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Cooperative Estimation of Agile Target Acceleration Using Extended Kalman Filter for Use in Advanced Guidance Laws

Parsayi Moghadam, Reza | 2017

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52557 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Saghafi, Fariborz
  7. Abstract:
  8. In this document, the cooperative estimation of states between two interceptors and one target is expressed in order to evaluate the effect of information sharing in different situations on the estimation performance. Due to the importance of the proper estimation of the target acceleration in advanced navigation rules, the main focus of the document is on a more precise estimation of the absolute states of the target. For this purpose, a filter based on the extended Kalman filter was used as an estimator, and to evaluate the promotion of the estimation performance, the combination of measured data was used in two different ways. Matlab Simulink is the simulation software used. The dynamics of interceptors and the target were modeled in a two-dimensional and ideal model, and on the basis of this dynamics and using the augmented proportional navigation guidance law, the flight simulation was completed. Then, the navigation guidance loop is implemented and the required subsystems are modeled. Then, the relative dynamics of the interceptor and the target is calculated, and using the same dynamics, first, the one-to-one estimation filter will be developed based on the extended Kalman filter. Then, by modifying the Jacobin-based linearized model, an innovative filter dynamic model is introduced and implemented based on the state space behavior. The results of the performance show the improvement of the estimator behavior in critical states in comparison with the reference model in the extended Kalman filter. Then, in the following chapter, using the same method for the cooperative flight of two interceptors, the relative dynamics and data sharing methods are extracted, and the cooperative estimation Kalman filter is presented with the help of new dynamics, and its performance will be evaluated for different modes. The last part of this report is about comparing the performance of one-to-one and cooperative filters. First, assuming identical seekers for interceptors and then, with the assumption of cooperative seekers being weaker, the performance of the filters is controlled and reported and then, the effect of electronic jamming on the performance of cooperative evaluation filter is considered as a serious factor of jamming, and then, given different accuracy of seekers, the predictor's performance is simulated. At the end of this chapter, we will examine the accuracy of the results of the past sections in various flight conditions. Finally, in the final chapter, the report is summarized, and the final results of this project are presented
  9. Keywords:
  10. Estimation ; Extended Kalman Filter ; Cooperative Flight ; Target Acceleration Estimation ; Augmented Proportional Navigation ; Multi Sensor Estimation

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