This disclosure describes a radar system configured to estimate a yaw-rate and an over-the-ground (OTG) velocity of extended targets in real-time based on raw radar detections. This disclosure further describes techniques for determining instantaneous values of lateral velocity, longitudinal velocity, and yaw rate of points of a rigid body in a radar field-of-view (FOV) of the radar system.

Aspects of the present disclosure include methods, systems, and non-transitory computer readable media for transmitting at least one incident radar signal, receiving at least one reflected radar signal in response to the at least one incident radar signal reflected from a person associated with the RFID tag, determining a first movement vector of the person based on the reflected radar signal, transmitting at least one incident RFID signal, receiving at least one backscattered RFID signal from the RFID tag, determining a second movement vector of the RFID tag based on at least one phase measurement of the at least one backscattered RFID signal, and determining whether the RFID tag is associated with the person based on the first movement vector and the second movement vector.

Methods and systems are provided for classifying an object proximate a first vehicle having a first radar system. First information is received from a first radar signal of the first radar system pertaining to the object. Second information is received from a second radar signal of a second vehicle pertaining to the object. The object is classified using the first information and the second information.

In a radar apparatus, a determining unit compares a ratio (−Vr/Vn) of a relative velocity (Vr) to a radar-apparatus-installed vehicle velocity (Vn) with a determination value (a) that is a cosine (cos θc) of the detection limit angle (±θc) or the cosine (cos θc) plus a correction value including a measurement error. When a determination is made that the ratio (−Vr/Vn) exceeds the determination value (α), a target is determined to be a real target of a crossing object, such as a crossing pedestrian, or a stationary object, whereas when a determination is made that the target is not a real target, the target is determined to be a ghost of a crossing object or a stationary object. Thus, a real target of a crossing object or a stationary object can be distinguished from a ghost of the object, and hence a ghost is not falsely determined to be a real target, preventing inappropriate brake control.

An apparatus comprising circuitry configured to transfer motion information obtained from a plurality of sensors of different or similar type to a common representation.

Techniques are discussed for determining reflected returns in radar sensor data. In some instances, pairs of radar returns may be compared to one another. For example, a velocity associated with a first radar return may be projected onto a radial direction associated with a second radar return to determine a projected velocity. In some examples, the second radar return may be a reflected return if the magnitude of the projected velocity corresponds to a magnitude of the second radar return. In some instances, a vehicle, such as an autonomous vehicle, may be controlled at the exclusion of information from reflected returns.

A computer includes a processor and a memory storing instructions executable by the processor to collect at least one set of data with a first Doppler sensor, each set of data including a radial distance, an azimuth angle and a range rate between the first Doppler sensor and a target, collect at least one set of data with a second Doppler sensor, determine that the collected sets of data include a first, second, and third set, determine respective radial components of a ground velocity of the target based on the first, second and third sets of data a position on a host vehicle of the respective Doppler sensor that collected the sets of data, and determine a linear velocity of the target and a yaw rate of the target based on the radial components of the ground velocity of the target.

A process and systems for constructing arbitrarily large virtual arrays using two or more collection platforms (e.g. AUX and MOV systems) having differing velocity vectors. Referred to as Motion Extended Array Synthesis (MXAS), the resultant imaging system is comprised of the collection of baselines that are created between the two collection systems as a function of time. Because of the unequal velocity vectors, the process yields a continuum of baselines over some range, which constitutes an offset imaging system (OIS) in that the baselines engendered are similar to those for a real aperture of the same size as that swept out by the relative motion, but which are offset by some (potentially very large) distance.

A method is provided for assigning a bounding box to an object in an environment of a vehicle. Data related to objects located in the environment of the vehicle are acquired via a sensor. Based on the data, a respective spatial location and a respective size of a plurality of preliminary bounding boxes are determined such that each preliminary bounding box covers one of the objects at least partly. A respective velocity of each preliminary bounding box is estimated based on the data. A subset of the plurality of preliminary bounding boxes being related to a respective one of the objects is selected, where the subset is selected based on the respective velocity of each of the preliminary bounding boxes. A final bounding box is assigned to the respective one of the objects by merging the preliminary bounding boxes of the corresponding subset.

A method and system for integrating multiple measurement hypotheses in an efficient labeled multi-Bernoulli (LMB) filter. The LMB filter estimates a plurality of tracks for a plurality of objects, each track of the plurality of tracks having a unique label, a probability, and a state, wherein each track of the plurality of tracks is associated to an object of a plurality of objects to be tracked, each object having an object state. The method receives one or more measurement hypotheses of the multiple measurement hypotheses for each object of the plurality of objects; updates each track of the plurality of tracks based on the respective track and the one or more measurement hypotheses of the multiple measurement hypotheses; determines, for each combination of track of the plurality of tracks and measurement hypothesis, a likelihood η.sub.i(j, k); samples, for each iteration of a plurality of iterations, an update hypothesis γ.sup.(t), based on an association of each track of the plurality of tracks to one of: a measurement hypothesis, an events missed detection, or a track dying detection; determining the state of each track of the plurality of tracks based on its respective associations in the updated hypotheses γ.sup.(t); extracts, for each track of the plurality of tracks, an existence probability; predicting the object state of each object of the plurality of objects with respect to a next measurement time; determines, whether another update is to be performed; and if another update is to be performed, repeats again the method steps from and including updating each track of the plurality of tracks.