The Study On The Target Signals Attribute Fusion Of Airborne Multi-sensor

With the battlefield getting increasingly complex, it is significant to study airborne multi-sensor target identification in a complex interference environment in order to improve performance of aircraft weapon system. So, in this paper, the attribute fusion problem of airborne multi-sensor is mainly studied. The results are as following:1) Firstly, based on an analysis of the problems of attribute fusion in a complex interference environment, such as uncertainty of fusion information to make the system's robustness and self-adaptation worse, a self-adaptive fusion model is brought forward. The model is constituted by three parts: uncertain information processing, classified information fusing and knowledge updating. The knowledge updating utilizes intelligence technology to meet the requirements of environmental self-adaptation and robustness of data fusion system working in a changeful environment and information level. Based on that model, this paper studies foundation of information-updating module and then prensents knowledge's intellectualized analysis and consequence optimization by ant colony algorithm to realize self-adaptation management of knowledge and updating in a complex environment. To improving algorithm, the paper researches amelioration problem of ant colony algorithm, and then presents Ant System with Bit Climbing Aberrance, which combine ant colony algorithm with part-search to improve arithmetic's evening calculative efficiency.2) In view of attribute fusion problems in data level, this paper researches different algorithm presented by T.Fukuda and Luo R.C and then presents grouping fusion algorithm based on consistency and reliability of sensors' information. This algorithm amends classification estimation of the classification confidence to reduce the estimation error of object's classification confidence made by measure error; classes sensors by consistency measure and estimates the reliability of different sensor groups by the sensors' transcendent reliability; lastly, chooses the best sensor group of reliability to fuse, which can amend influence of conflict information. This algorithm combines uncertain information processing with the fusion structure organically, which enhances anti-interference capability of attribute fusion.3) In view of attribute fusion problem of hard-decision, this paper studies algorithm optimization of the distributed detection system when object transcendent information was known, such as searching algorithm and mixed searching algorithm. After discovering that these algorithms are inferior-optimizing, this paper presents Bayesian global-optimization algorithm based on Ant System with Bit Climbing Aberrance. For the complex environment and unknown background knowledge about targets, this paper discovers that subjective BAYES and NP algorithm is difficultly to improve the performance and self-adaptation at the same time, and then brings forward BAYES global-optimization algorithm based on consistency grouping, strategy of classified learning and Ant System with Bit Climbing Aberrance. This algorithm has self-adaptation of environment and is global-optimizing.4) In view of attribute fusion problem of soft-decision, on the base of fuzzy integral, this paper gives a new definition about density of fuzzy integral, presents an algorithm which makes use of Ant System with Bit Climbing Aberrance to ascertain Statistic integral density, uses Environment integral density to amend environment molestation and discovers that this algorithm can enhance self-adaptation of environment and performance of this algorithm is better, after contrasting performance of genetic algorithm and NN algorithm.5) Lastly, this paper applies the above algorithm to target identification in airborne battlefield environment and failure diagnosis of airborne flux- temperature sensors in the different condition of aviation and electromagnetism interference, which validates its effectiveness.