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الأربعاء، 5 نوفمبر، 2014

A Proposed Target Tracking and Fusion Algorithms for Multistatic Radar Systems

Title: A Proposed Target Tracking and Fusion Algorithms for Multistatic Radar Systems  
Author        : Tarek Ahmed Reda Abd El-Shahid
Collection   : Ph.D. Electric
    Abstract:
This thesis introduces an automatic radar tracking system with multiple observations 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.

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