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Research on Technology in Identification of Aerial Targets Based on Support Vector Machine

Author: PengYuXing
Tutor: WangHongQiang
School: National University of Defense Science and Technology
Course: Information and Communication Engineering
Keywords: Support Vector Machine (SVM) K- nearest neighbor ( KNN ) The fast SVM classifier ( FCSVM ) Doppler spectrum Target recognition Accumulation strategy
CLC: TN953
Type: Master's thesis
Year: 2009
Downloads: 62
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In this paper, support vector machine technology and its application in the air target identification . The first chapter describes the research background , air target identification research status , and support vector machine pattern classification research status . Second chapter first describes the support vector machine technology , summarized its characteristics and advantages , target recognition technology based on neural network , and then analyzed by simulation experimental comparison of the performance of these two target recognition technology , show that support vector machine technical superiority in the air target identification . Chapter III for fixed-wing and rotary-wing these two types of typical air target identification , first analyzes the difference of the Doppler spectrum structure , and then proposed based on the air target identification KNN - SVM algorithm , the algorithm uses the K - nearest neighbor ( KNN ) rotor target spectrum towards training sample set to filter , so that the level of support vector closer to the optimal classification surface ; Finally, accumulated to identify strategies and identification methods , this method can obtain more spectrum samples under similar conditions , high recognition rate. Simulation results demonstrate the effectiveness of the method . The fourth chapter, the the fast SVM in the air target identification . A fast SVM classification algorithm and its improved algorithm first introduced in the analysis on the basis of its shortcomings and deficiencies , to further improve the algorithm , an optimized fast SVM classification algorithm , simulation results show that this method makes the classification speed improve . Finally, a summary of the work of the full text pointed out that further research directions .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Radar > Radar tracking system
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