Remote recovery of acoustic signals from passive sources is provided. Wideband radars, such as ultra-wideband (UWB) radars can detect minute surface displacements for vibrometry applications. Embodiments described herein remotely sense sound and recover acoustic signals from vibrating sources using radars. Early research in this domain only demonstrated single sound source recovery using narrowband millimeter wave radars in direct line-of-sight scenarios. Instead, by using wideband radars (e.g., X band UWB radars), multiple sources separated in ranges are observed and their signals isolated and recovered. Additionally, the see-through ability of microwave signals is leveraged to extend this technology to surveillance of targets obstructed by barriers. Blind surveillance is achieved by reconstructing audio from a passive object which is merely in proximity of the sound source using clever radar and audio processing techniques.

A polarimetric radar system for object classification and road condition estimation includes a radar transmitter unit for transmitting radar waves of different polarizations, a radar receiving unit for receiving radar waves of different polarizations, a radar signal generating unit for generating and providing the radar waves to be transmitted, a signal processing circuitry for processing the generated and received radar waves, and a signal evaluation unit. The signal evaluation unit receives processed signals from the signal processing circuitry, estimates values for a set of predetermined object parameters on the basis of the received processed signals, and selects an object class from a plurality of predetermined object classes upon detecting a match of the estimated values with one out of a plurality of predetermined sets of object parameters. The signal evaluation unit is configured to provide information that is indicative of the at least one classified object.

Systems and methods are provided for generating a radar model for a target object. In embodiments, a target simulation model is received that represents one or more physical aspects of a target object, an environment simulation model is received that represents one or more physical aspects of an environment object, and a target distance parameter is received that identifies a reference distance between the target object and a radar system to be simulated. A simulation model is generated based, at least in part, on the target simulation model, the environment simulation model, and the reference distance, and further based on a target aspect angle that identifies an angular position of the target object in relation to the radar system. Interaction of the radar system with the target object and the environment object is simulated using the simulation model, and results of the simulation are used to generate a range profile for the target object at the target aspect angle, wherein the range profile identifies a radar return strength for the reference distance. The target aspect angle is then incremented, and the operations are repeated until range profiles are generated for the target object at a plurality of target angles amounting to a 360 degree rotation of the target object. The range profiles at the plurality of target angles are then accumulated to generate the radar model for the target object.

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.

The invention concerns a method (1) to detect deformations of, and/or damages to, structures permanently arranged on the earth's surface. In particular, said method (1) comprises: acquiring (11) georeferencing data indicative of geographical reference positions of predefined points of interest of a given structure to be monitored permanently arranged on the earth's surface, wherein said predefined points of interest are representative of a 3D geometry of the given structure without deformations and damages; acquiring (12) a SAR image of an area of the earth's surface where the given structure is arranged, wherein said SAR image is associated with a given reference coordinate system; transforming (13) the geographical reference positions of the predefined points of interest into corresponding expected positions in the given reference coordinate system associated with the acquired SAR image so as to carry out a reprojection of the 3D geometry of the given structure without deformations and damages on the acquired SAR image; identifying (14) in the acquired SAR image the predefined points of interest of the given structure; determining (15) actual positions in the given reference coordinate system associated with the acquired SAR image of the predefined points of interest identified in said SAR image; making a comparison (16) between the expected positions of the predefined points of interest and the corresponding actual positions in the acquired SAR image; and detecting (17) one or more deformations of, and/or one or more damages to, said given structure on the basis of the comparison made.

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.

An object recognition device is configured to perform object recognition based on information from a unit configured to detect an object image in each frame based on a reflected signal of a transmission signal and configured to calculate one or more patterns of a confidence degree regarding the object image detected according to one or more preset calculation methods. The object recognition device includes: a first evaluation value calculator configured to increase a count of a first evaluation value indicating a probability that the object image is a real image based on the one or more patterns of the confidence degree; and a real image determiner configured to determine that the object image is a real image when a cumulative count of the first evaluation value exceeds a comparative value.

Techniques and apparatuses are described that implement motion classification using low-level detections. In particular, a radar system identifies fused detections associated with an object and determines whether the fused detections indicate that the object is moving. If it is determined to be moving or moving perpendicular to the host vehicle, a current motion counter or perpendicular motion counter is incremented, respectively. A current motion flag and/or a perpendicular motion flag are set as true if the current motion counter or the perpendicular motion counter has a value greater than a threshold value, respectively. In response to setting either flag as true, the radar system increments a historical motion counter as true. The host vehicle is then operated based on the current motion flag, the perpendicular motion flag, and the historical motion counter. In this way, the radar system introduces hysteresis to improve the reliability and stability of motion classification.

The present disclosure is directed to processing data associated with a non-radar type sensing device to identify data points associated with a radar type sensing device that are likely secondary radar reflections such that a processor of a sensing apparatus can direct processing resources to processing radar data that are associated with primary radar reflections. The receipt of secondary radar reflections may cause a processor of a sensing apparatus to identify that an object is located at a location when there is no object in that location. Because of this, methods and apparatus of the present disclosure identify and avoid processing radar data that are likely to be associated with a object that does not exist. Eliminating false radar data, therefore, can prevent a processor of a sensing apparats from performing unnecessary processing tasks and can help prevent that processor from making false determinations.

Systems and methods for determining a presence of cargo within a container are described. The determination may comprise transmitting a first electromagnetic signal within the container and receiving a first reflected electromagnetic signal corresponding to the first electromagnetic signal. The first reflected electromagnetic signal may be converted into a set of first magnitude values corresponding to a set of bin values representing a respective distance from the radar device. The first magnitude values may be integrated, using a moving window, over successive subranges of bin values to produce a set of integrated magnitude values. A set of integrated base magnitude values may be subtracted from the set of integrated magnitude values to produce a set of normalized integrated magnitude values. A presence of cargo within the container may then be determined by comparing each of the normalized integrated magnitude values with corresponding threshold values.