Abstract: A system for tracking targets. A sequence of sensor observations is processed with two thresholds, including a first threshold, and a second threshold, higher than the first threshold. Signals that exceed the first threshold are identified as low-confidence target detections and stored for possible future use. When a signal exceeds the higher second threshold, it is identified as a high-confidence detection, and one or more candidate tracks are formed, including the high-confidence detection and one or more low-confidence detections from within a neighborhood of the high-confidence detection.

Abstract: A system for tracking targets. A sequence of sensor observations is processed with two thresholds, including a first threshold, and a second threshold, higher than the first threshold. Signals that exceed the first threshold are identified as low-confidence target detections and stored for possible future use. When a signal exceeds the higher second threshold, it is identified as a high-confidence detection, and one or more candidate tracks are formed, including the high-confidence detection and one or more low-confidence detections from within a neighborhood of the high-confidence detection.

Abstract: The technology described herein includes a system and/or a method for global hypothesis tracking. In some examples, a method generates one or more paired segments based on track data representing kinematic data of target objects. Each paired segment includes a list of tracks incompatible with the paired segment, which are tracks sharing common track data, and a likelihood score. The method generates a transition probability between each pair of the paired segments based on the list of tracks incompatible with the paired segment and the likelihood score associated with each paired segment. The method further generates one or more multi-segment tracks based on the one or more paired segments also based on the transition probability between each pair of the paired segments.

Abstract: The technology described herein includes a system and/or a method for multiple hypothesis tracking. In some examples, a multiple hypothesis tracking system includes a receiver, an array generation module, and a local search move module. The receiver is configured to receive tracking data associated with one or more target families from one or more sensors. The array generation module is configured to generate a matrix array based on the tracking data. The local search move module is configured to perform one or more local search moves on one or more cells in the matrix array to increase a global hypothesis score for the tracking data.

Abstract: The technology described herein includes a system and/or a method for global hypothesis tracking In some examples, a method generates one or more paired segments based on track data. The method further generates one or more multi-segment tracks based on the one or more paired segments and generates a transition probability between each pair of the one or more paired segments.

Abstract: The technology described herein includes a system and/or a method for multiple hypothesis tracking In some examples, a multiple hypothesis tracking system includes a receiver, an array generation module, and a local search move module. The receiver is configured to receive tracking data associated with one or more target families from one or more sensors. The array generation module is configured to generate a matrix array based on the tracking data. The local search move module is configured to perform one or more local search moves on one or more cells in the matrix array to increase a global hypothesis score for the tracking data.

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 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.