Genetic model compensation: Theory and applications

The adaptive filtering algorithm known as Genetic Model Compensation (GMC) was originally presented in the author's Master's Thesis. The current work extends this earlier work. GMC uses a genetic algorithm to optimize filter process noise parameters in parallel with the estimation of the state and based only on the observational information available to the filter. The original stochastic state model underlying GMC was inherited from the antecedent, non-adaptive Dynamic Model Compensation (DMC) algorithm. The current work develops the stochastic state model from a linear system viewpoint, avoiding the simplifications and approximations of the earlier development, and establishes Riemann sums as unbiased estimators of the stochastic integrals which describe the evolution of the random state components. These are significant developments which provide GMC with a solid theoretical foundation.; Orbit determination is the area of application in this work, and two types of problems are studied: real-time autonomous filtering using absolute GPS measurements and precise post-processed filtering using differential GPS measurements. The first type is studied in a satellite navigation simulation in which pseudorange and pseudorange rate measurements are processed by an Extended Kalman Filter which incorporates both DMC and GMC. Both estimators are initialized by a geometric point solution algorithm. Using measurements corrupted by simulated Selective Availability errors, GMC reduces mean RSS position error by 6.4 percent, reduces mean clock bias error by 46 percent, and displays a marked improvement in covariance consistency relative to DMC.; To study the second type of problem, GMC is integrated with NASA Jet Propulsion Laboratory's Gipsy/Oasis-II (GOA-II) precision orbit determination program creating an adaptive version of GOA-II's Reduced Dynamic Tracking (RDT) process noise formulation. When run as a sequential estimator with GPS measurements from the TOPEX satellite and ground station network, this adaptive version of GOA-II reduces mean RSS position error by 5.9 percent and mean RSS velocity error by 3.7 percent relative to non-adaptive RDT. The success of this integration effort establishes GMC as a viable and widely applicable adaptive filtering algorithm.; Standard hypothesis testing techniques are used in both studies to establish the statistical significance of GMC's apparent superiority over conventional non-adaptive process noise formulations.