Study Of Data Fusion Methods Based On Fuzzy Information Processing

Data fusion technique combine data from multiple sensors, and related information from associated databases, to achieve improved accuracies and more specific inferences than could be achieved by the use of a single sensor alone. It is actually the integration of many traditional disciplines and new areas of engineering, and an important part of modem command, control, communication and intelligence (C31) systems. Data fusion process model contains the position, identity assessment in low level and the situation assessment and the threat assessment in high level. Based on fuzzy information processing technique solving the data fusion problems, such as multisensor signal detection, target tracking and identification, are discussed in this dissertation. The thesis is classified into six chapters and the contents are outlined as follows: In Chapter 1, an introduction is first given to the general principles and theoretical basis of data fusion. And then ,the importance of the research on data fusion based on fuzzy information processing technique are explained and its development is reviewed. Finally, the main achievements and arrangements of the thesis are concluded. In Chapter 2, a design method of decentralized signal detection system which consists of adaptive fuzzied local detectors and a data fusion rule of on line self-learning the weights. The local detectors for inaccurate signal parameters are modeled by means of fuzzy sets which can be adapted to change of the inaccurate signal parameters. The data fusion center where the optimal decision rules are used as objective function can learn the local decision weights on line. In Chapter 3, we propose a data association algorithm employing fuzzy logic based on multisensor and the features of targets, which solves the uncertainty of the data received from sensor measurements in high noisy. A learning method to train fuzzy data association systems with full-fuzzy model based on a steepest descent gradient is studied. The improvement of multiple information on fuzzy data association is analyzed. In Chapter 4, Based on the theory of the estimation and fuzzy logical system,this paper proposes a data association algorithm of the fuzzy logic梡robability interacting, to solve the data association problems typically encountered in the application of multisensor tracking a manoeuving target in a heavily-cluttered environment. The combination of fuzzy association degree and probabilistic association forms the weights that the ith received measurment is target originated. The proposed data association algorithm counteracts the weaknesses of probabilistic data association filter (PDAF). In Chapter 5, we propose some fuzzy Hough transforms for track initiation in clutter environment. In order to consider the effect of the neighboring feature points , the fuzzy membership value is given to the point in a limited area. So , the fuzzy Hough transform may solve effectively the uncertainty of the data received from sensor measurements in high noisy, ang identify possible tracks. In Chapter 6, A hierarchical multisensor image fusion fuzzy algorithm with adaptive weights is presented. It provides a powerful way to represent and process imprecise and uncertain information, and it full utilizes the redundancy and complementarity between the informational sources, and the reliability of the informational sources themselves.