Examples disclosed herein relate to a Meta-Structure (“MTS”) antenna system with adaptive frequency-based power compensation. The MTS antenna system includes a radiating array structure having a plurality of radiating elements, and a transmission array structure coupled to the radiating array structure and feeding a transmission signal through to the radiating array structure. The transmission array structure has a plurality of super element transmission paths, each having a plurality of vias to form transmission paths and a plurality of slots for feeding the transmission signal to the radiating array structure, and a plurality of power amplifiers coupled to an adaptive feedback module, each power amplifier coupled to a super element transmission path, the adaptive feedback module to adjust a power gain at a center frequency.

A vehicle radar device includes a radar control unit, a first antenna array, a second antenna array, a first circuit board and a second circuit board. The first antenna array is communicatively connected to the radar control unit. The first antenna array includes a plurality of first transmitting elements and a plurality of first receiving elements. The second antenna array is communicatively connected to the radar control unit. The second antenna array includes a plurality of second transmitting elements and a plurality of second receiving elements. The first antenna array is a plurality of circuit board antennas and disposed on the first circuit board. The second antenna array is a plurality of circuit board antennas and disposed on the second circuit board.

The present disclosure relates to a vehicle radar, a vehicle radar controlling method, and a vehicle radar controlling system. Specifically, the vehicle radar includes a signal transmitter which transmits a transmission signal for detecting a target object, a signal receiver which receives a reception signal including a target signal generated by the transmission signal being reflected by the target object, and a signal processor which processes the reception signal to form a frequency spectrum of the reception signal. Specifically, the signal processor determines a window size based on the frequency spectrum of the reception signal, determines spectrum similarity between an up-chirp frequency and a down-chirp frequency based on the determined window size, and determines a length of the target object if the spectrum similarity is greater than a preset threshold value.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing gesture recognition using a space time neural network. The space time neural network employs machine learning to recognize a user's gesture based on complex radar data. The space time neural network is implemented using a multi-stage machine-learning architecture, which enables the radar system to conserve power and recognize the user's gesture in real time (e.g., as the gesture is performed). The space time neural network is also adaptable and can be expanded to recognize multiple types of gestures, such as a swipe gesture and a reach gesture, without significantly increasing size, computational requirements, or latency.

Apparatus and associated methods relate to enabling a radar system to use different sensing mechanisms to estimate a distance from a target based on different detection zones (e.g., far-field and near-field). In an illustrative example, a curve fitting method may be applied for near-field sensing, and a Fourier transform may be used for far-field sensing. A predetermined set of rules may be applied to select when to use the near-field sensing mechanism and when to use the far-field mechanism. The frequency of a target signal within a beat signal that has less than two sinusoidal cycles may be estimated with improved accuracy. Accordingly, the distance of a target that is within a predetermined distance range (e.g., two meters range for 24 GHz ISM band limitation) may be reliably estimated.

False alarms in RADAR processing are reduced. One or more transforms may be performed to generate an array of spectrum values for a first domain spanning at least one of a range axis, a direction of arrival (DoA) axis, or a velocity axis. One or more spectrum values may be obtained from the array of spectrum values, wherein for each of the one or more spectrum values, (1) the spectrum value is associated with a range estimate, and (2) the spectrum value exceeds a range-dependent maximum threshold established based on a quartic function of the range estimate. The one or more spectrum values identified as exceeding the range-dependent maximum threshold may be excluded, or one or more reduced-magnitude values obtained, to generate an array of modified spectrum values for the first domain, used to generate a range estimate, a DoA estimate, or a velocity estimate, or any combination thereof.

A radar (30) is an FMCW radar. A determination unit (901) of the radar (30) executes at least one program of an attenuation determination program (324a), which determines whether an abnormal attenuation is present in a beat signal (S305), and a frequency characteristic determination program (325a), which determines whether an anomaly is present in a frequency characteristic of the beat signal (S305). The radar (30) can determine whether the beat signal (S305) is abnormal by software by executing the attenuation determination program (324a) and the frequency characteristic determination program (325a).

A phase correcting device includes a local oscillator that includes an all digital phase-locked loop configured to output a local oscillation signal, a first phase detector configured to detect a phase of the local oscillation signal to output the phase of the local oscillation signal, a reference phase device configured to generate a quasi-reference phase corresponding to a reference phase of the local oscillation signal to output the quasi-reference phase, based on a reference clock, a second phase detector configured to detect a fluctuation amount of a phase of the local oscillator, based on the phase detected by the first phase detector and the quasi-reference phase, and a correction circuit configured to correct the phase of the inputted signal by using a detection result of the second phase detector.

A method for separating large and small targets from noise in radar IF signals, according to which a receiver receives, echo signals that are reflected from targets of different size (such as walls or ground), in response to the transmission of chirp FMCW radar signals, modulated (e.g., using Linear Frequency Modulation) in a predetermined modulation speed for a predetermined duration. The echo signals are down-converted by mixing them with the transmitted signal, to obtain received Intermediate Frequency (IF) signal, which is then sampled both in phase (I-channel) and in quadrature phase (Q-channel). The received IF signal passes a Fourier transform, to obtain power spectral components that belong to a relevant frequency domain, associated with an echo signal reflected from a real target, along with corresponding power spectral components that belong to an irrelevant, opposite frequency domain. The noise distribution and level in the relevant frequency domain is calculated by estimating the noise level in the irrelevant frequency domain and targets represented by a set of consequent relevant frequencies are detected by comparing the power spectral component at each relevant frequency to the calculated noise level and identifying power spectral components with likelihood, which is above a predetermined threshold.

Motion-related 3D feature extraction by receiving a set of sequential radar signal data frames associated with a subject, determining radar signal amplitude and phase for each data frame, determining radar signal phase changes between sequential data frames, and extracting, by a trained machine learning model, one or more three-dimensional features from the sequential radar signal data frames according to the radar signal amplitude and the radar signal phase changes between sequential data frames.