A sensor system includes a camera module and a radar module, wherein the camera module and the radar module are housed separately and detachably, and the sensor system is mounted in a cabin of a vehicle.

An information transmission method of a mobile reception terminal that is capable of connecting to a server over a network and capable of receiving a direct wave and a reflected wave of radio waves transmitted by a transmission station that has a fixed position and transmits radio waves of a same modulation scheme continuously or periodically, includes: creating a delay profile of reception of the direct wave and the reflected wave that indicates a time difference between the direct wave and the reflected wave; and transmitting the delay profile to the server over the network.

Systems and methods are provided for morphological components analysis (MCA) techniques for efficient maritime target detection. Embodiments of the present disclosure provide systems, methods, and devices for determining the free parameter λ for MCA analysis. Embodiments of the present disclosure using MCA utilize effective pre-processing step(s) that separate target signals from clutter, thereby improving the overall performance of subsequent target detection processing. Systems and methods in accordance with embodiments of the present disclosure can optimize the value of the parameter λ, significantly affecting MCA performance.

A result acquisition unit repeatedly acquires measurement results from an environment monitoring sensor that emits probe waves to a probe region and measures the distance and the direction to a reflection point at which the probe waves are reflected. A probability calculation unit calculates a detection probability for each reflection point in accordance with the measurement results acquired by the result acquisition unit. A type determination unit determines the type of the target having the reflection point in accordance with the detection probability calculated by the probability calculation unit.

A method is described for locating and/or classifying at least one object, a radar sensor that is used including at least one transmitter and at least one receiver for radar waves. The method includes: the signal recorded by the receiver is converted into a two- or multidimensional frequency representation; at least a portion of the two- or multidimensional frequency representation is supplied as an input to an artificial neural network, ANN that includes a sequence of layers with neurons, at least one layer of the ANN being additionally supplied with a piece of dimensioning information which characterizes the size and/or absolute position of objects detected in the portion of the two- or multidimensional frequency representation; the locating and/or the classification of the object is taken from the ANN as an output.

A radar calibration method, an electronic device, a roadside device and a storage medium are provided, which are related to the field of intelligent transportation. The method includes acquiring reference position information respectively corresponding to M reference objects collected by a radar in a coordinate system of the radar within a preset time length, wherein M is an integer greater than or equal to 1; determining N pieces of track information based on the reference position information respectively corresponding to the M reference objects, wherein N is an integer greater than or equal to 1; and determining a calibration parameter of the radar based on the N pieces of track information and relevant information of a high-precision map, wherein the calibration parameter of the radar is used for representing a transformation relation between the coordinate system of the radar and a target coordinate system.

An information processing device according to one aspect of the present invention includes at least one memory storing instructions; and at least one processor coupled to the memory and configured to execute the instructions to: extract a candidate point which is a point contributing to a signal at a target point, based on a position of the target point in a three-dimensional space and a shape of an observed object, the target point being a point specified in an intensity map of the signal from the observed object, the intensity map being acquired by a radar; and generate an image indicating a position of the candidate point in a spatial image capturing the observed object.

A method for estimating a velocity of a target using a host vehicle equipped with a radar system includes determining a plurality of radar detection points, determining a compensated range rate, and determining an estimation of a first component of a velocity profile equation of the target and an estimation of a second component of the velocity profile equation of the target by using an iterative methodology comprising at least one iteration. The estimations and of the first and second components and of the velocity profile equation are not determined from a further iteration if at least one statistical measure representing the deviation of an estimated dispersion of the estimations and of the first and second components, and of a current iteration from a previous iteration and/or the deviation of an estimated dispersion of the residual from a predefined dispersion of the range rate meets a threshold condition.

image data captured from a traveling vehicle is considered, and it is not possible to reduce the transmission band of the image data. It is assumed that a radar 4 mounted in a traveling vehicle 10 detects a certain distant three-dimensional object at a time T in a direction of a distance d1 [m] and an angle θ1 . Since the vehicle 10 travels at a vehicle speed Y [km/h], it is predicted that a camera 3 is capable of capturing the distant three-dimensional object at a time (T+ΔT) and an angle ϕ1 or at a distance d2 [m]. Therefore, if a control unit 2 outputs a request to the camera 3 in advance, so as to cut out an image of the angle ϕ1 or the distance d2 [m] at the time (T+ΔT), when the time (T+ΔT) comes, the camera 3 transfers a whole image and a high-resolution image being a cutout image of only a partial image, to the control unit 2.

Disclosed are techniques for employing deep learning to analyze radar signals. In an aspect, an on-board computer of a host vehicle receives, from a radar sensor of the vehicle, a plurality of radar frames, executes a neural network on a subset of the plurality of radar frames, and detects one or more objects in the subset of the plurality of radar frames based on execution of the neural network on the subset of the plurality of radar frames. Further, techniques for transforming polar coordinates to Cartesian coordinates in a neural network are disclosed. In an aspect, a neural network receives a plurality of radar frames in polar coordinate space, a polar-to-Cartesian transformation layer of the neural network transforms the plurality of radar frames to Cartesian coordinate space, and the neural network outputs the plurality of radar frames in the Cartesian coordinate space.