Optimal Distributed Fusion Research Articles

Optimal fusion state estimator for a multi-sensor system subject to multiple packet dropouts

In this note, we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts. A novel optimal distributed fusion estimator is derived by applying a re-sending mechanism and a parallel information filtering structure. It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.

Data fusion and simulation-based planning and control in cyber physical system for digital assembly of aeroplane

This paper introduces a CPS application for intelligent aeroplane assembly. At first, the CPS structure is presented, which acquires the characteristics of general CPS and enables "simulation-based planning and control" to achieve high level intelligent assembly. Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling, respectively. The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation, and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration, it can also directly link to data, and the error is smaller. This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control. A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness. At the same time, an outer collaborative trajectory planning is also designed. All the experiment results show the feasibility of above proposed methods.

Time-varying optimal distributed fusion white noise deconvolution estimator

White noise deconvolution has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. Using the Kalman filtering method, the time-varying optimal distributed fusion white noise deconvolution estimator is presented for the multisensor linear discrete time-varying systems. It is derived from the centralized fusion white noise deconvolution estimator so that it is identical to the centralized fuser, i.e., it has the global optimality. It is superior to the existing distributed fusion white noise estimators in the optimality and the complexity of computation. A Monte Carlo simulation for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.

Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion

In distributed estimation fusion, processed data from each sensor is sent to the fusion center. By taking linear transformation of the raw measurements of each sensor, two optimal distributed fusion algorithms are proposed in this paper. Compared with existing fusion algorithms, they have three nice properties. First, they are optimal in the sense that they are equivalent to the optimal centralized fusion. Second, their communication requirements from each sensor to the fusion center are equal to or less than those of the centralized and most existing distributed fusion algorithms. Third, they do not need the inverses of estimation error covariance matrices, which are assumed to exist in most existing algorithms but can not be guaranteed to exist. So the proposed algorithms can be applied in more cases. Pros and cons of these two new algorithms are analyzed. A possible way to reduce the computational complexity of the new algorithms, an extension to the case of a singular covariance matrix of measurement noise, and an extension to the reduced-rate communication case for some simple systems are also discussed.