Abstract: A method for four-dimensional radar tracking includes transmitting a first probe signal; receiving a first reflected probe signal at first and second radar arrays of the radar system; detecting a tracking target; calculating a target range; calculating a target range rate; performing ambiguous angle calculations for first and second target angles; performing unambiguous angle calculations for the first and second target angles; and calculating a four-dimensional tracking solution, including position and range-rate, from the target range, target range-rate, ambiguous angle calculations, and unambiguous angle calculations.

Abstract: A method for four-dimensional radar tracking includes transmitting a first probe signal; receiving a first reflected probe signal at first and second radar arrays of the radar system; detecting a tracking target; calculating a target range; calculating a target range rate; performing ambiguous angle calculations for first and second target angles; performing unambiguous angle calculations for the first and second target angles; and calculating a four-dimensional tracking solution, including position and range-rate, from the target range, target range-rate, ambiguous angle calculations, and unambiguous angle calculations.

Abstract: This invention is related to an automated traffic surveillance system to monitor traffic comprising of a plural number of Doppler radars, circuitry for processing radar signals, and data recording and displaying devices. Although the system is mainly designed for roadside traffic surveillance, it can be used in different applications, such as mounted on a host vehicle. The system will provide continuous surveillance of all incoming and leaving traffic.

Abstract: This invention is related to a Video-Doppler-Radar Traffic Surveillance System comprising of multiple Doppler radars and video cameras, circuitry for processing radar and video signals, and data recording devices. Although the system is mainly designed for roadside traffic surveillance, it can be used in different applications, such as mounted on a host vehicle or on a UAV. The system will provide continuous surveillance of all incoming and leaving traffic.

Abstract: A radar system for detecting a single object from a plurality of objects and calculating 4D information of said object may include a first radar for obtaining first positional information of a first object, a second radar for obtaining second positional information of a second object and a computer for comparing the first positional information with the second positional information to determine if the first object is the same as the second object and combining the matched first positional and second positional information into 4D information.

Abstract: This invention is related to an automated traffic surveillance system to monitor traffic comprising of a plural number of Doppler radars, circuitry for processing radar signals, and data recording and displaying devices. Although the system is mainly designed for roadside traffic surveillance, it can be used in different applications, such as mounted on a host vehicle. The system will provide continuous surveillance of all incoming and leaving traffic.

Abstract: This invention is related to a Doppler-Vision-Radar Traffic Surveillance System comprising of multiple Doppler radars, circuitry for processing radar signals, and data recording and displaying devices. Although the system is mainly designed for roadside traffic surveillance, it can be used in different applications, such as mounted on a host vehicle or on a UAV. The system will provide continuous surveillance of all incoming and leaving traffic.

Abstract: A moving sensor suite for imaging a scene has three Doppler radars, two moving and one fixed, a fixed video camera and a fixed GPS receiver. The Doppler radars measure the relative velocities between the radars and the scene, as well as the scene's electromagnetic reflectivity, while the video camera records the motion of the camera and the optical property of the scene. The correct registration of the Doppler radars and the camera is established by finding the intersections of the moving Doppler radar motion vectors with the image plane of the video camera. The scene features in the first frame are determined by Doppler circle intersections. The correspondences of the features in the next two frames are established by a feature matching operation.

Abstract: This invention is related to a fusion algorithm for a video-Doppler-radar (Vidar) traffic surveillance system comprising of (1) a robust matching algorithm which iteratively matches the information from a video camera and multiple Doppler radars corresponding to a same moving vehicle, and (2) a stochastic algorithm which fuses the matched information from the video camera and Doppler radars to derive the vehicle velocity and range information.

Abstract: This invention is related to a Video-Doppler-Radar Traffic Surveillance System comprising of multiple Doppler radars and video cameras, circuitry for processing radar and video signals, and data recording and displaying devices. Although the system is mainly designed for roadside traffic surveillance, it can be used in different applications, such as mounted on a host vehicle or on a UAV. The system will provide continuous surveillance of all incoming and leaving traffic.

Abstract: A moving sensor suite for imaging a scene has three Doppler radars, two moving and one fixed, a fixed video camera and a fixed GPS receiver. The Doppler radars measure the relative velocities between the radars and the scene, as well as the scene's electromagnetic reflectivity, while the video camera records the motion of the camera and the optical property of the scene. The correct registration of the Doppler radars and the camera is established by finding the intersections of the moving Doppler radar motion vectors with the image plane of the video camera. The scene features in the first frame are determined by Doppler circle intersections. The correspondences of the features in the next two frames are established by a feature matching operation.

