A method is provided. The method comprises: initializing a point mass filter; initializing the one or more Bayesian filters; obtaining measurement data associated with a horizontal position on a surface; obtaining measurement data of a horizontal position and a velocity; obtaining geo-mapping data; estimating, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data; estimating, with the one or more Bayesian filters, remaining state parameters based upon an output of the point mass filter and the measurement data; predicting, with the point mass filter, an a priori horizontal position, on a surface for a future time when the next measurement data will be obtained; and predicting, with the one or more Bayesian filters, an a priori remaining state parameters for a future time when the next measurement data will be obtained.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. 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; at least one sensor coupled to the processing system; wherein the memory comprises geo-mapping data, 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; and 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. 2. The data-based referenced navigation system of claim 1, wherein the models comprise a state equation and a measurement equation. 3. The data-based referenced navigation system of claim 1, wherein the at least one Bayesian filter comprises at least one Kalman filter. 4. The data-based referenced navigation system of claim 1, wherein the inertial navigation system comprises at least one at inertial measurement unit coupled to a processor. 5. The data-based referenced navigation system of claim 1, wherein the at least one sensor comprises at least one of a barometric altimeter, a radar altimeter, an inertial measurement unit including at least one of an accelerometer and a gyroscope, a pressure sensor, a magnetometer, an accelerometer, a gravity gradiometer, a gravimeter, a water depth sensor, a bathymetric echo-sounder, a camera-type sensor, and a star tracker. 6. The data-based referenced navigation system of claim 1, wherein the geo-mapping data comprises at least one of terrain geo-mapping data, bathymetric geo-mapping data, gravity field geo-mapping data, gravity gradient field geo-mapping data, magnetic field geo-mapping data, stellar image-based geo-mapping data, and terrestrial image-based geo-mapping data. 7. The data-based referenced navigation system of claim 1, wherein the models comprise: x k + 1 = [ x k + 1 n x k + 1 l ] = [ F β γ α ]  x k + w k z k =  h ~  ( x k n + x ^ k DR ) + Hx k l + v k =  h  ( x k n ) + Hx k l + v k . 8. The data-based referenced navigation system of claim 7, wherein β = [ 0 0 0 0 ]   and   γ = [ 0 0 0 0 ] . 9. The data-based referenced navigation system of claim 1, wherein the estimator is configured to: initialize the point mass filter; initialize the one or more Bayesian filters; obtain measurement data from at least one of the one or more sensors, and the inertial navigation system; obtain geo-mapping data; estimate, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data; and 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, an a priori horizontal position, on a surface for a future time when the next measurement data will be obtained; and predict, with the one or more Bayesian filters, a priori remaining state parameters for a future time when the next measurement data will be obtained. 10. A method of estimating horizontal position, comprising: initializing a point mass filter; initializing the one or more Bayesian filters; obtaining measurement data associated with at least one of a horizontal position on a surface, and a horizontal position and a velocity; obtaining geo-mapping data; estimating, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data; estimating, with the one or more Bayesian filters, remaining state parameters based upon an output of the point mass filter and the measurement data; predicting, with the point mass filter, an a priori horizontal position, on a surface for a future time when the next measurement data will be obtained; and predicting, with the one or more Bayesian filters, an a priori remaining state parameters for a future time when the next measurement data will be obtained. 11. The method of claim 10, further comprising determine if consistency testing is satisfied; if consistency testing is satisfied, then performing the estimating with the point mass filter, the estimating with the one or more Bayesian filters, the predicting with the point mass filter, and the predicting with the one or more Bayesian filters; and if consistency testing is not satisfied, then performing the predicting with the point mass filter, and the predicting with the one or more Bayesian filters. 12. The method of claim 10, wherein estimating, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data comprises estimating, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and measurement data associated with the horizontal position on the surface; and wherein predicting, with the point mass filter, the a priori horizontal position, on a surface for the future time when the next measurement data will be obtained comprises predicting, with the point mass filter, the a priori horizontal position, on the surface based upon measured data from an inertial navigation system for the future time when the next measurement data will be obtained. 13. The method of claim 10, 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. 14. The method of claim 10, wherein obtaining measurement data associated with at least one of the horizontal position on the surface, and the horizontal position and the velocity comprises obtaining measurement data that is measured altitude over the surface. 15. The method of claim 10, wherein initializing the one or more Bayesian filters comprises initializing mean and variance grids of one or more Kalman filters; and wherein estimating, with the one or more Bayesian filters, the remaining state parameters based upon the output of the point mass filter and the measurement data comprises estimating, with the one or more Kalman filters, the remaining state parameters based upon the output of the point mass filter and the measurement data; and 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 predicting, with the one or more Kalman filters, the a priori remaining state parameters for the future time when the next measurement data will be obtained. 16. The method of claim 10, wherein initializing the point mass filter comprises initializing a probability density function of a point mass function grid; and wherein the probability density function of the point mass filter is represented by: p  ( x 0 n  z - 1 ) ≅ ∑ i  P 0  - 1 ( i )  ( ξ 0 ( i ) )  S  { x 0 n  :  ξ 0 ( i ) , δ } , where ξ0(i) is an i-th grid point, P0|−1(i)(ξ0(i))=p(ξ0(i)|z−1)=N{ξ0(i):{circumflex over (x)}0|−1n,P0|−1n} is the value of an initial, predicted probability density function at the i-th point, and S{x0n:ξ0(i),δ} is a piece-wise constant function of variable xkn at the point ξk(i) within the interval δ. 17. The method of claim 10, estimating, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data comprises determining: p  ( x k n  z k ) ≅ ∑ i  P k  k ( i )  ( ξ k ( i ) )  S  { x k n  :  ξ k ( i ) , δ } where  : P k  k ( i )  ( ξ k ( i ) ) = c - 1  P k  k - 1 ( i )  ( ξ k ( i ) )  P mLike , k ( i ) c = ∑ j = 1 N  P k  k - 1 ( j )  ( ξ k ( j ) )  P mLike , k ( j ) 18. The method of claim 10, wherein estimating, with the one or more Bayesian filters, remaining state parameters based upon the output of the point mass filter and the measurement data comprises estimating, with the one or more Bayesian filters, errors of at least one of the one or more sensors, the map data, and the inertial navigation system based upon the output of the point mass filter and the measurement data; and 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 predicting, with the one or more Bayesian filters, the a priori errors of at least one of the one or more sensors, the map data, and the inertial navigation system for the future time when the next measurement data will be obtained. 19. A system, comprising: a moveable object comprising a data-based reference navigation system; wherein the data-based reference navigation system comprises: a processing system comprising a memory coupled to a processor; at least one inertial measurement unit coupled to the processing system; at least one sensor coupled to the processing system; wherein the memory comprises geo-mapping data, models, measurement data, and an estimator; wherein the measurement data comprises data measured by the at least one inertial measurement unit, 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; and wherein the processing system is configured to estimate a horizontal position. 20. The moveable object of claim 19, wherein the one or more sensors comprises at least one of a barometric altimeter, a radar altimeter, an inertial measurement unit including at least one of an accelerometer and a gyroscope, a pressure sensor, a magnetometer, a gravity gradiometer, an accelerometer, gravimeter, a water depth sensor, bathymetric echo-sounder, a camera-type sensor, and a star tracker; and wherein the geo-mapping data comprises at least one of terrain geo-mapping data, bathymetric geo-mapping data, gravity field geo-mapping data, gravity gradient field geo-mapping data, magnetic field geo-mapping data, stellar image-based geo-mapping data, and terrestrial image-based geo-mapping data.