Abstract: A system and method for tracking an object receive electronic intelligence (ELINT) track information related to the object and radar track information. A first likelihood that the radar track information is also related to the object is determined at a first time of the ELINT track information and a first time of the radar track information. A second likelihood that the radar track information is also related to the object is determined at a second time of the ELINT track information and a second time of the radar track information. The first likelihood and the second likelihood are processed to determine whether the ELINT track information and the radar track information should be associated as both being related to the object.

Abstract: A system including one or more cloud computing units and a ground unit. The one or more cloud computing units may be configured to process video content. The ground unit may be configured to pre-process the video content and deliver the video content to the one or more cloud computing units.

Abstract: A system and method for tracking an object receive electronic intelligence (ELINT) track information related to the object and radar track information. A first likelihood that the radar track information is also related to the object is determined at a first time of the ELINT track information and a first time of the radar track information. A second likelihood that the radar track information is also related to the object is determined at a second time of the ELINT track information and a second time of the radar track information. The first likelihood and the second likelihood are processed to determine whether the ELINT track information and the radar track information should be associated as both being related to the object.

Abstract: Aspects of the present invention relate to a system (10) and a method for tracking one or more targets by a radar using a multiple hypothesis tracking (MHT) algorithm, the method including operating the radar to transmit a radar beam from a first location toward the one or more targets, operating the radar to receive a plurality of return signals at the first location from the one or more targets, and to generate a plurality of observations for a single radar dwell respectively corresponding to the plurality of return signals, and processing the plurality of observations in accordance with the MHT algorithm for at least two passes such that more than one of the plurality of observations are associated with a single track of the one or more targets.

Abstract: Embodiments described herein are directed to multiple hypothesis systems and methods for tracking observations that are domain agnostic and involves determining the probability that a given set of observations (i.e., a track) corresponds to a particular target, object or linked set of events. One embodiment described herein relates to cyber security tracking methods and systems.

Abstract: Aspects of the present invention relate to a system (10) and a method for tracking one or more targets by a radar using a multiple hypothesis tracking (MHT) algorithm, the method including operating the radar to transmit a radar beam from a first location toward the one or more targets, operating the radar to receive a plurality of return signals at the first location from the one or more targets, and to generate a plurality of observations for a single radar dwell respectively corresponding to the plurality of return signals, and processing the plurality of observations in accordance with the MHT algorithm for at least two passes such that more than one of the plurality of observations are associated with a single track of the one or more targets.

Abstract: Methods for resolving radar ambiguities using multiple hypothesis tracking are described. One such method includes (a) choosing a single waveform for each of a plurality of dwells of a first scan, wherein the single waveforms of consecutive scans are different, (b) generating the first scan using the single waveform for each of the dwells of the first scan, (c) receiving observation data as a result of the first scan, the observation data comprising measured positions of true targets and false targets, (d) generating, using multiple hypothesis tracking, position predictions for true targets and false targets, (e) comparing the predicted positions and measured positions, repeating (a)-(e) until a preselected process condition is met, and determining the true targets based on the results of the comparisons.

Abstract: Embodiments described herein are directed to multiple hypothesis systems and methods for tracking observations that are domain agnostic and involves determining the probability that a given set of observations (i.e., a track) corresponds to a particular target, object or linked set of events. One embodiment described herein relates to cyber security tracking methods and systems.

Abstract: A method for measuring sensor bias and a method for estimating sensor bias is provided. Road location data is obtained. Sensor observation data is obtained. A sensor observation is identified that corresponds to an on-road target moving on a known road. A measurement of one-dimensional sensor bias is formed. As one-dimensional sensor bias measurements accumulate, multi-dimensional sensor bias is estimated. Sensor bias estimates may then be incorporated into a multiple hypothesis tracker system.

Abstract: Methods for resolving radar ambiguities using multiple hypothesis tracking are described. One such method includes (a) choosing a single waveform for each of a plurality of dwells of a first scan, wherein the single waveforms of consecutive scans are different, (b) generating the first scan using the single waveform for each of the dwells of the first scan, (c) receiving observation data as a result of the first scan, the observation data comprising measured positions of true targets and false targets, (d) generating, using multiple hypothesis tracking, position predictions for true targets and false targets, (e) comparing the predicted positions and measured positions, repeating (a)-(e) until a preselected process condition is met, and determining the true targets based on the results of the comparisons.

Abstract: Embodiments described herein are directed to multiple hypothesis systems and methods for tracking observations that are domain agnostic and involves determining the probability that a given set of observations (i.e., a track) corresponds to a particular target, object or linked set of events. One embodiment described herein relates to cyber security tracking methods and systems.

Abstract: Embodiments described herein are directed to multiple hypothesis systems and methods for tracking observations that are domain agnostic and involves determining the probability that a given set of observations (i.e., a track) corresponds to a particular target, object or linked set of events. One embodiment described herein relates to cyber security tracking methods and systems.

Abstract: A method for measuring sensor bias and a method for estimating sensor bias is provided. Road location data is obtained. Sensor observation data is obtained. A sensor observation is identified that corresponds to an on-road target moving on a known road. A measurement of one-dimensional sensor bias is formed. As one-dimensional sensor bias measurements accumulate, multi-dimensional sensor bias is estimated. Sensor bias estimates may then be incorporated into a multiple hypothesis tracker system.

Abstract: A method for automatic group tracking detects target formations and group tracks corresponding to each formation. Pseudo-observations are formed to represent missing observations in each track group. The pseudo-observations update the track states of undetected targets. Track validity estimates eliminate false tracks resulting from series of pseudo-observations. A group average velocity applied to each track in the group helps to maintain velocity stability. The operator has the option of suppressing the display of all tracks except for the leader of each group to eliminate the distraction of intra-group switching. This method can be applied to trackers that maintain a single track for each target or to trackers that maintain multiple track branches on each target, such as MHT.

Abstract: A multiple hypothesis tracking system (10) which generates a substantially continuous output to a system user. The multiple hypothesis tracking system (10) generates a primary set of tracks (12) which best represents the expected number of targets of interest in a cluster. For multiple sensor applications, a secondary set of tracks is generated having a less probability than the tracks in the primary set. A knowledge of track data is maintained from one scan to a subsequent scan such that tracks can be merged and deleted (14). A universal track file (16) is generated in which track associations from one scan to a subsequent scan are correlated such that the output of the track file remains consistent with respect to the number of tracks.