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Multi-sensor information fusion based Target Recognition Algorithm

Author: ZhuNing
Tutor: WangQingChao
School: Harbin Institute of Technology
Course: General and Fundamental Mechanics
Keywords: Multi-sensor information fusion Air target identification Neural Network Evidence Theory Multi-class classification Support Vector Machine
CLC: TP202
Type: Master's thesis
Year: 2008
Downloads: 136
Quote: 2
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Abstract


Modern warfare, air defense battle command target recognition is fast, accurate, one of the key decisions . Aerial target recognition directly affect the success of the deployment of anti-aircraft fire , distribution, and effective strikes, then that would affect the entire battlefield . Therefore, the target recognition technology for improving the performance of the entire air defense system has important significance. Based on multi-sensor information fusion technique to study the air target identification method and its application. First introduced the theory of multi-sensor information fusion , analysis of the existing evidence combination method , and that its shortcomings , then the evidence for different focal element in the conflict between the degree of research , based on the mutual confidence based conflicting evidence combination method for aerial target identification. Simulation results show that : The synthesis of the new approach is good, even better synthesis result . For multi-sensor information fusion basic probability assignment problems are difficult to obtain , given a neural network technology to obtain basic probability assignment methods, and on this basis, the evidence presents a combination of theory and neural networks for target recognition fusion method . Focuses on the support vector machine (SVM) in a multi- sensor aerial target recognition . SVM is a statistical learning theory developed on the basis of a new generation learning algorithm based on structural risk minimization (SRM criterion ) , using the empirical risk and the confidence range two while minimizing the risk as functional . Several multi- class classification by support vector machines , will DAGSVM applied target identification. Simulation study of the neural network, neural network and evidence theory combining recognition algorithm , DAGSVM performance of the three algorithms , the results show that SVM DAG noise in any case , have the highest recognition rate, especially in large background noise under identify the more obvious advantages , indicating support vector machine in the air, the effectiveness of target recognition . On the other hand , in DAGSVM adding parallel algorithms, simulation results show that the algorithm speed has been effectively improved.

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