Abstract: A processor operatively coupled to a speed sensor adapted to generate a measure of a longitudinal speed of a vehicle on a roadway, and to a source of a measure of yaw rate of the vehicle, provides for selecting a most likely roadway model of a plurality of different roadway models and for outputting a corresponding associated at least one curvature parameter as an estimate of curvature of the roadway, wherein the processor incorporates a plurality of curvature estimators associated with the corresponding plurality of different roadway models.

Abstract: A processor using a first Kalman filter estimates a host vehicle state from speed and yaw rate, the latter of which may be from a yaw rate sensor if speed is greater than a threshold, and, if less, from a steer angle sensor and speed. Road curvature parameters are estimated from a curve fit of a host vehicle trajectory or from a second Kalman filter for which a state variable may be responsive to a plurality of host state variables. Kalman filters may incorporate adaptive sliding windows. Curvature of a most likely road type is estimated with an interacting multiple model (IMM) algorithm using models of different road types. A road curvature fusion subsystem provides for fusing road curvature estimates from a plurality of curvature estimators using either host vehicle state, a map database responsive to vehicle location, or measurements of a target vehicle with a radar system.

Abstract: A processor operatively coupled to a speed sensor adapted to generate a measure of a longitudinal speed of a vehicle on a roadway, and to a source of a measure of yaw rate of the vehicle, provides for selecting a most likely roadway model of a plurality of different roadway models and for outputting a corresponding associated at least one curvature parameter as an estimate of curvature of the roadway, wherein the processor incorporates a plurality of curvature estimators associated with the corresponding plurality of different roadway models.

Abstract: A first Kalman filter estimates true measures of yaw rate and vehicle speed from associated noisy measures thereof generated by respective sensors in a host vehicle, and a second Kalman filter estimates therefrom parameters of a clothoid model of road curvature. Measures of range, range rate, and azimuth angle from a target state estimation subsystem, e.g. a radar system, are processed by an extended Kalman filter to provide an unconstrained estimate of the state of a target vehicle. Associated road constrained target state estimates are generated for one or more roadway lanes, and are compared—either individually or in combination—with the unconstrained estimate. If a constrained target state estimate corresponds to the unconstrained estimate, then the state of the target vehicle is generated by fusing the unconstrained and constrained estimates; and otherwise is given by the unconstrained estimate alone.

Abstract: A processor using a first Kalman filter estimates a host vehicle state from speed and yaw rate, the latter of which may be from a yaw rate sensor if speed is greater than a threshold, and, if less, from a steer angle sensor and speed. Road curvature parameters are estimated from a curve fit of a host vehicle trajectory or from a second Kalman filter for which a state variable may be responsive to a plurality of host state variables. Kalman filters may incorporate adaptive sliding windows. Curvature of a most likely road type is estimated with an interacting multiple model (IMM) algorithm using models of different road types. A road curvature fusion subsystem provides for fusing road curvature estimates from a plurality of curvature estimators using either host vehicle state, a map database responsive to vehicle location, or measurements of a target vehicle with a radar system.

Abstract: A first Kalman filter estimates true measures of yaw rate and vehicle speed from associated noisy measures thereof generated by respective sensors in a host vehicle, and a second Kalman filter estimates therefrom parameters of a clothoid model of road curvature. Measures of range, range rate, and azimuth angle from a target state estimation subsystem, e.g. a radar system, are processed by an extended Kalman filter to provide an unconstrained estimate of the state of a target vehicle. Associated road constrained target state estimates are generated for one or more roadway lanes, and are compared—either individually or in combination—with the unconstrained estimate. If a constrained target state estimate corresponds to the unconstrained estimate, then the state of the target vehicle is generated by fusing the unconstrained and constrained estimates; and otherwise is given by the unconstrained estimate alone.

Abstract: A multiresolution, multirate approach for detecting and following targets. The resolution of data obtained from a target scanning region is reduced spatially and temporally in order to provide to a tracker a reduced amount of data to calculate. Balances of computer processing resources with long-range detection and multiple target tracking are provided. Computer processing resources can be allocated to other systems while still maintaining an accurate tracking function and alternatively, the tracking function can receive a high level of computer processing resources and track at a finer resolution. An ability to recover raw data points from the spatially and temporally reduced values is also provided.