Study On Theory And Application Of Multi-Sensor State Fusion Estimation

Due to the advent of the sensor technology and communication technique, multi-sensor systems have recently attracted considerable attention, especially, those with diversified complex using background. The emerging interest of research into multi-sensor information theory is viewed as timely since the multiformity, massiness, complex and real-time processing of information in multi-sensor system has gone beyond the human brain capacity of processing information.State fusion estimation is an important study field in the information fusion theory, mainly dealing with how to estimate the system state exactly by multi-sensors. It is usually applied in tracking system and other exact estimating systems.This dissertation considers state fusion estimation of multisensor information fusion theory. The main work of here is to solve the problems when fusion estimation theory is applied in practice.In details, the major contributions of this thesis are as the following:As ,we know, distributed suboptimal method need complex compute processing and can't be used in the system containing more than 3 sensors. The optimized algorithm is developed, which avoids computing correlated estimate covariance and has the advantages of the distributed structure. Meanwhile, the simulations show the developed algorithm has the similar performance as the classical distributed suboptimal fusion method.The present optimal distributed and centralized fusion methods are enriched and expanded. Two-levels and three levels algorithms are discussed in the more general system, which has control input and in which the processing noise and measurement noise are correlated Gaussian white noise with nonzero mean. The discussed algorithms have more universal formats and are easilygeneralized to the more-levels system.The dissertation defines and develops the covariance performancefunction and proves that it can decide the fusion estimate covariance. The simulations identify the conclusions.The dissertation studies the fusion estimation of uncertainty multisensor system. Two uncertain models and corresponding centralized robust fusion estimate methods are discussed. The simulations compared different fusion methods detailedly in frequency and time fields. Moreover., it's proved that with the exact transforming condition, robust centralized estimate can be transformed to the distributed fusion method with the same fusion estimate performance.The dissertation systemically studies the multisensor system with correlated measurement noise. When the measurement noise covariance is certain matrix that can be transformed to a diagonal matrix by matrix resemble transform, the dissertation develops optimal centralized and distributed fusion estimate. For the other systems, the decomposed-combined fusion estimation method is discussed. The simulations show that the developed algorithm can obtain a better performance than the general robust fusion estimation methods for the multiserisors system with correlated measurement noise.As to the application of information fusion theory, the state fusion estimation methods are applied in the basis weight estimation of exact paper machine. The paper studies several practical situations and points out that the estimate results can develop if more sensors are used, even though when some of them fail. But if the measurement noises are correlated, the estimation methods must be chosed correctly, or else the estimation performance may decline.