A maritime radar system is provided, comprising a transmitter, a receiver, and one or more processors arranged to provide range and azimuth discrimination of a detection area by performing a delay/Doppler analysis of the echo of a single beam transmitted by the transmitter and received by the receiver.

Systems and methods involve detecting objects using a radar system of a vehicle. Tracks of the objects are initiated in a track database. The tracks store data, respectively, for the objects and are updated based on additional detections of the objects. The tracks of the objects are initially unclassified tracks. Two tracks corresponding to two of the objects are selected as a candidate pair. Criteria are applied to the candidate pair to determine whether one track is of a ghost object and another track is of a true object corresponding with the ghost object. The ghost object represents detection of the true object in an incorrect location. The candidate pair is classified as tracks of a true object and ghost object pair based on determining that the one track is of the ghost object and the other track is of the true object corresponding with the ghost object.

A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

To provide a radar device capable of tracking a target accurately and stably, taking into account the possibility that a plurality of echoes may merge. A radar device is provided with a tracking processing unit, a merging possibility calculating unit and a preliminary handling processing unit. The merging possibility calculating unit calculates an echo merging possibility, which is the possibility that echo merging, whereby a plurality of echoes become integrated, will occur in the future, based on movement information including the position and speed of a target. The preliminary handling processing unit performs an advance handling process, which is a process for handling future occurrences of echo merging, based on the echo merging possibility.

A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

Methods, systems, and apparatus, including medium-encoded computer program products, for 3D flight tracking of objects include a method including determining a golf ball trajectory based on observations by sensor(s), extrapolating the trajectory backward in time, calculating distance measure(s) between the extrapolated trajectory and physical locations, estimating a systemic error for observation(s), wherein the systemic error affects observed ball positions, estimating a stochastic error associated with the observation(s), wherein the stochastic error affects an angle of a trajectory determined from observed ball positions, combining the estimated systemic and stochastic errors to form error measure(s) for the distance measure(s), identifying one of the physical locations as an origin for the golf ball when the error measure(s) satisfy a criterion, and waiting for additional observations of the golf ball by the sensor(s) when the error measure(s) do not satisfy the criterion.

A method of identifying item selection by a user, the method comprising: receiving signals at a receiver of a fixed terminal from a transmitter of a mobile terminal associated with the user, generating a signature at the receiver of the fixed terminal of the movement of the user based on changes in the signals received from the transmitter, matching the signature with prior stored movement information to determine the movement of the user, and identifying the item being selected by the user based on the determined movement of the user.

Camera data and radar echoes are received from the surroundings. At least one radar echo is assigned to a delimiting frame of an object detected on the basis of a camera, the delimiting frame being generated using the camera data by comparing corresponding azimuth angles and specified distances of the radar echo and the object detected on the basis of a camera. In the event of a successful assignment, a distance which is assumed on the basis of a camera is corrected according to the distance of the respective detected object in the surroundings, said distance being determined in a radar-based manner. The respective delimiting frame together with the corrected distance is then output as an object data set which indicates a successful object detection.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular estimation using machine learning. In particular, a radar system 102 includes an angle-estimation module 504 that employs machine learning to estimate an angular position of one or more objects (e.g., users). By analyzing an irregular shape of the radar system 102's spatial response across a wide field of view, the angle-estimation module 504 can resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar system 102 to correctly identify the angular position of the object. Using machine-learning techniques, the radar system 102 can achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.

A method for tracking an object can include receiving first data input including first feature values of features that indicate a first position. The method can further include generating a first grid of cells representing an object track with the received feature values within an extent of the first grid of cells. The method can further include receiving second data input including second feature values of the features that indicate a second position. The method can further include, in response to determining the second feature values are within the extent of the first grid of cells adding a point corresponding to the second feature values to the first grid of cells to associate the point to an object track.