Provided is an object recognition device including a prediction processing unit, a temporary setting unit, and a association processing unit. The prediction processing unit predicts, as a prediction position on an object model obtained by modeling a tracking target, a position of a movement destination of the tracking target based on a trajectory formed by movement of at least one object of a plurality of objects as the tracking target. The temporary setting unit sets, based on specifications of a sensor that has detected the tracking target, a position of at least one candidate point on the object model. The association processing unit sets, based on the position of the candidate point and the prediction position, a reference position on the object model. The association processing unit determines whether the position of the detection point and the prediction position associate with each other based on a positional relationship between a association range which is set so that the association range has a reference position on the object model as a reference and a detection point at a time when the sensor has detected the at least one object of the plurality of objects.

The invention relates to a method for detecting at least one road user on a traffic route by means of a radar sensor and an optical detector, wherein with said method radar radiation is emitted by at least one radar transmitter of the radar sensor and reflected by the at least one road user, the reflected radar radiation is detected by means of at least one radar receiver of the radar sensor, the detected radar radiation is evaluated in such a way that at least one distance and one radial velocity of the at least one road user relative to the radar sensor is determined, an optical image of the at least one road user is detected by means of the optical detector, and the optical image is evaluation,
wherein at least one parameter of the at least one road user is determined both from the detected radar radiation and the optical image.

The described aspects and implementations enable fast and accurate object identification in autonomous vehicle (AV) applications by combining radar data with camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar image of a first hypothetical object in an environment of the AV, obtaining a camera image of a second hypothetical object in the environment of the AV, and processing the radar image and the camera image using one or more machine-learning models MLMs to obtain a prediction measure representing a likelihood that the first hypothetical object and the second hypothetical object correspond to a same object in the environment of the AV.

A method is described below which can be used in a radar system. According to one example implementation, the method comprises providing a digital baseband signal using a radar receiver. The baseband signal comprises a plurality of segments, wherein each segment is assigned to a chirp of an emitted chirp sequence and each segment comprises a specific number of samples. For each signal sequence of n samples of the segments, where n in each case denotes a specific sample position within the respective segment, the method comprises the following: detecting interference-affected samples of the signal sequence; splitting the signal sequence into two or more sub-band signal sequences, wherein each sub-band signal sequence is assigned in each case to a frequency sub-band; replacing interference-affected samples in the two or more sub-band signal sequences in each case with a value which is based on adjacent samples in order to obtain corrected sub-band signal sequences; and determining a corrected signal sequence of n samples of the segments based on the corrected sub-band signal sequences.

Methods, apparatus, systems and articles of manufacture are disclosed to improve Doppler velocity estimation. An example apparatus is disclosed including a transmitter to transmit a first sweep signal at a first position in a first block of time during a transmit time sequence pattern, and transmit a second sweep signal at a second position in a second block of time during the transmit time sequence pattern, the second position different than the first position. The example apparatus also includes a velocity analyzer to determine a velocity and a direction of arrival of a target object identified during the transmit time sequence pattern.

The invention discloses a golf ball tracking system, which includes a distributed sensor and processor system adapted to simultaneously track the trajectories of multiple golf balls hit by one of more golfers. The system is adapted to keep track of the location of the golfers to enable the allocation of shots to the correct golfer. The system is operated at a golf driving range, where multiple players can hit balls from anywhere within a designated area and/or fixed hitting bay locations. Multilateration is used to determine the location of multiple targets in 3D space, based on the reported range and Doppler from distributed radar sensors.

In an illustrative integrated circuit, a chirp generator provides a chirp signal having linearly-ramped chirp intervals, while a shift frequency generator provides a signal having a different shift frequency during each of multiple segments in each chirp interval. A modulator combines the signals to derive a segmented chirp signal having multiple linearly-ramped chirp segments in each chirp interval. The modulator may be a single sideband modulator to provide frequency up-shifted and frequency down-shifted chirp segments. The segmented chirp signal may be suppressed during resettling intervals of the original chirp signal.

A MIMO radar system including a transmitter array, and a receiver array, the antenna distances in one of the transmitter and receiver arrays being above the Nyquist limit for unambiguous angle measurements, but the antenna distances in the combination of the transmitter and receiver arrays being below this Nyquist limit. The system also includes a control and evaluation unit.

A radar including an interface configured to receive a frequency estimation output and a synchronization signal correlation output from a radio communication device; and a processing block configured to use the received frequency estimation output and synchronization signal correlation output for velocity and range estimation.

A radar antenna calibration method includes: forming a detection matrix from signals detected by an arrangement of receive antennas in response to chirps transmitted by an arrangement of transmit antennas, the detection matrix having multiple rows corresponding to the chirps, multiple columns corresponding to a signal sample, and multiple planes corresponding the receive antennas; deriving a range matrix by performing a frequency transform on a portion of each row of the detection matrix; extracting a slice of the range matrix, with different rows of the slice being associated with different chirps and with different receive antennas; deriving a velocity matrix from the extracted slice by performing a frequency transform on a portion of each column of the extracted slice; analyzing the velocity matrix to determine a current peak width; and adjusting, based on the current peak width, phase shifts associated with one or more of the receive antennas.