Honeywell International Inc.

Honeywell Interntaional Inc.

Eric A Euteneuer

Michael Ray Elgersma

Vibhor L Bageshwar

Ruy C Brandao

Christine Marie Haissig

Ruy L Brandao

John R Weed

Dongsong Zeng

Eduard Shestak

Dale Frederick Enns

Paul Samanant

Steve Pratt

Christine M Haissig

In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes obtaining measurements corresponding to a first object with at least one sensor, the at least one sensor providing one or more first track IDs for the measurements. A Tfirst predicted intensity is generated for the first object based on a Tfirst track intensity. A Tmeasurement from a first sensor of the at least one sensors is obtained, the first sensor providing a second track ID for the Tmeasurement. The second track ID is compared to the one or more first track IDs, and the Tfirst predicted intensity is selectively updated with the Tmeasurement based on whether the second track ID matches any of the one or more first track IDs to generate a Tfirst measurement-to-track intensity for the first object.

Updating intensities in a PHD filter based on a sensor track ID

In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes providing a plurality of intensities. The plurality of intensities are pruned based on their respective weight to remove lower weighted intensities and produce a first set of intensities. Intensities in the first set of intensities that are identified as corresponding to the same object are then merged to produce a second set of intensities. The second set of intensities is then pruned again to produce a final set of intensities, wherein pruning the second set of intensities includes in the final set of intensities, up to a threshold number of intensities having the largest weights in the second set of intensities and excludes from the final set of intensities any remaining intensities in the second set of intensities.

Two step pruning in a PHD filter

In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes generating a first intensity by combining a first one or more measurements, wherein a first set of track IDs associated with the first intensity includes track IDs corresponding to respective measurements in the first one or more measurements. A second intensity is generated by combining a second one or more measurements, wherein a second set of track IDs associated with the second intensity includes track IDs corresponding to respective measurements in the second one or more measurements. The first set of track IDs is compared to the second set of track IDs, and the first intensity is selectively merged with the second intensity based on whether any track IDs in the first set of track IDs match any track IDs in the second set of track IDs.

Merging intensities in a PHD filter based on a sensor track ID

In one embodiment, a method for tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes comparing second track IDs corresponding to newly obtained measurements to one or more first track IDs corresponding to a Tpredicted intensity having a predicted weight. If all of the one or more first track IDs match any of the second track IDs, the predicted weight is multiplied by a first value. If less than all of the one or more first track IDs match any of the second track IDs, the predicted weight is multiplied by a second value, wherein the second value is greater than the first value. The method then determines whether to prune the Tpredicted intensity based on the predicted weight after multiplying with either the first value or the second value.

Adjusting weight of intensity in a PHD filter based on sensor track ID

Systems and methods for calculating coherent bearing that allow one to reduce the number of TCAS antenna elements. In one embodiment, the bearing calculation takes into account signal-to-noise ratio (SNR) difference experienced from top and bottom antennas mounted on a vehicle.

TCAS bearing estimation with reduced antenna elements

Systems and methods are operable maintain a proscribed Self Separation distance between an unmanned aircraft system (UAS) and an object. In an example system, consecutive intruder aircraft locations relative to corresponding locations of a self aircraft are determined, wherein the determining is based on current velocities of the intruder aircraft and the self aircraft, and wherein the determining is based on current flight paths of the intruder aircraft and the self aircraft. At least one evasive maneuver for the self aircraft is computed using a processing system based on the determined consecutive intruder aircraft locations relative to the corresponding locations of the self aircraft.

Systems and methods for unmanned aircraft system collision avoidance

System, method and computer program product for determining bearing using ADS-B and TCAS standard reply bearing estimates are disclosed. In one embodiment, a method for determining bearing based upon ADS-B signals includes receiving ADS-B signals and standard transponder reply signals. A first bearing estimate is based on the ADS-B signal. A second bearing estimate is based on the standard transponder reply signals. A database is developed according to the first and second bearing estimates via the ADS-B and standard transponder reply signals. In one embodiment, where ADS-B signals are unavailable, associated ADS-B signals associated with the standard transponder reply previously stored in the database are used to determine bearing.

Methods and systems of determining bearing when ADS-B data is unavailable

A device for determining the bearing of a vehicle using Inertial Reference Unit (IRU) and Traffic Alert Collision Avoidance System (TCAS) data. The device includes a means to communicate with the vehicle such as a transmitter, receiver, and antenna. The device also includes a processor configured to receive the IRU and TCAS data from the vehicle via the communication means and then generate a bearing value using the received data.

Traffic alert collision avoidance system (TCAS) devices and methods

A position keeping system, useable to maintain a position of a vehicle in a vehicle formation, to keep all formation members flying at a specified velocity and altitude, and to perform commanded maneuvers, determines guidance corrections based on a track state referenced to a leader or a virtual leader of the formation and on a formation position error indicating the error in the position of the vehicle in the formation. The guidance corrections are required so as to maintain the vehicles in the formation.

Growing community of inventors

St. Anthony Village, MN, United States of America

Eric A Euteneuer