A data-based referenced navigation system, comprising: a processing system comprising a memory coupled to a processor; an inertial navigation system coupled to the processing system; wherein the inertial navigation system comprises at least one inertial measurement unit, IMU, and where each IMU comprises at least one gyroscope and at least one accelerometer; at least one sensor coupled to the processing system; wherein the memory comprises geo-mapping data being a representation of a distribution of a parameter over a surface, models, measurement data, and an estimator; wherein the measurement data comprises data measured by at least one of the inertial navigation system and the at least one sensor; wherein the estimator is a Rao-Blackwellized point mass filter which comprises a point mass filter, and at least one Bayesian filter; wherein the processing system is configured to estimate a horizontal position on a surface based upon the geo-mapping data, the models, the measurement data, and the estimator; and wherein the estimator is configured to: estimate, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data; estimate, with the one or more Bayesian filters, remaining state parameters based upon an output of the point mass filter and the measurement data; predict, with the point mass filter, a priori horizontal position, on the surface for a future time when next measurement data will be obtained; and predict, with the one or more Bayesian filters, the a priori remaining state parameters for the future time when the next measurement data will be obtained, wherein predicting, with the one or more Bayesian filters, the a priori remaining state parameters for the future time when the next measurement data will be obtained comprises: constructing a new grid; updating the point mass filter with the new grid; and updating the one or more Bayesian filters with the new grid. a processing system comprising a memory coupled to a processor; an inertial navigation system coupled to the processing system; wherein the inertial navigation system comprises at least one inertial measurement unit, IMU, and where each IMU comprises at least one gyroscope and at least one accelerometer; at least one sensor coupled to the processing system; wherein the memory comprises geo-mapping data being a representation of a distribution of a parameter over a surface, models, measurement data, and an estimator; wherein the measurement data comprises data measured by at least one of the inertial navigation system and the at least one sensor; wherein the estimator is a Rao-Blackwellized point mass filter which comprises a point mass filter, and at least one Bayesian filter; wherein the processing system is configured to estimate a horizontal position on a surface based upon the geo-mapping data, the models, the measurement data, and the estimator; and wherein the estimator is configured to: estimate, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data; estimate, with the one or more Bayesian filters, remaining state parameters based upon an output of the point mass filter and the measurement data; predict, with the point mass filter, a priori horizontal position, on the surface for a future time when next measurement data will be obtained; and predict, with the one or more Bayesian filters, the a priori remaining state parameters for the future time when the next measurement data will be obtained, estimate, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data; estimate, with the one or more Bayesian filters, remaining state parameters based upon an output of the point mass filter and the measurement data; predict, with the point mass filter, a priori horizontal position, on the surface for a future time when next measurement data will be obtained; and predict, with the one or more Bayesian filters, the a priori remaining state parameters for the future time when the next measurement data will be obtained, wherein predicting, with the one or more Bayesian filters, the a priori remaining state parameters for the future time when the next measurement data will be obtained comprises: constructing a new grid; updating the point mass filter with the new grid; and updating the one or more Bayesian filters with the new grid. constructing a new grid; updating the point mass filter with the new grid; and updating the one or more Bayesian filters with the new grid.