Methods and systems include, in at least one aspect: obtaining from a camera 2D image data of an object, obtaining from a radar device radar data of the object, combining the radar data and the 2D image data to produce 3D location information of the object, and modeling a 2D trace of the object using the 2D image data by finding an initial version of the 2D trace, receiving an initial portion of the 3D location information, extending the initial portion of the 3D location information in accordance with physical-world conditions to find at least one 3D location beyond the initial portion of the 3D location information, projecting the at least one 3D location into a 2D image plane of the camera to locate 2D region, and processing the 2D region in the 2D image data to extend the 2D trace of the object in flight.

A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.

A computer implemented method for determining the position of a vehicle, wherein the method comprises: determining at least one scan comprising a plurality of detection points, wherein each detection point is evaluated from a signal received at the at least one sensor and representing a location in the vehicle environment; determining, from a database, a predefined map, wherein the map comprises a plurality of elements in a map environment, each of the elements representing a respective one of a plurality of static landmarks in the vehicle environment, and the map environment representing the vehicle environment; matching the plurality of detection points and the plurality of elements of the map; determining the position of the vehicle based on the matching; wherein the predefined map further comprises a spatial assignment of a plurality of parts of the map environment to the plurality of elements, and wherein the spatial assignment is used for the matching.

The present invention provides a radar target detection method based on estimation before detection (EBD), which comprises: obtaining pre-detect targets (PDTs) based on conventional pulse-Doppler processing and pre-detection; estimating ranges and speeds of PDTs, i.e., performing parameter EBD; establishing a dimension-reduction observation model of a received signal based on PDTs and parameter thereof; reconstructing a target vector in the dimension-reduction observation model based on a sparse recovery algorithm; and designing a generalized likelihood ratio detector based on the reconstruction result for target detection. The method of the present invention can significantly reduce the radar signal processing loss, and the target detector used in the method has the constant false alarm rate (CFAR) property, so that the weak target detection performance can be greatly improved.

The learning device includes an acquisition unit, a learning data generation unit, and a learning processing unit. The acquisition unit acquires operation data generated during an operation of a radar device and the operation history data indicating operations performed by an operator on the radar device from the radar device. The learning data generation unit generates the learning data using the operation data and the operation history data. The learning processing unit learns an operation determination model that determines an operation to be performed on the radar device based on the operation data, using the learning data.

A system for virtual aperture array radar tracking includes a transmitter that transmits first and second probe signals; a receiver array including a first plurality of radar elements positioned along a first radar axis; and a signal processor that calculates a target range from first and second reflected probe signals, corresponds signal instances of the first reflected probe signal to physical receiver elements of the radar array, corresponds signal instances of the second reflected probe signal to virtual elements of the radar array, calculates a first target angle between a first reference vector and a first projected target vector from the first reflected probe signal, and calculates a position of the tracking target relative to the radar array from the target range and first target angle.

An object detection method includes: obtaining a first offset value and a second offset value, setting a first detection threshold value by adding the first offset value to a first reference threshold value, setting a second detection threshold value by adding the second offset value to a second reference threshold value, obtaining a detection input, and performing target detection upon the detection input according to at least the first detection threshold value and the second detection threshold value. The first offset value is different from the second offset value. The first reference threshold value is determined for detecting if at least one object with a first value of an object characteristic exists. The second reference threshold value is determined for detecting if at least one object with a second value of the object characteristic exists. The second value is different from the first value.

An object detection apparatus detects a target object present in a periphery of a moving body. The object detection apparatus derives recognition information indicating a state of a target object, and predicts a state of the target object at a next second observation timing, based on the recognition information derived at a first observation timing. The object detection apparatus derives a score based on a degree of difference between a state of the target object observed at the second observation timing and a next state of the target object predicted at the first observation timing. The object detection apparatus derives a reliability level by statistically processing scores related to the target object derived at a plurality of observation timings from past to present. In response to the reliability level satisfying a predetermined reference, the object detection apparatus determines that the target object related to the reliability level is actually present.

An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.

In an embodiment, a method includes: receiving reflected radar signals with a millimeter-wave radar; generating a displacement signal indicative of a displacement of a target based on the reflected radar signals; filtering the displacement signal using a bandpass filter to generate a filtered displacement signal; determining a first rate indicative of a heartbeat rate of the target based on the filtered displacement signal; tracking a second rate indicative of the heartbeat rate of the target with a track using a Kalman filter; updating the track based on the first rate; and updating a setting of the bandpass filter based on the updated track.