition of the burning state, which is essential in realtime control systems. This paper proposes a new approach to the burning state recognition by comparing the structural similarity (SSIM) index of flame images. The burning state is identified according to the maximum SSIM index between the real time flame image and images in two standard libraries which consist reference images with normal-burning state and under-burning state respectively. This method has low computational complexity and is suitable for online control in the rotary kiln system. Simulation results show that the proposed method achieves high recognition accuracy with low computation.

Infinite horizon LQG control with fixed-rate quantization for scalar systems

We study the infinite-horizon LQG control systems with the constraint that the measurement signal is quantized by a fixed-rate quantizer before going into the controller. It has been shown recently that only weak separation principle holds for the LQG control system with communication channels. In this paper, we study the problem of quantized LQG for a scalar system. An adaptive fixed-rate quantizer is designed to achieve the mean-square stability and the good long term average performance. The long term average cost is divided into two parts. The first part depends on the classical LQG cost, and the second part depends on the distortion of the quantizer. For a quantizer with a fixed bit rate of R (per sample), we show that the quantization distortion order is R2−2R for a large R.

Multiple bits distributed moving horizon state estimation for wireless sensor networks

Wireless sensor networks distributed signal processing has to operate under stringent energy and bandwidth limitations. Usually, the observations need to be quantized before they are sent to fusion center (FC). This paper we introduce a multiple bits distributed moving Horizon state estimation approach based on a multiple state single output Gaussian-Markov model. Each sensor node has a thresholds book, then the sensors quantize their measurements to several bits by using the thresholds book which are sent to fusion center. After receiving these information, fusion center make final estimation for system states. Compared with single bit distributed moving horizon state estimation (DMHE), this method ensures that FC need not to send the estimate information back to sensor nodes and provides higher precision of state estimation.

Water pollution source localization based on the contour in sensor networks

To make up the shortcomings of the coarse pollution source localization algorithm and the localization methods based on the diffusion models in water, a pollution source localization algorithm based on the concentration contour is proposed. In the method, the location of the source is obtained according to the geometrical configuration feature of the contour. In the simulations, based on the simulation data of MODFLOW, the proposed localization method is tested and compared with the localization methods based on diffusion models and the CPA(Closest Point Approach) localization method. The results show that the performance of the proposed algorithm is better than the other two methods when the concentration contour is axisymmetric.