Monte Carlo techniques for prediction and filtering of non-linear stochastic processes
Automatica (Journal of IFAC)
Computers & Mathematics with Applications
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Multisensor navigation data synthesis (MNDS) is the process of fusing outputs from inertial sensors with information from other sensors and information processing blocks into one representational form. This technique is anticipated to accomplish enhanced accuracy and more specific inferences than could be achieved by the use of a single sensor alone. Therefore, this research work expounds innovative filtering methodology for the multisensor navigation data synthesis for a ballistic missile application that augments navigation system performance. The premise and characteristics of strapdown inertial navigation system (SINS) integrated with the astronavigation system (ANS) based on the unscented particle filter (UPF) are investigated in this paper. Configuration of the integrated navigation system is presented with its canonical model, and system dynamic and stochastic models required for the filtering algorithm are presented. To exemplify integrated navigation filter mechanization is the foremost aspiration of this research. To validate and corroborate the designed MNDS technique, simulations are carried out that demonstrate the validity of this method on enhancing the navigation system's accuracy with estimation and compensation for the gyro's drift. This integrated system results in a significant reduction in impact-point dispersion of a re-entry vehicle.