A sensing method and a communication device are provided. The sensing method includes: obtaining, by a first device, at least one sensing measurement result based on an eigenvalue of at least one first matrix. The at least one first matrix is obtained based on a time-frequency domain channel matrix. The time-frequency domain channel matrix includes related information of frequency domain channel responses corresponding to a plurality of time-frequency domain sampling points. Each time-frequency domain channel matrix corresponds to one antenna transmit-receive combination. The related information of the frequency domain channel response is obtained by the first device by performing channel estimation on a received first signal. The first matrix is a covariance matrix or a correlation coefficient matrix corresponding to the time-frequency domain channel matrix; and obtaining, by the first device, a target sensing measurement result based on the at least one sensing measurement result.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles. A method for generating virtual sensor data includes simulating, using one or more processors, a three-dimensional (3D) environment comprising one or more virtual objects. The method includes generating, using one or more processors, virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining, using one or more processors, virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth comprises a dimension or parameter of the one or more virtual objects. The method includes storing and associating the virtual sensor data and the virtual ground truth using one or more processors.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as wheel stops or parking barriers. A method for generating virtual sensor data includes simulating a three-dimensional (3D) environment comprising one or more objects. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth includes information about at least one object within the virtual sensor data. The method also includes storing and associating the virtual sensor data and the virtual ground truth.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as wheel stops or parking barriers. A method for generating virtual sensor data includes simulating a three-dimensional (3D) environment comprising one or more objects. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth includes information about at least one object within the virtual sensor data. The method also includes storing and associating the virtual sensor data and the virtual ground truth.

A system for filtering RADAR data includes one or more graphical processing units (GPUs) for performing steps of a filtering process in parallel. For example, a first GPU or first portion of GPU circuitry calculates a threshold parabola for a tensor of RADAR data. In parallel, a second GPU or second portion of GPU circuitry separately reduces and indexes the tensor for comparison to the threshold parabola. The threshold parabola is compared to reduced and indexed data to filter the RADAR data. Importance sampling can also be used to reduce data, e.g., if the RADAR data includes four dimensions (range, Doppler, azimuth, and elevation).

A method for estimating the equivalent radar cross section (RCS) of an object using near-field measurements. The method using a diffraction model of the object in far field and a diffraction model in near field. These models make it possible to determine respectively bases adapted to said object in far field and in near field. The measurement vector is first of all projected onto the base adapted in near field and the components obtained are transformed into components on the base in far field. The vector obtained is then returned into the analysis base of the RCS in order to provide a reconstructed vector. The components of the reconstructed vector are next used for calculating the RCS.

The invention relates to a method of calculating gating scores in the pulse-Doppler radar using a combination of position, power, size of detection and ambiguous Doppler measurement data to effectively overcome the phenomenon of wrongly assigning detections to the target trajectory and creating false targets in noise regions. The method intelligently merges position, power, detection size and ambiguous Doppler measurement data information of the detections in the algorithm of calculating gating score to solve the problem of selection and disputation detections. The method is carried out through two steps, step 1: determine statistical quantities from the standard data set; step 2: gating selection algorithm.

A radar sensor system comprises a first radar sensor and at least a second radar sensor and one or more processors configured to perform acts comprising transmitting a first signal from a first transmit antenna in a first radar sensor and transmitting a second signal from a second transmit antenna in a second radar sensor. The acts further comprise detecting an object at the first radar sensor and the second radar sensor and estimating vector velocity information v.sub.x and v.sub.y for the object. The acts also comprise generating a radar measurement vector z that comprises position information p.sub.x and p.sub.y for the object and incorporating the vector velocity information v.sub.x and v.sub.y into the radar measurement vector z. Additionally, the acts comprise iteratively performing a measurement update using the measurement vector z, with velocity information incorporated therein, and a linear Kalman filter until correct velocity values are determined.

Solutions enabling updating clutter components that can be used in clutter removal when joint communication and sensing, for example, are disclosed. To update clutter components by second clutter components, sets of measurement results of sensing of an environment of which clutter components have been determined are obtained. Then, per a set, multi-dimensional representations of echo signals received as measurement results of the set are transformed at least partly into one-dimensional echo signals. The second clutter components are determined based at least on corresponding one-dimensional echo signals and a clutter matrix representation that comprises elements obtained from previously determined clutter components of the environment.

A method for use in acoustic imaging, comprising: transmitting, from a transmitter, a first sound wave pulse at a first frequency determined by a maximum sampling rate of a receiver; transmitting at least one second sound wave pulse at a frequency substantially equal to the first frequency, the first and at least one second sound wave pulses being transmitted substantially within a fraction of a sample interval of the receiver; receiving and sampling, at the receiver, a reflection of at least two of the first and at least one second pulses to generate a set of receiver samples; and expanding the set of receiver samples, based on the first frequency and a total number of the first and at least one second pulses transmitted, to generate an expanded sample set with a larger number of samples than the set of receiver samples.