Dissertation > Excellent graduate degree dissertation topics show

Multiple Model Estimation with Uncertain Observation

Author: TangXiaoFang
Tutor: LiuMeiQin
School: Zhejiang University
Course: System Analysis and Integration
Keywords: randonmized unscented Kalman filter uncertain observation maneuvering target tracking multiple-model estimation
CLC: N945.1
Type: Master's thesis
Year: 2013
Downloads: 2
Quote: 0
Read: Download Dissertation


As one of the most essential technologies in the field of estimation, state estimation plays an important part in the fields of national defense and national economy. Kalman filter is the optimal estimator in linear systems, and the unscented Kalman flter (UKF) becomes an effective approach of nonlinear filtering. The multiple model estimation approaches become effective for target tracking. Observations contain uncertainties in practice. The uncertainties refer to the wrong measurements generated by sensors in clutter, or measurements missing beacause of temporary sensor failures etc. which can not be modeled by measurements noise. This kind of uncertainties will deteriorate the estimation or even make the estimators divergent when using standard filters.Based on the above background, we first introduce a nonlinear estimation approach named randomized unscented Kalman filter (RUKF). RUKF eliminates the system errors caused by the UKF. Thus the accuracy of the estimation is improved. Then from the characteristics of the interacting multiple model estimation (IMM) algorithm, we apply it on estimating the states in systems with uncertain observations. The model set in the proposed IMM approach containes two sub model-sets. Final estimates are obtained basing on the fusion of the two model-sets’ estimates. This approach improves the stability of the estimator in systems with uncertain observations and performs better. Besides, the approach performs well when applied in target tracking. The simulation results show the proposed approach performs better than the traditional one-model estimation approaches in systems with uncertain observation.

Related Dissertations

  1. Research of Data Fusion Algorithm for Multiple Sensor Target Tracking,TP202
  2. Uncertain Observation multisensor information fusion estimated ARMA signal,TP202
  3. Research on the Methods of Track Initiation and Maneuvering Target Tracking,TN953
  4. Study on the Technique of Information Fusion Applied to the Prediction of Landslide,P642.22
  5. Maneuvering target adaptive Kalman filter algorithm,TN959
  6. Particle filter based method of maneuvering target tracking,TN953
  7. Research on Tracking of Maneuvering Targets Based on New Particle Filter Algorithms,TN953
  8. Maneuver Detection and Tracking with the Radar Rate Measurement,TN953
  9. A Research on Tracking Algorithm of Multi-sensor Data Fusion Based on Interactive Multiple Models (IMM),TP212
  10. Application of Nonlinear Filtering in Ballistic Target Tracking,TJ012
  11. Short-range radar detection system in fast tracking method,TN958
  12. Key Technologies Research on Multi-target Tracking of Multi-sensor Data Fusion,TP202
  13. The modern multi- target tracking and multi-sensor integration of key technologies,TN953
  14. Research of Information Fusion Technology on Multi-sensor in Integrated Navigation,V249.32
  15. Kinematics single observer passive location and tracking key technology research,TN966
  16. Data Processing and Simulation of Integrated Navigation Systems,P228.4
  17. Maneuvering Target Tracking and Self-organizing Sensor Networks,TP212
  18. Research on Algorithms for Single Observer Passive Tracking with the Information of Spatial-Frequency Domain,TN953
  19. Research on the Technology for High Maneuvering Target Tracking,TN953
  20. The hybrid dynamic filtering theory and applications of linear / non-linear system,TN713

CLC: > SCIENCE AND > Journal of Systems Science > Systems Engineering > Systems Analysis
© 2012 www.DissertationTopic.Net  Mobile