Honeywell International Inc.

Milos Vesely

Milos Sotak

Jindrich Dunik

Wesley J Hawkinson

Yunqian Ma

Vibhor L Bageshwar

Kenneth Steven Morgan

Jan Lukas

Michal Dobes

Nuri Kundak

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.

Apparatus and method for terrain aided navigation using inertial position

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.

Apparatus and method for data-based referenced navigation

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.

Apparatus and method for performing grid adaption in numerical solution of recursive bayesian estimators

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.

Unmanned aircraft systems sense and avoid sensor fusion track initialization

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.

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Nemcice, Czechia

Milos Vesely