Research On Key Technologies Of Multi-sensor Data Fusion

With the rapid development of Control Theory, Information Theory, Microelectronic Technology, Computer Technology, Network Technology and Sensor Technology since 1950s, multi-sensor data fusion technology has an extremely wide use in the domain of military and civilian, for example: complex industrial control, robotics, automatic target recognition, traffic control, marine monitoring and management, agriculture, remote sensing, medical diagnostics, image processing, pattern recognition, etc.Compared with the single sensor, using multi-sensor data fusion technology to solve the problems of exploration, tracking and target recognition, can improve system reliability and robustness, enhance data reliability, improve accuracy, extend the system time and space coverage, and increase the system's real-time performance and information utilization. Through the multi-level, multifaceted and multi-level processing of the data from multiple sensors, we can obtain moremeaningful information which can not be provided by a single sensor and accurate information, decision-making basis for a variety of application system can be provided. Therefore, data fusion service has become one of the most important applications in the sensor network.In this dissertation, a number of key issues of multi-sensor data fusion technology have been studied, including: a unified description and modeling of heterogeneous information; null value attributes estimating and feature reduction technology of incomplete information system; distributed data fusion technology, etc.Firstly,the basic theory of random sets and the relationship of mutual transformation between random sets and traditional methods of uncertain information fusion including D-S evidence theory and fuzzy sets are introduced, then a method that using random sets theory to descript heterogeneous information in condition monitoring and fault diagnosis is proposed. The first step, the concept of the global sensor is introduced into the case of using multi-sensor to monitor one of factors that affect the condition, and then obtain the value of theglobal sensor by curve fitting; the second step, using the random sets theory to descript the information provided by the sensors and the experts, and the plausibility measure random sets probability to descript basic probability distribution; The third step, do the fusion of sensors data and the experiences of experts in the framework of random sets, get the final result.Secondly, the knowledge of the rating prediction in the collaborative filtering is introduced, the method that solve the sparse data problem in collaborative filtering is combined with null-value estimation in order to solve the sparse data problem; using sparsity to control the selectivity of the estimating algorithm and the similarity weight ensure accuracy of the null-value estimation, and then an improved method based on similarity relation of null-value estimation is proposed; finally, the capability of the algorithm is validated and analysed through classic data sets and real data sets.Thirdly, a limited tolerance relation based on existence null-values interpolated is proposed. The relationship model can deal with the incomplete information system including both existence null-values and inexistence null-value. The concept of knowledge granularity is introduced, and the calculation method of attribute importance and feature reduction algorithm used in this relationship model is given; Time complexity of the model is analyzed by experiments; the validity of the model is verified by comparing with other relational model.Finally, a distributed D-S evidence theory data fusion method based on the predicted of credibility is proposed. First, the conception of predicted of credibility coefficient and formula are given based on the existing evidence source credibility coefficient algorithm, and the method of training balancing factor of the credibility coefficient is introduced; Second, combining the formula with original D-S evidence theory in order to solve the evidence conflict problem without changing the properties of original combination rules; Finally, the effectiveness of the algorithm is proved by the simulation.At present, there are still a lot of key issues in the research of multi-sensor data fusion technology. In this dissertation, some of these issues are studied and discussed, and the method based on the random sets theory, solving the problem that the unified description and modeling of heterogeneous information is proposed. This method provides a prerequisite for heterogeneous multi-source data fusion; the feature reduction algorithm based on existential null-values interpolated can remove redundant information in the data and effectively reduce the time complexity and space complexity; The distributed D-S evidence theory data fusion method based on the predicted of credibility can improve the efficiency of data fusion under the premise of ensuring the results. Therefore, the study in this dissertation has important theoretical value and application value.