Various examples for multi-tone continuous wave detection and ranging are disclosed herein. In some embodiments, an initial signal is generated using initial radio frequency (RF) tones, and is emitted as a multi-tone continuous wave signal. The initial signal is reflected from a target and received as a reflected signal. Resultant RF tones, including a frequency, a phase and a power, are determined from the reflected signal in a frequency domain. A frequency-domain sinusoidal wave is fitted to the resultant RF tones in the frequency domain, and a distance to the target is determined using a modulation of the frequency-domain sinusoidal wave. A phase processing algorithm is applied to generate the target distance and speed by triangulating the range information encoded in the backscattered RF tones.

Various examples for multi-tone continuous wave detection and ranging are disclosed herein. In some embodiments, an initial signal is generated using initial radio frequency (RF) tones, and is emitted as a multi-tone continuous wave signal. The initial signal is reflected from a target and received as a reflected signal. Resultant RF tones, including a frequency and a power, are determined from the reflected signal in a frequency domain. A frequency-domain sinusoidal wave is fitted to the resultant RF tones in the frequency domain, and a distance to the target is determined using a modulation of the frequency-domain sinusoidal wave.

A maritime radar system is provided, comprising a transmitter, a receiver, and one or more processors arranged to provide range and azimuth discrimination of a detection area by performing a delay/Doppler analysis of the echo of a single beam transmitted by the transmitter and received by the receiver.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing near-range detection. The radar system employs a near-range detection module for detecting objects at near ranges in the presence of interference and a far-range detection module for detecting objects at far ranges. By evaluating separate range intervals, these modules can be designed to achieve a target false-alarm rate and detection performance by tailoring their processing to general characteristics of objects and interference at their respective range intervals. This enables the near-range detection module to detect a near-range object without generating a false detection associated with the interference. By utilizing the near-range detection module and the far-range detection module, the radar system can detect objects at both near and far ranges while achieving a target false-alarm rate.

Machine learning network trained to tune settings and optimize images. In accordance with one aspect, a method is provided for image optimization with a medical ultrasound scanner. A medical ultrasound scanner images a patient using first settings. A first image from the imaging using the first settings and patient information for the patient are input to a machine-learned network. The machine-learned network outputs second settings in response to the inputting of the first image and the patient information. The medical ultrasound scanner re-images the patient using the second settings. A second image from the re-imaging is displayed.

The present disclosure provides a system for processing radar data. The system may comprise a frequency generator configured to generate a reference frequency signal; a timing module configured to generate a shared clock signal or a plurality of timing signals; and a plurality of radar modules in communication with the frequency generator and timing module. The radar modules may be configured to: (i) receive the reference frequency signal and at least one of a shared clock signal and a timing signal, (ii) transmit a first set of radar signals based in part on the reference frequency signal and at least one of the shared clock signal and the timing signal, and (iii) receive a second set of radar signals reflected from a surrounding environment. The system may comprise a processor configured to process radar signals received by each radar module, by coherently combining radar signals using phase and timestamp information.

Aspects of the present disclosure are directed to radar communications with disparate pulse repetition intervals, as may be implemented with radar transmission, receiver and processing circuitry. As may be utilized in accordance with one or more embodiments herein, time division multiplexing (TDM) multi-input multi-output (MIMO) radar signals are transmitted by transmitting sets of successive radar signals, each set having a pulse repetition interval (PRI) that is different than the PRI of sets of radar signals transmitted in another one of the sets. Positional characteristics of a target may be ascertained based on the PRI used in each of the sets and on phase characteristics of ones of the radar signals reflected from the target.

A method includes receiving from a transceiver, at a processor of an electronic device, a radar signal, the radar signal including a set of signal strength values across a range of Doppler frequency shift values and a range of time values. The method further includes extracting a time series of frequency features from the radar signal, wherein frequency features of the time series of frequency features include a determined Doppler frequency shift value for each time value of the range of time values, identifying segments within the time series of frequency features, analyzing the segments to determine a start time for a classification engine, at the start time, analyzing, by the classification engine, at least one of a subset of the time series of frequency features or a subset of the radar signal to identify a control gesture, and triggering a control input associated with the control gesture.

Distributed radar systems and techniques for processing data received from such distributed radar systems. The distributed radar systems may utilize data on beam spatial pattern for processing collected signals and determining direction of one or more reflection origins (e.g., one or more objects reflecting transmitted signal).

According to an aspect, a method of determining two dimensional (2D) angle of arrival (AoA) in a radar system comprising determining one dimensional (1D) AoA to generate a first set of (AoA), selecting a set of valid 1D AoA angles from the first set AoA, and determining the 2D AoA from the set of valid 1D AoA, Wherein the 1D AoA is determined on a first set of data received over a first uniform linear antenna array arranged in the first axis and the 2D AoA is determined on a second set data received over the first and the second MIMO antenna array arranged in the second axis and the set of valid 1D AoA in the first axis. Wherein the second antenna array need not be orthogonal to the first linear antenna array.