A system for sensing a target in a region of interest (ROI) includes a coded compressive antenna (CCA) to generate an EM field codified in multiple dimensions. One or more receivers receives EM energy reflected by the target, and produces reflection information corresponding to the reflected energy. A compressive sensing imaging processor analyzes reflection information to generate an image representing the target. The CCA may use a distorted reflector, a vortex lens, and/or meta-materials to codify the EM field in multiple dimensions. The system may evaluate a sensing matrix that characterizes the transmission channel and the codified EM field. The system configures the CCA to produce a coded EM field enhances certain sensing matrix singular values, with respect to an EM field produced by a non-codified antenna. The sensing system provides increased target sensitivity while reducing false detections.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles. A method for generating virtual sensor data includes simulating, using one or more processors, a three-dimensional (3D) environment comprising one or more virtual objects. The method includes generating, using one or more processors, virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining, using one or more processors, virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth comprises a dimension or parameter of the one or more virtual objects. The method includes storing and associating the virtual sensor data and the virtual ground truth using one or more processors.

An environment sensing method includes the following steps, carried out by a data processor a) defining an occupancy grid comprising a plurality of cells; b) acquiring at least one measurement result from a distance sensor, representative of the distance of one or more nearest targets; and c) computing an occupation probability of the cells of the occupancy grid by applying to the measurement an inverse sensor model stored in a memory device in the form of a data structure representing a plurality of model grids associated to respective distance measurement results, at least some cells of a model grid corresponding to a plurality of contiguous cells of the occupancy grid belonging to a same of a plurality of angular sectors into which the field of view of the distance sensor is divided, and associating a same occupation probability to each one of the plurality of cells. An apparatus programmed or configured for carrying out the environment sensing method and a computer-implemented method of computing an inverse sensor model suitable for carrying out the environment sensing method are also provided.

This document describes techniques and systems of multiple-target, simultaneous beamforming for four-dimensional (4D) radar systems for efficient angle estimation in two dimensions with a high dynamic range. For example, a processor can use electromagnetic (EM) energy received by a two-dimensional (2D) array to determine first angles in a first dimension associated with one or more objects. The processor can then determine a subspace projection matrix using the first angles without an estimate of the power of noise or interference signals in the received EM energy. Using the subspace projection matrix, the processor can determine an interference-orthogonal subspace projection-based beamformer. With the interference-orthogonal subspace projection-based beamformer, the processor can determine the desired signal output from an adaptive beamformer for the EM energy and second angles corresponding to respective first angles for the objects.

A radar apparatus of the present invention includes a transmitting array antenna that transmits signals orthogonal to one another from a plurality of transmitting antennas, a receiving array antenna that receives the signals reflected from a target by a plurality of receiving antennas, and a signal processing unit that detects the target from reception signals received by the plurality of receiving antennas. The signal processing unit includes a separation unit that separates the reception signals received by the plurality of receiving antennas, into signals corresponding to transmission signals from the plurality of transmitting antennas, a correlation matrix calculation unit that determines a first correlation matrix corresponding to the transmitting array antenna and a second correlation matrix corresponding to the receiving array antenna, on the basis of the reception signals separated by the separation unit, and a detection unit that detects the target on the basis of an evaluation value calculated using eigenvectors of the first correlation matrix and the second correlation matrix. This enables separate detection of a plurality of target reflected waves while suppressing degradation in angular resolution and increase in beam side lobes.

An apparatus configured to: receive an input dataset indicative of radar signals, reflected from targets, received at an antenna; determine an objective function for evaluation over a plurality of points of a search space representing possible direction-of-arrival angles; evaluate the objective function for a first candidate number of targets; perform a first evaluation of a branch metric function based on a second candidate number of targets, wherein the branch metric function is indicative of a change in the objective function; and if the branch metric function is greater than a predetermined threshold, then evaluate the objective function for the second candidate number of targets; if the branch metric function is less than the predetermined threshold, then evaluate the objective function for the first candidate number of targets, wherein the evaluation is based on at least one of the first candidate number of targets having a different, second candidate direction-of-arrival angle.

An image processing method for reducing distortion of a depth image may include: obtaining a plurality of original images based on light beams which are emitted to and reflected from a subject; determining original depth values of original depth images obtained from the plurality of original images, based on phase delays of the light beams, the reflected light beams comprising multi-reflective light beams that distort the original depth values; determining imaginary intensities of the multi-reflective light beams with respective to each phase of the multi-reflective light beams, based on regions having intensities greater than a predetermined intensity in the original depth images; correcting the original depth values of the original depth images, based on the imaginary intensities of the multi-reflective light beams; and generating corrected depth images based on the corrected original depth values.

Provided are a method and device for designing and optimizing a multi-degree-of-freedom frequency-modulation (FM) signal, and a computer storage medium. The method includes that: a time domain function of the multi-degree-of-freedom FM signal is determined; the constraint condition and the objective function of multi-degree-of-freedom FM signal optimization are established; an algorithm model of augmented Lagrangian genetic algorithm is determined based on the constraint condition and the objective function; characteristic parameters of the first iteration of the algorithm model are initialized, where the characteristic parameters of the first iteration at least include the Lagrange multiplier of the first iteration and the offset of the first iteration; initialization signal of the multi-degree-of-freedom FM signal is acquired, and initialization frequency control points of the initialization signal are determined based on the initialization signal.

The invention is an iterative process for performing iteratively the phase retrieval of an adaptive signal x(t) matching two sets of constraint both concerning the time envelope u.sub.e(t) of signal x(t) and magnitude distribution U.sub.m(f) of its spectral representation. At each iteration k the process computes an estimate {tilde over (x)}.sub.k(t) of signal x(t) which is obtained from a first projection P.sub.A on a first set of constraint in time domain of a computed value x.sub.k(t) of x(t), x.sub.k(t) deriving from an estimate {tilde over (X)}.sub.k1(f) of the spectrum of signal x(t), said estimate {tilde over (X)}.sub.k1(f) being itself obtained from a second projection P.sub.B on a second set of constraints in spectral domain of the Fourier transform X.sub.k(f) of the estimate {tilde over (x)}.sub.k1(t) of x(t) computed at iteration k1. Iterative computation of estimate {tilde over (x)}.sub.k(t) is repeated until {tilde over (x)}.sub.k(t) meets a predefined criterion which indicates that estimate {tilde over (x)}.sub.k(t) is close enough to expected signal x(t).

A method for encoding and storing digital data, which include a plurality of real values, in a signal processing unit of a radar sensor in which at least one real value r in an exponential representation in the form r=m.Math.b.sup.?k is stored, where m is a digital mantissa having a length p, b is a base, and k is a positive number that is encoded as a digital number having a length q. An exponential representation with b>2 is used for the compressed storage of the values r.