A monitoring system for an aircraft can include an image sensor and a radar sensor. The system can provide noise compensation to a radar sample corresponding to a return radar signal received by the radar sensor based on information detected by the image sensor. The system can identify one or more object types in the image captured by the image sensor and then translate the identified object types to corresponding positions on a map. The system can correlate the radar sample to a position on the map and any object type located at that position can be identified. The system can then select a noise pattern that corresponds to the identified object type from the map and use the selected noise pattern to compensate the radar sample.

Trapped or confined individuals may be located and rescued by detecting their movement using reflected, radio frequency signals over a range of multiple antenna polarities.

A field of awareness (FOA) system provides an operator of a vessel with intuitive object detection and positioning information. The system may comprise an FOA cloud server and an FOA unit. The FOA cloud server may be configured to perform a machine learning training operation to modify an FOA model based on a location-based relationship between training radar data and truth data. The FOA unit may be disposed on the vessel and may comprise processing circuitry configured to apply radar data to the FOA model to perform a comparison to determine a matched model signature, an associated matched object type, and an icon representation for the object of interest. The processing circuitry also be configured to control the display device to render the icon representation of the object at a position relative to a representation of the vessel based on the relative object position.

A method with grid map generation includes: determining position information of a moving object corresponding to a first time step based on a position sensor of the moving object; determining detection information of nearby objects present around the moving object corresponding to the first time step based on a radio detection and ranging (radar) sensor of the moving object; selecting a still object in a moving range of the moving object from among the nearby objects, based on the position information and the detection information; updating a point cloud determined based on the radar sensor in a previous time step of the first time step, based on the position information and on detection information of the still object comprised in the detection information of the nearby objects; and generating a grid map based on an occupancy probability for each grid of the updated point cloud.

A radar ranging method includes: obtaining a first signal, where the first signal is a frequency domain signal obtained after low frequency suppression is performed in a beat frequency signal, and the beat frequency signal is a signal obtained by mixing a transmitted signal transmitted by a frequency modulated continuous wave FMCW radar and a received echo signal; performing mean gradient calculation on the first signal in frequency domain to obtain a second signal; and calculating at least one of a speed or a distance of a target object based on a peak signal in the second signal.

Techniques and apparatuses are described that implement a distributed radar system. The distributed radar system includes two or more radar front-end circuits and at least one processor. The radar front-end circuits are distributed within a device at different positions. By partitioning antennas and transceivers across multiple radar front-end circuits instead of consolidating into a single integrated circuit, individual radar front-end circuits can have a smaller footprint than the single integrated circuit. This smaller footprint enables the radar front-end circuits to be integrated within space-constrained devices. The smaller footprint also provides additional flexibility in positioning the radar front-end circuits away from other components within the device that can cause interference. This can reduce the amount of interference seen by the distributed radar system.

Techniques and apparatuses are described that implement a distributed radar system. The distributed radar system includes two or more radar front-end circuits and at least one processor. The radar front-end circuits are distributed within a device at different positions. By partitioning antennas and transceivers across multiple radar front-end circuits instead of consolidating into a single integrated circuit, individual radar front-end circuits can have a smaller footprint than the single integrated circuit. This smaller footprint enables the radar front-end circuits to be integrated within space-constrained devices. The smaller footprint also provides additional flexibility in positioning the radar front-end circuits away from other components within the device that can cause interference. This can reduce the amount of interference seen by the distributed radar system.

Radio frequency motion sensors may be configured for operation in a common vicinity so as to reduce interference. In some versions, interference may be reduced by timing and/or frequency synchronization. In some versions, a master radio frequency motion sensor may transmit a first radio frequency (RF) signal. A slave radio frequency motion sensor may determine a second radio frequency signal which minimizes interference with the first RF frequency. In some versions, interference may be reduced with additional transmission adjustments such as pulse width reduction or frequency and/or timing dithering differences. In some versions, apparatus may be configured with multiple sensors in a configuration to emit the radio frequency signals in different directions to mitigate interference between emitted pulses from the radio frequency motion sensors.

This document describes techniques and systems for generating a fused object bounding box based on uncertainty. At least two bounding boxes, each associated with a different sensor, is generated. A fused center point and yaw angle as well as length, width, and velocity can be found by mixing the distributions of the parameters from each bounding box. A discrepancy between the center points of each bounding box can be used to determine whether to refine the fused bounding box (e.g., find an intersection between at least two bounding boxes) or consolidate the fused bounding box (e.g., find a union between at least two bounding boxes). This results in the fused bounding box having a confidence level of the uncertainty associated with the fused bounding box. In this manner, better estimations of the uncertainty of the fused bounding box may be achieved to improve tracking performance of a sensor fusion system.

A user identification device according to a disclosed embodiment includes a transmitter for scattering radio-frequency (RF) signals into tissues of a body part of a user, a receiver for receiving the RF signals having passed through the tissues of the body part of the user, a memory for storing parameters of a trained classification algorithm, and a processor for identifying the user by analyzing the received RF signals based on the trained classification algorithm by using the parameters of the trained classification algorithm in response to receiving the RF signals through the receiver.