Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

Techniques and apparatuses are described for radar angular ambiguity resolution. These techniques enable a target's angular position to be determined from a spatial response that has multiple amplitude peaks. Instead of solely considering which peak has a highest amplitude, the techniques for radar angular ambiguity resolution select a frequency sub-spectrum, or multiple frequency sub-spectrums, that emphasize amplitude or phase differences in the spatial response and analyze an irregular shape of the spatial response across a wide field of view to determine the target's angular position. In this way, each angular position of the target has a unique signature, which the radar system can determine and use to resolve the angular ambiguities. Using these techniques, the radar can have an antenna array element spacing that is greater than half a center wavelength of a reflected radar signal that is used to detect the target.

A method and a device for separating echo signals of STWE SAR in elevation are provided. The method includes that: aliasing echo signals of multiple sub-swaths are received; for a target sub-swath of the multiple sub-swaths, multiple sub-beams associated with the target sub-swath are generated, the multiple sub-beams pointing to different directions of the target sub-swath respectively, and a null of each of the multiple sub-beams being used for deep nulling suppression on echo signals of sub-swaths except the target sub-swath; and the aliasing echo signals are processed based on the multiple sub-beams and multiple nulls corresponding to the multiple sub-beams to generate a target echo signal of the target sub-swath.

Methods for detecting radar targets are provided. According to one exemplary embodiment, the method includes providing a digital radar signal having a sequence of signal segments. Each signal segment of the sequence is respectively associated with a chirp of a transmitted RF radar signal. The method further includes detecting one or more radar targets based on a first subsequence of successive signal segments of the sequence. For each detected radar target, a distance value and a velocity value are determined. If a group of radar targets having overlapping signal components has been detected, a respective spectral value is calculated for each radar target of the group of radar targets based on a second subsequence of the sequence of signal segments and further based on the velocity values ascertained for the group of radar targets.

Techniques and apparatuses are described that implement a smart device with an integrated radar system. The radar integrated circuit is positioned towards an upper-middle portion of a smart device to facilitate gesture recognition and reduce a false-alarm rate associated with other non-gesture related motions of a user. The radar integrated circuit is also positioned away from Global Navigation Satellite System (GNSS) antennas and a wireless charging receiver coil to reduce interference. The radar system operates in a low-power mode to reduce power consumption and facilitate mobile operation of the smart device. By limiting a footprint and power consumption of the radar system, the smart device can include other desirable features in a space-limited package (e.g., a camera, a fingerprint sensor, a display, and so forth).

This document describes “Multi-domain Neighborhood Embedding and Weighting” (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.

A system for estimating a parameter of an object includes a receiver configured to detect a return signal of a radar signal, and a processing device configured to sample the return signal to generate a series of signal samples, partition a time frame into a plurality of successive segments k, and for each segment k, apply a Doppler Fourier transform and calculate a complex value y.sub.k as a function of Doppler frequencies f.sub.D. The processing device is also configured to calculate an index based on an acceleration hypothesis and a velocity hypothesis of a set of hypotheses, and for each segment, select one or more Doppler frequency bins based on the index and extract components of the complex value y.sub.k (f.sub.D) associated with each selected Doppler frequency bin. The processing device is further configured to calculate a velocity and acceleration spectrum, and estimate an object parameter based on the spectrum.

A system and method for ice crystal detection and qualification are disclosed. The system for ice crystal detection may include an aircraft weather radar and processing circuitry. The aircraft weather radar may perform scans at one or more elevations at successive times. The processing circuitry may calculate power and reflectivity values based on the scans. The processing circuitry may further compare the power and reflectivity values to threshold values to determine the presence of ice water content. The processing circuitry may display different colors on a display for areas in which the power and reflectivity values are lower than the threshold values.

A device, a memory medium, a computer program, and a computer-implemented method for validating a data-based model for classifying an object into a class for an object type or a function type for a driver assistance system of a vehicle. The classification is determined as a function of a digital signal using the data-based model. A reference classification for the object is determined as a function of the digital signal, using a reference model. It is checked, as a function of the classification and the reference classification, whether or not the classification of the data-based model for the object is correct, and the data-based model is validated or not validated, depending on whether or not the classification is correct. The classification and the reference classification are determined for a set of digital signals that are associated with different distances between the object and a reference point.

A device, a memory medium, a computer program, and a computer-implemented method for validating a data-based model for classifying an object into a class for an object type or a function type for a driver assistance system of a vehicle. The classification is determined as a function of a digital signal using the data-based model. A reference classification for the object is determined as a function of the digital signal, using a reference model. It is checked, as a function of the classification and the reference classification, whether or not the classification of the data-based model for the object is correct, and the data-based model is validated or not validated, depending on whether or not the classification is correct. The classification and the reference classification are determined for a set of digital signals that are associated with different distances between the object and a reference point.