Abstract: A vehicle detect and avoid system comprises a sensor arrangement including cooperative sensors, non-cooperative sensors, or combinations thereof. A detect and avoid module receives first measurement tracks from the cooperative sensors, and second measurement tracks from a first category of non-cooperative sensors. A correlator system communicates with the detect and avoid module, receives measurements from second or third categories of non-cooperative sensors, and outputs correlated measurement tracks. The detect and avoid module fuses the first measurement tracks from the cooperative sensors, the second measurement tracks from the first category of non-cooperative sensors, and the correlated measurement tracks, to estimate an optimal track for objects or targets in an environment around the vehicle. The optimal track for each of the objects or targets is used by the detect and avoid module to provide guidance to a guidance and/or control system so the vehicle remains well clear of the objects or targets.

Abstract: 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 Tk+1 first predicted intensity is generated for the first object based on a Tk first track intensity. A Tk+1 measurement from a first sensor of the at least one sensors is obtained, the first sensor providing a second track ID for the Tk+1 measurement. The second track ID is compared to the one or more first track IDs, and the Tk+1 first predicted intensity is selectively updated with the Tk+1 measurement based on whether the second track ID matches any of the one or more first track IDs to generate a Tk+1 first measurement-to-track intensity for the first object.

Abstract: 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.

Abstract: 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.

Abstract: 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 Tk+1 predicted 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 Tk+1 predicted intensity based on the predicted weight after multiplying with either the first value or the second value.

Abstract: 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.

Abstract: 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 Tk+1 first predicted intensity is generated for the first object based on a Tk first track intensity. A Tk+1 measurement from a first sensor of the at least one sensors is obtained, the first sensor providing a second track ID for the Tk+1 measurement. The second track ID is compared to the one or more first track IDs, and the Tk+1 first predicted intensity is selectively updated with the Tk+1 measurement based on whether the second track ID matches any of the one or more first track IDs to generate a Tk+1 first measurement-to-track intensity for the first object.

Abstract: 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.

Abstract: 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 Tk+1 predicted 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 Tk+1 predicted intensity based on the predicted weight after multiplying with either the first value or the second value.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.