Research On Uncertainty Information Fusion Methods And Their Application

With the development of various kinds of advanced physical, chemical and biologicalsensors, and some sensing technologies are not mature, so that measurement information wasbeing quantities, multidimensional, dynamic, uncertain, incomplete and spatiotemporalvariation, and thus these technologies put forward the higher request for the cominginformation processing. It is difficult to meet the requirement with the traditional data analysis.Therefore, it is an urgent problem for processing these sensor measurement data that reducethe error from single sensor or the uncertainty from multiple sensors. In this dissertation, anumber of key issues of uncertain information fusion technology were studied, and got somesignificant results. The main content and contributions of this dissertation are summarized asfollows:1. In order to solve the problem of the soil properties measurement error because ofmultiple factors interference, the dissertation adopts Rough Sets for reduction of the attributeset and target set for the raw data from various sensors, thus the noise and redundancy will bereduced in sampling. Then constructs information prediction system of Support VectorMachine according to the preprocessing information structure, and solves the problem ofmultisensor data fusion in the situation of small sample and uncertainty. In order to get theoptimal fusion accuracy, it uses Particle Swarm Optimization (PSO) for fusion parameters. Tomake operate faster, and also to increase the accuracy of the fusion, a feature selection processwith PSO is used in this dissertation to optimize the fusion accuracy by its the superiority ofoptimal search ability.2. Although DS evidence theory has many advantages, but the uncertainty formaldescription method based on DS theory has some defects. To reduce the defects and enhancethe fusion efficiency, a new data fusion algorithm based on weighed evidence synthetictechnique is presented. Firstly, the concept of weight of sensor evidence itself and evidencedistance based on a quantification of the similarity between sets to acquire the reliabilityweight of the relationship between evidences is set up. Then considering the disadvantages ofthe improved DS theory, a best method of obtaining evidence weight value is presented by animproved PSO. Finally, we use the improved PSO to acquire the reliability weight of therelationship between evidences to modify DS theory.3. Considering uncertainty measurement and the principle of Least Squares, recursiveweighted least squares fusion algorithm is deduced, and sensor weight coefficient is closerelationship with measuring variance. However, weight coefficient often is deduced frommeasuring variance, due to measurement attribute weight is different, and the dimensionalinconsistencies, then it is difficult to realize the effective measure for the sensor precision. Meanwhile, the sensor itself variance parameter is assigned or given in advance according tothe experience. Thus the anti-interference ability of fusion results is weak, and the robustnessof the algorithms is low. The robust least squares estimation approach proposed in thisdissertation employs an adaptive weighted viewpoint. For the different weight design criteria,the measurement estimation value is got. Due to introduce robust weight factor and learningfactor, we take full advantages of redundancy information of measurement data, and improvethe estimated precision of redundant system measurement data.4. Sensor management is very essential to the multisensor system in the targetassignment, and become the core element of the information fusion system to improve itsperformance. This dissertation describes an intelligent sensor management paradigm thatminimizes system uncertainties with an evolutionary algorithm that evaluates each controldecision from the bottom up. PSO algorithm works from the bottom up searching theparameter space for the best sensor measurement setting. The Bayesian network is moresuited towards assessing the situation and selecting proper performance goals based onconditions and operator inputs. Bayesian networks allow the designer to direct therelationship between the system goals and global performance values providing the flexibilityto define these performance values to fit any situation. Once a relationship between the globalperformance values is well-defined, PSO algorithm searches to optimally select the sensorparameters that achieve the desired global performance, and then the problem ofautomatically allocating the resources of multi-sensor is solved.