Dissertation > Excellent graduate degree dissertation topics show

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
Quote: 0
Read: Download Dissertation

Abstract


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 .

Related Dissertations

  1. ISAR Imaging Simulation of Space Targets and Target Recognition Based on ISAR Images,TN957.52
  2. Research on Autamatic Music Structrue Analysis,TN912.3
  3. Study on Virtual Detector of Infrared Hyper-Spectral Image,TP391.41
  4. Research on the Image Real-Time Acquisition, Storage and Image Processing System,TP391.41
  5. Research for Infrared Image Target Identification and Tracking Technology,TP391.41
  6. The Compression and Fusion Technique Research of Underwater Target Feature,TN911.7
  7. Study on the Road Condition Monitoring Based on Vehicular 3D Acceleration Sensor,TP274
  8. Research on Subimage Selection and Mathching Method for Synthetic Aperture Radar(SAR) Target Recognition,TN957.52
  9. Design and Implementation of Intelligent Transportation Video Monitoring System Based on 3G Network,TP391.41
  10. Research on Specific Target Recognition Algorithms in Video Surveillance,TP391.41
  11. Development and Application Research on a Video-based Motion Tracking and Analysis System for Animals,TP391.41
  12. Integration of Spatial Information Bag of Feature in Image Annotation,TP391.41
  13. Targets Recognition in High-Resolution Remote Sensing Image,TP751
  14. Multi-camera target detection and tracking method,TP391.41
  15. Underwater Manipulator information fusion and operations planning studies,TP241
  16. Based on the visual system of unmanned autonomous helicopter landing,V279
  17. Based on the model and features of the package ML_pLSA Target Recognition Algorithm,TP391.41
  18. Servers based on infrared image classification conditions,TP393.05
  19. Based on infrared imaging technology research for Ship Identification,TP391.41
  20. Automatic Target Recognition fluorescent magnetic particle inspection image processing techniques,TP391.41
  21. Local learning based super -pixel image target recognition,TP391.41

CLC: > Industrial Technology > Radio electronics, telecommunications technology > Radar > Radar tracking system
© 2012 www.DissertationTopic.Net  Mobile