Various systems and methods for optimizing use of environmental and operational sensors are described herein. A system for improving sensor efficiency includes object recognition circuitry implementable in a vehicle to detect an object ahead of the vehicle, the object recognition circuitry configured to use an object detection operation to detect the object from sensor data of a sensor array, and the object recognition circuitry configured to use at least one object tracking operation to track the object between successive object detection operations; and a processor subsystem to: calculate a relative velocity of the object with respect to the vehicle; and configure the object recognition circuitry to adjust intervals between successive object detection operations based on the relative velocity of the object.

A detection system for a vehicle, including at least one detection device operable for detecting objects in a detection area. The detection device also includes a first emission antenna, a first reception antenna, and a plurality of extension portions. One of the plurality of extension portions is integrally formed as part of the first emission antenna, and another of the plurality of extension portions is integrally formed as part of the first reception antenna. The first emission antenna generates an emission wave at a predetermined angle which contacts objects in the first detection area and deflects of the objects in the first detection area, and returns to the first reception antenna as a return wave. In an embodiment, the first emission antenna includes at least one Vivaldi wing, and the first reception antenna includes at least one Vivaldi wing.

An object recognition device 80 includes a scene determination unit 81, a learning-model selection unit 82, and an object recognition unit 83. The scene determination unit 81 determines, based on information obtained during driving of a vehicle, a scene of the vehicle. The learning-model selection unit 82 selects, in accordance with the determined scene, a learning model to be used for object recognition from two or more learning models. The object recognition unit 83 recognizes, using the selected learning model, an object in an image to be photographed during driving of the vehicle.

Examples disclosed herein relate to a radar system in an autonomous vehicle for object detection and classification. The radar system has a radar module having a dynamically controllable beam steering antenna and a perception module. The perception module includes a machine learning module trained on a first set of data and retrained on a second set of data to generate a set of object locations and classifications, and a classifier to use velocity information combined with the set of object locations and classifications to output a set of classified data.

Apparatuses and methods for studying and recording acoustic/electromagnetic responses of devices that contain electrical or electronic circuits help to improve the effectiveness of detecting and characterizing electronics used with an acoustic radar. The apparatuses and methods generate, measure, and record the interactions of electromagnetic (EM) and acoustic waves at or inside those devices that are to be detected using acoustic radar.

An example method can include receiving, at a sensor, a signal associated with a motion of a target, processing the signal via a first filter having a first motion model and a second filter having a second motion model to yield a first tracking output and a second tracking output for the target, and weighting the first tracking output and second tracking output according to how well each of the first motion model and second motion model represents the motion of the target, to yield a first weight for the first tracking output and a second weight for the second tracking output. The method can include combining the first tracking output and second tracking output to yield a fused tracking output and sending, to a fusion system, the fused tracking output, the first weight associated with the first tracking output and the second weight associated with the second tracking output.

An example method can include receiving, at a sensor, a signal associated with a motion of a target, processing the signal via a first filter having a first motion model and a second filter having a second motion model to yield a first tracking output and a second tracking output for the target, and weighting the first tracking output and second tracking output according to how well each of the first motion model and second motion model represents the motion of the target, to yield a first weight for the first tracking output and a second weight for the second tracking output. The method can include combining the first tracking output and second tracking output to yield a fused tracking output and sending, to a fusion system, the fused tracking output, the first weight associated with the first tracking output and the second weight associated with the second tracking output.

In an embodiment, a method includes: receiving radar signals with a millimeter-wave radar; generating range data based on the received radar signals; detecting a target based on the range data; performing ellipse fitting on in-phase (I) and quadrature (Q) signals associated with the detected target to generate compensated I and Q signals associated with the detected target; classifying the compensated I and Q signals; when the classification of the compensated I and Q signals correspond to a first class, determining a displacement signal based on the compensated I and Q signals, and determining a vital sign based on the displacement signal; and when the classification of the compensated I and Q signals correspond to a second class, discarding the compensated I and Q signals.

A millimeter-wave real-time imaging based safety inspection system and safety inspection method. The safety inspection system includes a conveying device (10), a millimeter wave transceiver module (11), an antenna array (17, 18), a switch array (16a, 16b), a switch control unit (15a, 15b), a quadrature demodulation and data acquisition module (12), and an image display unit (13). By using an Inverse Synthetic Aperture Radar (ISAR) imaging principle, the millimeter-wave real-time imaging based safety inspection system performs real-time imaging on an object to be inspected when the object moves, so that not only the imaging speed is improved, but also the field of view is enlarged. A safety inspector can determine whether an inspected person carries dangerous goods by observing a three-dimensional diagram of the inspected person, thereby eliminating the inconvenience caused by back-and-forth movement of a safety inspection device used by the safety inspector around the inspected person.

A millimeter-wave real-time imaging based safety inspection system and safety inspection method. The safety inspection system includes a conveying device (10), a millimeter wave transceiver module (11), an antenna array (17, 18), a switch array (16a, 16b), a switch control unit (15a, 15b), a quadrature demodulation and data acquisition module (12), and an image display unit (13). By using an Inverse Synthetic Aperture Radar (ISAR) imaging principle, the millimeter-wave real-time imaging based safety inspection system performs real-time imaging on an object to be inspected when the object moves, so that not only the imaging speed is improved, but also the field of view is enlarged. A safety inspector can determine whether an inspected person carries dangerous goods by observing a three-dimensional diagram of the inspected person, thereby eliminating the inconvenience caused by back-and-forth movement of a safety inspection device used by the safety inspector around the inspected person.