الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
This thesis introduces an automatic radar tracking system with multiple observ aticns from multistatic radars using particle filter as a nonlinear predictor for data fusion and prediction. The algorithm .is based on using particle filter instead of using the linear or non-linear Kalman filters. Particle filtering, also known as sequential Monte Carlo is an attractive estimation procedure for non-linear dynamic systems. Recently, several popular methods such as forward backward and maximum a-posteriori smoothers have been introduced into the literature. These techniques involve a re-computation of the discrete distribution obtained from the particle filter. While the smoother offers an improvement in the estimation, there is a significant computation cost that often makes this step unattractive in practice.
The system is simulated using Matlab to compare the performance of the estimation routines of both the Kalman and particle filters, and particle filter with and without smoothers. The processing time is also studied. Simulation results show that the Kalman filter improves the automatic tracking system with multiple observations. Particle filter improves the fusion and prediction estimate of the non-linear moving object in presence of measurement errors. On the other hand, this thesis is devoted to propose data fusion algorithms into multistatic radar network to improve its tracking capability. The proposed data fusion algorithm is based on using common measurement architecture and gives state estimates followed by cumulative measurement fusion or cumulative state vector fusion algorithm which is very simple, easy to implement and can be used in real time. Extended Kalman filter is used as a non-linear tracking and predictor algorithm. The system is simulated using Matlab to compare the performance of the estimation routines of both fusion algorithms and the targets scenario is simulated using Monte Carlo simulation. Simulation results have shown that, these cumulative fusion algorithms improve the multi static radar network tracking capability and procuce a significant reduction in the root sum square error, absolute error, and root sum square variance than achieved from mono static radar.