A sanitary device includes a radio wave sensor, a control section. The radio wave sensor is configured to obtain information on a sensing target by a reflection wave of an emitted radio wave. The control section is configured to control operation of an instrument based on a sensing signal including a first signal and a second signal outputted from the radio wave sensor. Difference between a phase of the second signal and a phase of the first signal is not less than 60° and not more than 120°. The control section is configured to determine presence or absence of the sensing target based on sum of square of difference between a value of the first signal and a first reference value and square of difference between a value of the second signal and a second reference value.

Parking detection sensor (100) has a Doppler sensor (110), a magnetic sensor (120) that detects magnetism on XYZ axes, a change point detection unit (130) that detects a change point in the output of the Doppler sensor (110) and the magnetic sensor (120), a level difference detection unit (140) that detects the magnetic level difference over time in the output of the Doppler sensor (110) and the magnetic sensor (120), and a state assessment unit (150) that assesses the parking state of a vehicle on the basis of the detection results of the change point detection unit (130) and the detection results of the level difference detection unit (140).

Multi-timescale Doppler processing and associated systems and methods are provided. In one example, a receiver receives radar return data, where the radar return data is associated with reflections, from a scene, of a plurality of transmitted radar signals. The radar return data is processed to obtain a plurality of sets of detection data, where each set of detection data of the plurality of sets of detection data is associated with a respective processing size. Target data associated with the scene is generated based at least in part on the plurality of sets of detection data. Related systems and methods are also provided.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for multistatic radar communications. In one aspect, a wireless communication device may determine a distance and direction of one or more receiving devices. The wireless communication device may transmit, to the one or more receiving devices, timing information indicating a timing relationship between a codeword sequence and one or more pulses. The wireless communication device may transmit a respective codeword of the codeword sequence in the direction of each of the one or more receiving devices. The wireless communication device may further transmit the one or more pulses in a plurality of directions. The wireless communication device may receive feedback from at least one of the one or more receiving devices and determine ranging information about an object based on the feedback and the distance or direction of at least one receiving device.

A distributed radar system, apparatus, architecture, and method is provided for coherently combining physically distributed radars to jointly' produce target scene information in a coherent fashion without sharing a common local oscillator (LO) reference by configuring a first (slave) radar to apply fast and slow time processing steps to target returns generated from a second (master) radar, to compute an estimated frequency offset and an estimated phase offset between the first and second radars based on information derived from the fast and slow time processing steps, and to apply the estimated frequency offset and estimated phase offset to generate a bi-static virtual array aperture at the first radar that is coherent in frequency and phase with a mono-static virtual array aperture generated at the second radar, thereby achieving better sensitivity, finer angular resolution, and low false detection rate.

This disclosure relates generally to tracking motion of target in indoor environment. The method includes estimating an initial position of the target in a mesh grid form based on radar data captured from radar devices installed in the indoor environment. For a subsequent target movement, a subsequent position of the target is estimated in the mesh grid form based on the initial position and a resultant velocity vector of the target. A number of outlier grid-points is computed with a threshold number, and based on comparison the outlier grid-points are either replaced with interpolated grid-points or the subsequent position of the target is repaired based on a probable position of the target obtained from at least one of a linear regression based analysis of prior positions of the target, prior knowledge of the target velocity and sampling interval, and a trilateration based technique.

Aspects and implementations of the present disclosure relate to filtering of return points from a point cloud based on radial velocity measurements. An example method includes: receiving, by a sensing system of an autonomous vehicle (AV), data representative of a point cloud comprising a plurality of return points, each return point comprising a radial velocity value and position coordinates representative of a reflecting region that reflects a transmission signal emitted by the sensing system; applying, to each of the plurality of return points, at least one threshold condition related to the radial velocity value of a given return point to identify a subset of return points within the plurality of return points; removing the subset of return points from the point cloud to generate a filtered point cloud; and identifying objects represented by the remaining return points in the filtered point cloud.

A contactless switch device including a reflected signal acquisition unit that acquires a reflected signal of a Doppler radar or a distance measurement radar, an approach and recede detection unit that detects an approach and a recede of a target based on the reflected signal, a target identification unit that identifies the target as a hand waving left and right, up and down, or back and forth, or a thing other than the hand based on a repeated pattern of the approach and the recede of the target, and a switch control unit that executes on-and-off controls on a switch-controlled object based on whether the target is the hand waving left and right, up and down, or back and forth, or the thing other than the hand.

A vehicle radar system (3) and method including a first and second radar sensor arrangement (4a, 4b). Each radar sensor arrangement (4a, 4b) includes at least two transmitter antenna devices (10a1, 10a2) and at least two receiver antenna devices (13a1, 13a2, 13a3, 13a4), where each receiver antenna device (13a1, 13a2, 13a3, 13a4) has a corresponding boresight extension (46a, 46b) that is perpendicular to an antenna plane (57). Each receiver antenna device (13a1, 13a2, 13a3, 13a4) has a corresponding antenna radiation pattern (47a, 47b) that has a lower gain (48a, 48b) in its boresight extension (46a, 46b) than at a certain corresponding first maximum gain azimuth angle (φ.sub.1a, φ.sub.1b) where there is a first maximum gain (49a, 49b). Each radar sensor arrangement (4a, 4b) is mounted such that each first maximum gain (49a, 49b) is directed along a corresponding first maximum gain extension (51a, 51b), such that an overlap part (56) of the antenna radiation patterns (47a, 47b) is formed.

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.