A method for use in acoustic imaging, comprising: transmitting, from a transmitter, a first sound wave pulse at a first frequency determined by a maximum sampling rate of a receiver; transmitting at least one second sound wave pulse at a frequency substantially equal to the first frequency, the first and at least one second sound wave pulses being transmitted substantially within a fraction of a sample interval of the receiver; receiving and sampling, at the receiver, a reflection of at least two of the first and at least one second pulses to generate a set of receiver samples; and expanding the set of receiver samples, based on the first frequency and a total number of the first and at least one second pulses transmitted, to generate an expanded sample set with a larger number of samples than the set of receiver samples.

An electronic device may include a processor and wireless circuitry with transmit and receive antennas. Radar circuitry may use the transmit and receive antennas to perform spatial ranging on external objects farther than a threshold distance (e.g., 1-2 cm) from the transmit antenna. The wireless circuitry may include a voltage standing wave ration (VSWR) sensor coupled to the transmit antenna to detect the presence of objects within the threshold distance from the transmit antenna. This may serve to cover a blind spot for the radar circuitry near to the transmit antenna. The VSWR sensor may gather background VSWR measurements when other wireless performance metric data for the wireless circuitry is within a predetermined range of satisfactory values. The background VSWR measurements may be subtracted from real time VSWR measurements to perform accurate and robust ultra-short range object detection near to the transmit antenna.

Conventional ESPRIT (Estimation of Signal Parameters via Rational Invariance Techniques) cannot be directly applied to SFCW MIMO radar for localization of targets as the performance would be restricted by geometry of spatial MIMO. Thus, the present disclosure provides a method and system for localization of targets using SFCW MIMO radar. In this method, the channel response of the virtual uniform rectangular array (vURA) obtained by scanning at uniformly spaced frequency points is combined to form a larger array referred as Space-Frequency (SF) array. The 3D localization of targets is done by estimating azimuth angle, elevation angle and range using this SF array. The localization capability of the disclosed method largely depends upon the number of frequency scanning points and enables localizing far more targets than the dimension of the vURA. In addition, the inter-element spacing requirement of vURA is also greatly relaxed.

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.

A radar system is described. The system comprises a radiation transmission unit, a radiation collection unit, and a processing unit. The radiation transmission unit is configured to generate electromagnetic radiation formed by a plurality of quantum entangled photons comprising first transmitted photon (signal) and second reference photon (idler). The radiation transmission unit transmits the first transmitted photons toward a region to be inspected and measures the second reference photons to obtain and store measured data thereof. The radiation collection unit comprises at least one radiation collection element configured to receive photons reflected from one or more objects in said region and generate data indicative of one or more parameters of the collected photons. The processing unit is configured to receive stored measured data on the second reference photons and data on parameters of the collected photons from the radiation collection unit, and to determine correlation between the stored measured data and the collected photons to thereby differentiate between noise collected photons and reflection of said first transmitted photons from one or more objects in the region to be inspected.

Spectral components of waves having one or more properties other than phase and amplitude that vary monotonically with time at a receiver, and provide retardations or lags in the variation in proportion to the times or distances traveled from the sources of the waves to the receiver. The lags denote the property values prior to departure from a source and are absent in its proximity. Orthogonality of the lags to modulated information makes them useful for ranging and for separation or isolation of signals by their source distances. Lags in frequencies and wavelengths permit multiplication of capacities of physical channels. Constancy of the lagging wavelengths along the entire path from a source to the receiver enables reception through channels or media unusable at the source wavelengths, as well as imaging at wavelengths different from the illumination.

A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a parked vehicle with its engine running. The location of the parked vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of the parked vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a parked vehicle with its engine running based on actual sensor outputs input to the machine learning model.

A method for angle estimation based on signals of a radar sensor with angular resolution in at least one dimension. The radar sensor includes a MIMO-enabled antenna array with at least three transmitting antennas and at least three receiving antennas. A cross-path model represented by a control matrix and models reflections of transmitted and/or received signals on a reflective surface is used to estimate a location angle of a radar target. The control matrix includes a Kronecker product A.sub.tx .Math.A.sub.rx of two submatrices, one, A.sub.tx, representing the arrangement of the transmitting antennas and the other, A.sub.rx, representing the arrangement of the receiving antennas. For calculating a DML estimation function, a matrix product Y=A.sup.H.sub.rx.Math.X.Math.A*.sub.tx is calculated approximately using an FFT from the submatrices and a reception matrix X that specifies the complex amplitudes of the signals received with different combinations of antennas.

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.

This document describes techniques and systems for electromagnetic response simulation for arbitrary road surface profiles. An electromagnetic response simulator receives a position and an orientation of both an electromagnetic sensor (e.g., radar sensor) and a target, and a geometric profile of a road surface. The road surface may vary in elevation in the lateral and/or longitudinal directions. The electromagnetic response simulator estimates reflection points of electromagnetic rays along the geometric profile of the road surface and translates the positions and the orientations of the electromagnetic sensor and the target into respective local coordinates corresponding to each reflection point. The electromagnetic responses can then be calculated, corresponding simulated rays can be output to a sensor simulator. In this manner, the variance in elevation of a road surface can be included in generating simulated rays, and the electromagnetic response simulator may more accurately simulate real-world electromagnetic responses.