A sensor system includes: a radar system configured to emit a radar beam and receive reflected radar signals from in a field of view of the radar system; a camera system including one or more cameras, at least one camera including a linear polarization filter in an optical axis of the camera, a field of view of the camera system overlapping the field of view of the radar system; and a processing system including a processor and memory, the memory storing instructions that, when executed by the processor, cause the processor to: receive radar data based on the reflected radar signals captured by the radar system; receive polarization raw frames captured by the camera system; and compute a track of a target in the field of view of the camera system and the field of view of the radar system based on the radar data and the polarization raw frames.

A computer implemented method for determining an angle of a detection comprises the following steps carried out by computer hardware components: acquiring a range rate of the detection; determining a pair of candidate angles of the detection based on the range rate; acquiring a beamvector of the detection; determining a correlation between the beamvector and a reference vector; and determining the angle of the detection based on the pair of candidate angles and based on the correlation.

A computer implemented method for determining an angle of a detection comprises the following steps carried out by computer hardware components: acquiring a range rate of the detection; determining a pair of candidate angles of the detection based on the range rate; acquiring a beamvector of the detection; determining a correlation between the beamvector and a reference vector; and determining the angle of the detection based on the pair of candidate angles and based on the correlation.

An apparatus for estimating the number of targets including a radar signal receiver configured to receive a radar signal that belongs to a detection signal transmitted by a radar and that is reflected by an object on the ground, and a controller configured to learn the number of targets by processing the received radar signal and to estimate the number of targets by processing a newly received radar signal based on the learned information.

The posture of the driver is detected from the driver head portion, and the detected value and the driver mounting determination value are used to determine that the driver is pushing the vehicle and obtain the vehicle pushing command value. Converts the vehicle pushing command value to the target vehicle pushing assistance vesicle speed, determines whether vehicle pushing assistance can be performed based on the driver's posture and the vehicle condition, and outputs the vehicle pushing assistance permission determination. Then, from the target vehicle pushing assistance vehicle speed and the vehicle pushing assistance permission determination, the control amount for the vehicle power source that assists the vehicle pushing is calculated and output.

The posture of the driver is detected from the driver head portion, and the detected value and the driver mounting determination value are used to determine that the driver is pushing the vehicle and obtain the vehicle pushing command value. Converts the vehicle pushing command value to the target vehicle pushing assistance vesicle speed, determines whether vehicle pushing assistance can be performed based on the driver's posture and the vehicle condition, and outputs the vehicle pushing assistance permission determination. Then, from the target vehicle pushing assistance vehicle speed and the vehicle pushing assistance permission determination, the control amount for the vehicle power source that assists the vehicle pushing is calculated and output.

Aspects of the present disclosure are directed to radar signal processing apparatuses and methods. As may be implemented in accordance with one or more embodiments, digital signals representative of received reflections of radar signals transmitted towards a target are mathematically processed to provide or construct a matrix pencil based on or as a function of a forward-backward matrix. Eigenvalues of the matrix pencil are computed and an estimation of the direction of arrival (DoA) of the target is output based on the computed eigenvalues.

Aspects of the present disclosure are directed to radar signal processing apparatuses and methods. As may be implemented in accordance with one or more embodiments, digital signals representative of received reflections of radar signals transmitted towards a target are mathematically processed to provide or construct a matrix pencil based on or as a function of a forward-backward matrix. Eigenvalues of the matrix pencil are computed and an estimation of the direction of arrival (DoA) of the target is output based on the computed eigenvalues.

This document describes techniques and systems for super-resolution based on iterative multiple-source angle-of-arrival estimation. Beam vectors received by an electromagnetic sensor may include information about multiple objects, but if the objects are close, the objects may initially appear as a single object in a Doppler-range bin. Performing iterative operations on a first angle derived from the beam vector and a subsequent second angle, associated with a second object, derived from the first angle, the first angle and the second angle may be refined and converge toward their actual respective values. The iterative operations include performing calculations involving only the first angle value and the second angle value as unknowns. Noise has been approximated to be random Gaussian noise with zero mean. Additionally, phase ambiguity, associated with sparse channel arrays has been eliminated. The calculations may require less computational complexity and maintain accuracy resulting in safer and reliable tracking systems.

This document describes techniques and systems for super-resolution based on iterative multiple-source angle-of-arrival estimation. Beam vectors received by an electromagnetic sensor may include information about multiple objects, but if the objects are close, the objects may initially appear as a single object in a Doppler-range bin. Performing iterative operations on a first angle derived from the beam vector and a subsequent second angle, associated with a second object, derived from the first angle, the first angle and the second angle may be refined and converge toward their actual respective values. The iterative operations include performing calculations involving only the first angle value and the second angle value as unknowns. Noise has been approximated to be random Gaussian noise with zero mean. Additionally, phase ambiguity, associated with sparse channel arrays has been eliminated. The calculations may require less computational complexity and maintain accuracy resulting in safer and reliable tracking systems.