A method and system is disclosed for determining a probability that an encountered platform was of a specific type given that a plurality of emitters exist on the platform and each emitter has a computed probability that it is of each of a set of types. A preprocessing stage operates on a description of the environment and determines the probability of a set of events that are independent of any observation. A runtime processing stage uses the terms computed in the preprocessing stage along with data assembled from a set of observations to determine the conditional probability that a particular platform type was the type encountered.

There is provided radar operation in a wireless communication system. A method comprises transmitting a random access message for one or more communication resources for a radar operation of a wireless device of a wireless communication system, receiving a grant for at least one communication resource for the radar operation of the wireless device, selecting a radar sequence for generating a radar signal, and transmitting the radar signal using the selected radar sequence in the communication resource.

This disclosure concerns a method for mitigating radar interference in an area, carried out by a first radar sensor device on a first vehicle and including: transmitting to a broadcasting center, via a wireless communication, navigation information of the first radar sensor device, radar parameter information including the current radar operating parameters of the first radar sensor device and mitigation capability information indicating the capabilities of the first radar sensor device to change one or more radar parameters and mitigate radar interference; receiving from the broadcasting center an updated list of radar sensor devices in the area including, for each radar sensor device, a navigation information and a radar parameter information, the radar parameter information for the first radar sensor device including a new radar parameter assigned in compliance with the transmitted mitigation capability information; and activating the new radar parameter in the first radar sensor device.

A frequency band state determining method and a related device are provided. The method includes: A detection apparatus determines an intermediate frequency signal based on a detected interfering signal in an environment and an oscillation signal that belongs to a first frequency band. The detection apparatus performs first processing on the intermediate frequency signal, to obtain a first detection result, and when the first detection result indicates that the first frequency band is busy, the detection apparatus performs second processing on the intermediate frequency signal, to obtain a second detection result. The detection apparatus determines a state of the first frequency band based on the second detection result. The detection apparatus may be a radar, and the radar may work in a use scenario of a cooperative radar.

A radar data processing device includes at least one analog-to-digital converter (ADC) configured to digitize a plurality of input signals, wherein each input signal includes radar chirp and radar chirp reflection information received at one of a plurality of receiver antennas. The radar data processing device also includes Fast Fourier Transform (FFT) logic configured to generate FFT output samples based on each digitized input signal, wherein at least some of the generated FFT output samples are across antenna FFT output samples associated with at least two of the plurality of receiver antennas. The radar data processing device also includes a processor configured to determine a plurality of object parameters based on at least some of the generated FFT output samples, wherein the processor uses a neural network classifier trained to provide a confidence metric for at least one of the plurality of object parameters.

Ranging and detection data is processed to identify or correct for multipath reflection. A sensor point that represents a location of an object, the location based on an incidence of an electromagnetic wave received at a sensor is obtained. The first sensor point is determined to be a product of multipath reflection. A first point of reflection on a surface of a surface model is determined. The location of the first sensor point is corrected based on the first point of reflection on the surface of the surface model.

Machine learning network trained to tune settings and optimize images. In accordance with one aspect, a method is provided for image optimization with a medical ultrasound scanner. A medical ultrasound scanner images a patient using first settings. A first image from the imaging using the first settings and patient information for the patient are input to a machine-learned network. The machine-learned network outputs second settings in response to the inputting of the first image and the patient information. The medical ultrasound scanner re-images the patient using the second settings. A second image from the re-imaging is displayed.

In accordance with an embodiment, a computer-implemented method includes obtaining a time sequence of measurement frames of a radar measurement of a scene comprising an object; based on multiple subsequent measurement frames of the time sequence of measurement frames, determining one or more one-dimensional (1-D) time series of respective observables of the radar measurement associated with the object; and based on the one or more 1-D time series, determining a motion class of a motion performed by the object using a classification algorithm

A wireless communication system having base stations and remotely located terminal units. The base stations and the remotely located terminal units communicate data over operational wireless communication links assigned to respective sub-channels having tiles separated by frequency and time. Detectors for analyzing extraneous received signals in unassigned tiles of the communication links discriminate between a first type of extraneous signals detected in unassigned tiles of one sub-frame and also detected in other unassigned tiles, and a second type of extraneous signals detected in the unassigned tiles but not detected in other unassigned tiles. The reaction of the base stations is different based on the type of extraneous signals.

A system for providing integrated detection and deterrence against an unmanned vehicle including but not limited to aerial technology unmanned systems using a detection element, a tracking element, an identification element and an interdiction or deterrent element. Elements contain sensors that observe real time quantifiable data regarding the object of interest to create an assessment of risk or threat to a protected area of interest. This assessment may be based e.g., on data mining of internal and external data sources. The deterrent element selects from a variable menu of possible deterrent actions. Though designed for autonomous action, a Human in the Loop may override the automated system solutions.