An apparatus is for generating an emulated radar reflection signal of a target. The apparatus includes a radar detector configured to detect a radar signal frame emitted by a device under test (DUT), an emulation transmitter configured to generate an emulated radar reflection signal of a target being emulated, and a processor configured to generate control signals which control the emulation transmitter according to at least one characteristic of the target being emulated. The processor is further configured to determine a current radar parameter among plural possible radar parameters of the radar signal frame of the DUT, and to adapt the control signals which control the emulation transmitter according to the determined current radar parameter of the radar signal frame of the DUT.

An apparatus is for generating an emulated radar reflection signal of a target moving at a relative velocity. The apparatus includes a radar detector, an emulation transmitter, and a processor. The radar detector is configured to detect radar chirps emitted by a device under test (DUT), where the chirps are emitted at random time intervals. The emulation transmitter is configured to generate emulated radar reflection signals of the target being emulated. The processor is configured to generate control signals at intervals corresponding to the random time intervals at which the radar chirps are emitted by the DUT, where each control signal controls the emulation transmitter to generate a radar reflection signal. A relative phase of the control signals is adjusted according to a duration of each of the random time intervals between successive chirps and a magnitude and sign of the relative velocity of the target.

Methods, systems, and devices for wireless communications are described. User equipment (UE), such as vehicles, may implement radar transmissions to detect and avoid potential collisions with a target such as other UE or pedestrians. A first UE may receive an indication of a location of a second UE and an indication of one or more parameters associated with radar transmission from the second UE. The first UE may also receive a radio frequency waveform that includes a first component associated with the radar transmissions from the second UE and a second component that is associated with reflected radar transmission from the first UE. The first UE may compensate for an interference from the first component based on the location of the second UE and the one or more parameters. The first UE may generate a radar image from the received radio frequency waveform based on compensating for the interference.

A system and method are provided for emulating echo signals using test equipment, including an antenna and an I/Q mixer, in response to a radar signal transmitted by a radar under test. The method includes receiving the radar signal from the radar under test, where a reflection component of the radar signal is reflected from at least the antenna; mixing the received radar signal as a local oscillator (LO) signal with I and Q signals at the I/Q mixer to output a mixing product as a radio frequency (RF) signal, where a leakage component of the LO signal leaks through the I/Q mixer; substantially canceling the reflection component of the radar signal using the leakage component of the LO signal; and transmitting the RF signal as the emulated echo signal to the radar under test, wherein the emulated echo signal indicates at least a range to the emulated target.

In an embodiment, a method for testing a millimeter-wave radar module includes: providing power to the millimeter-wave radar module; performing a plurality of tests indicative of a performance level of the millimeter-wave radar module; comparing respective results from the plurality of tests with corresponding test limits; and generating a flag when a result from a test of the plurality of test is outside the corresponding test limits, where performing the plurality of tests includes: transmitting a signal with a transmitting antenna coupled to a millimeter-wave radar sensor, modulating the transmitted signal with a test signal, and capturing first data from a first receiving antenna using an analog-to-digital converter of the millimeter-wave radar sensor, where generating the flag includes generating the flag based on the captured first data.

Method for providing reduced interference for at least two co-located FMCW (Frequency Modulated Continuous Wave) Doppler radars, each of said radars being used in a system to detect respective distances to and velocities of objects moving through space, can include a propagation determination step, in which expected electromagnetic wave propagation times are determined between pairs of radars; a sweep time offset synchronizing step, in which different respective sweep time offsets are selected, with respect to each radar in a first group of radars; and a sweep frequency offset synchronizing step, in which a second sweep frequency offset is selected with respect to a second group of radars, the second sweep frequency offset being relative to a sweep frequency pattern used for radars belonging to said first group. The invention also relates to a system and to a computer software product.

A communication unit (300) is described that includes a plurality of cascaded devices that includes at least one master device and at least one slave device configured in a master-slave arrangement. The at least one master device comprises a modulator circuit (362) configured to: receive a system clock signal and a frame start signal; modulate the system clock signal with the frame start signal to produce a modulated master-slave clock signal (384); and transmit the modulated master-slave clock signal (384) to the at least one slave device. The at least one slave device comprises a demodulator circuit (364) configured to: receive and demodulate the modulated master-slave clock signal (384); and re-create therefrom the system clock signal (388, 385) and the frame start signal (390, 386).

Methods for monitoring of performance parameters of one or more receive channels and/or one or more transmit channels of a radar system-on-a-chip (SOC) are provided. The radar SOC may include a loopback path coupling at least one transmit channel to at least one receive channel to provide a test signal from the at least one transmit channel to the at least one receive channel when the radar SOC is operated in test mode. In some embodiments, the loopback path includes a combiner coupled to each of one or more transmit channels, a splitter coupled to each of one or more receive channels, and a single wire coupling an output of the combiner to an input of the splitter.

A radar data processing device includes at least one analog-to-digital converter (ADC) configured to digitize a plurality of input signals, wherein each input signal includes radar chirp and radar chirp reflection information received at one of a plurality of receiver antennas. The radar data processing device also includes Fast Fourier Transform (FFT) logic configured to generate FFT output samples based on each digitized input signal, wherein at least some of the generated FFT output samples are across antenna FFT output samples associated with at least two of the plurality of receiver antennas. The radar data processing device also includes a processor configured to determine a plurality of object parameters based on at least some of the generated FFT output samples, wherein the processor uses a neural network classifier trained to provide a confidence metric for at least one of the plurality of object parameters.

This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNN model having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix. The classifier layer identifies a type of the action performed by the target based on the features.