Abstract: Using geo-mapping data and only at least one inertial sensor, a Bayesian estimator uses at least one element of an uncorrected inertial navigation vector, of a moveable object on a surface, to estimate inertial navigation error information including corresponding statistical information. The estimated inertial navigation error information is used to error correct the uncorrected inertial navigation vector. The Bayesian filter also predicts inertial navigation error information, including corresponding statistical information, for a future time instance when a next uncorrected inertial navigation vector will be obtained.

Abstract: A method for determining fleet wide integrity comprises forming a user measurement block for each user in a fleet network, the user measurement block comprising: navigation measurements received by the user, relative user measurements received by the user from other users, and navigation state and integrity solutions. The user measurement block is signed and sent to all other users for validation. Each user executes a numerical process to determine a navigation state and integrity of all users. A fleet state block is formed comprising: a header including a hash of last valid fleet state block header, a nonce-proof of work, and a root hash of Merkle tree; and a data block including measurement, state, and integrity information of the fleet. The fleet state block is sent to all other users for validation. If the fleet state block passes validation, the user forms a chain of fleet state blocks.

Abstract: Using geo-mapping data and only at least one inertial sensor, a Bayesian estimator uses at least one element of an uncorrected inertial navigation vector, of a moveable object on a surface, to estimate inertial navigation error information including corresponding statistical information. The estimated inertial navigation error information is used to error correct the uncorrected inertial navigation vector. The Bayesian filter also predicts inertial navigation error information, including corresponding statistical information, for a future time instance when a next uncorrected inertial navigation vector will be obtained.

Abstract: 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.

Abstract: A system is provided. The system comprises: a processing system comprising a memory coupled to a processor; wherein the processing system is configured to be coupled to at least one sensor; wherein the memory comprises a grid adaptation system, a system model, measurement data, and an estimation system; wherein the measurement data comprises data measured by the at least one sensor; wherein the estimation system is configured to provide probability density functions (PDFs) for a predictive estimate and a filtered estimate of a state in a form of a point-mass density; and wherein the grid adaption system is configured to adapt grid parameters of a predictive estimate and a filtered estimate.

Abstract: A system is provided. The system comprises: a processing system comprising a memory coupled to a processor; wherein the processing system is configured to be coupled to at least one sensor; wherein the memory comprises a grid adaptation system, a system model, measurement data, and an estimation system; wherein the measurement data comprises data measured by the at least one sensor; wherein the estimation system is configured to provide probability density functions (PDFs) for a predictive estimate and a filtered estimate of a state in a form of a point-mass density; and wherein the grid adaption system is configured to adapt grid parameters of a predictive estimate and a filtered estimate.

Abstract: A method is provided. The method comprises: initializing at least one non-linear filter configured to provide at least one predictive measurement estimate probability density function; obtaining measurement data; determining if the measurement data is consistent; and if the measurement data is consistent, then estimating at least one state parameter with the at least one non-linear filter using the measurement data.

Abstract: 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.

Abstract: A method to initialize tracks from sensor measurements is provided. The method includes identifying at least one tentative track based on data collected from at least one sensor at three sequential times; initializing a confirm/delete track filter for the identified tentative tracks; and using gates computed from state vector statistics to one of: confirm the at least one tentative track; reprocess the at least one tentative track; or delete the at least one tentative track.

Abstract: Systems and methods for 3D data based navigation using a watershed method are provided. In at least one embodiment, a method for segmenting three-dimensional frames of data comprises acquiring at least one frame from at least one sensor, wherein the at least one frame provides a three-dimensional description of an environment containing the at least one sensor; and identifying a surface in the at least one frame. The method further comprises computing at least one residual map for the at least one frame based on the orthogonal distance from data points on the surface to at least one polynomial surface fitted to the surface; and segmenting the at least one residual map by performing a watershed algorithm on the residual map.

Abstract: A method to initialize tracks from sensor measurements is provided. The method includes identifying at least one tentative track based on data collected from at least one sensor at three sequential times; initializing a confirm/delete track filter for the identified tentative tracks; and using gates computed from state vector statistics to one of: confirm the at least one tentative track; reprocess the at least one tentative track; or delete the at least one tentative track.

Abstract: Systems and methods for 3D data based navigation using a watershed method are provided. In at least one embodiment, a method for segmenting three-dimensional frames of data comprises acquiring at least one frame from at least one sensor, wherein the at least one frame provides a three-dimensional description of an environment containing the at least one sensor; and identifying a surface in the at least one frame. The method further comprises computing at least one residual map for the at least one frame based on the orthogonal distance from data points on the surface to at least one polynomial surface fitted to the surface; and segmenting the at least one residual map by performing a watershed algorithm on the residual map.