Abstract: An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.

Abstract: An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.

Abstract: An illustrative example method of classifying a detected object includes detecting an object, determining that an estimated velocity of the object is below a preselected threshold velocity requiring classification, determining a time during which the object has been detected, determining a first distance the object moves during the time determining a speed of the object from the first distance and the time, determining a second distance that a centroid of the detected object moves during the time, and classifying the detected object as a slow moving object or a stationary object based on a relationship between the first and second distances and a relationship between the estimated velocity and the speed.

Abstract: An illustrative example method of tracking a detected object comprises determining that a tracked object is near a host vehicle, determining an estimated velocity of the tracked object, and classifying the tracked object as frozen relative to stationary ground when the estimated velocity is below a preselected object threshold and a speed of the host vehicle is below a preselected host threshold.

Abstract: An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.

Abstract: An illustrative example method of tracking a moving object includes determining an initial pointing angle of the object from a tracking device, determining an estimated position of a selected feature on the object based upon the initial pointing angle, determining a velocity vector at the estimated position, determining a lateral acceleration at the estimated position based upon the velocity vector and a yaw rate of the object, determining a sideslip angle of the selected feature based on the lateral acceleration, and determining a refined pointing angle of the object from the determined sideslip angle.

Abstract: An illustrative example method of tracking a moving object includes determining a heading angle of a centroid of the object from a tracking sensor, determining a raw difference value corresponding to a difference between a pointing angle of a selected feature on the object and the heading angle, wherein the raw difference is based on a trajectory curvature of the centroid from the tracking sensor and a distance between the centroid and the selected feature, determining a filtered difference between the pointing angle and the heading angle using a low pass filter, and determining the pointing angle by subtracting the filtered difference from the heading angle.

Abstract: An illustrative example method of tracking a detected object comprises determining that a tracked object is near a host vehicle, determining an estimated velocity of the tracked object, and classifying the tracked object as frozen relative to stationary ground when the estimated velocity is below a preselected object threshold and a speed of the host vehicle is below a preselected host threshold.

Abstract: An illustrative example method of classifying a detected object includes detecting an object, determining that an estimated velocity of the object is below a preselected threshold velocity requiring classification, determining a time during which the object has been detected, determining a first distance the object moves during the time determining a speed of the object from the first distance and the time, determining a second distance that a centroid of the detected object moves during the time, and classifying the detected object as a slow moving object or a stationary object based on a relationship between the first and second distances and a relationship between the estimated velocity and the speed.

Abstract: An illustrative example method of tracking a moving object includes determining an initial pointing angle of the object from a tracking device, determining an estimated position of a selected feature on the object based upon the initial pointing angle, determining a velocity vector at the estimated position, determining a lateral acceleration at the estimated position based upon the velocity vector and a yaw rate of the object, determining a sideslip angle of the selected feature based on the lateral acceleration, and determining a refined pointing angle of the object from the determined sideslip angle.

Abstract: An illustrative example method of tracking a moving object includes determining a heading angle of a centroid of the object from a tracking sensor, determining a raw difference value corresponding to a difference between a pointing angle of a selected feature on the object and the heading angle, wherein the raw difference is based on a trajectory curvature of the centroid from the tracking sensor and a distance between the centroid and the selected feature, determining a filtered difference between the pointing angle and the heading angle using a low pass filter, and determining the pointing angle by subtracting the filtered difference from the heading angle.

Abstract: Systems, methods, and computer-readable media for channel estimation in mud pulse telemetry based on a preamble waveform. A system located at a first location of a wellbore can receive, from a second device located at a second location of the wellbore, a signal including a Golay preamble waveform and data symbols. The Golay preamble waveform can be based on Golay complementary codes. Based on a measurement associated with the signal, the system can then detect the Golay preamble waveform in the signal. Next, the system can estimate a characteristic of a communication channel between the first location and the second location based on the detected Golay preamble waveform.

Abstract: Systems, methods, and computer-readable media for channel estimation in mud pulse telemetry based on a preamble waveform. A system located at a first location of a wellbore can receive, from a second device located at a second location of the wellbore, a signal including a Golay preamble waveform and data symbols. The Golay preamble waveform can be based on Golay complementary codes. Based on a measurement associated with the signal, the system can then detect the Golay preamble waveform in the signal. Next, the system can estimate a characteristic of a communication channel between the first location and the second location based on the detected Golay preamble waveform.