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.

Implementations disclosed describe techniques and systems for efficient estimation of spatial characteristics of an outside environment of a wireless device. The disclosed techniques include generating multiple covariance matrices (CMs) representative of obtained sensing values. Different CMs may be associated with different frequency increments used in sensing signals to probe the outside environment. The disclosed techniques may further include determining eigenvectors for the CMs, and identifying, based on the determined eigenvectors, one or more spatial characteristics of the object in the outside environment.

The present invention relates a method of improving a radar system, a module for improving a radar system and an improved radar system that are more efficient than current radar systems and methods of using same. Specifically, in the context of space-time adaptive processing at high angle-doppler resolutions, this advanced radar system utilizes an improved estimator of the interference covariance matrix together with the plug-in whiten-then-match filter. This improvement (a) roughly optimizes the output signal-to-interference-plus-noise, thereby increasing the probability of accurately detecting targets' angular positions and radial velocities, (b) maintains a roughly constant, and thus controllable, false alarm rate, and (c) sometimes associates data preprocessing steps with a Reed-Mallett-Brennan detection loss, providing a guideline for rejecting certain preprocessing steps. Collectively, these advancements signify a considerable leap forward in radar technology.

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.

A radar apparatus (1) of the present embodiment includes a transmitting unit (12) that transmits a frequency chirp signal whose frequency linearly changes with time, a receiving unit (13) that receives a reflected wave that is the frequency chirp signal reflected by an object, a mixer (14) that mixes the transmitted frequency chirp signal and the received reflected wave to obtain a beat signal, a frequency estimation unit (15) that estimates a frequency of the beat signal, and a distance estimation unit (16) that estimates a distance to the object based on the frequency of the beat signal. The frequency estimation unit (15) calculates an autoregressive coefficient of an autoregressive model from a sequence of discrete signal values of the beat signal and estimates the frequency of the beat signal using a base frequency that is based on a pole of the autoregressive model.

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.

Devices and systems for implementing a walk-through gate are provided. The devices include a walk-through gate structure having boundaries that have curved inner surfaces on each side of a cavity. The curved inner surfaces are partially covered by a reflective material. The devices include radio frequency (RF) signal transmitters positioned tangent to the curved inner surfaces and RF signal receivers. The devices also include apertures that provide access to the cavity of the walk-through gate structure.

An example method for performing a joint radon transform association includes detecting, by a processing device, a target object to track relative to a vehicle. The method further includes performing, by the processing device, the joint radon transform association on the target object to generate association candidates. The method further includes tracking, by the processing device, the target object relative to the vehicle using the association candidates. The method further includes controlling, by the processing device, the vehicle based at least in part on tracking the target object.

Aspects of the disclosure are directed to spatial imaging using an imaging radar including generating a plurality of range/Doppler/channel images from a detected image and a four-dimensional image; generating a transfer matrix for each of the plurality of range/Doppler/channel images; generating a plurality of scatterer parameters using maximum likelihood (ML) processing on the plurality of range/Doppler/channel images; generating a plurality of refined scatterer parameters from the plurality of scatterer parameters and the transfer matrix; determining a minimal-order scatterer configuration using the plurality of refined scatterer parameters and the transfer matrix; and generating a set of determined scatterer parameters from the minimal-order scatterer configuration and the transfer matrix.

In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.