A moving device and an object detection method thereof are provided. A method, performed by a moving device, of generating a map related to an object in a task space includes radiating at least one sensing signal toward aboveground and underground regions in a vicinity of the moving device, receiving signals reflected from an aboveground object located on a ground in the task space and an underground object located under the ground in the task space, and generating the map indicating distribution of the aboveground object and distribution of the underground object, based on the received reflected signals.

An apparatus for detecting objects includes a transceiver configured to generate a radar signal, a radar having a transmit antenna configured to transmit the radar signal, and a receive antenna configured to sense a return signal in response to a transmission of the radar signal. The apparatus also includes a processor configured to detect an object based on the return signal. One or more of the transmit antenna or the receive antenna include offset spiral feed antennas.

A system and method is disclosed for measuring time of flight to an object. A transmitter transmits an electromagnetic signal and provides a reference signal corresponding to the electromagnetic signal. A receiver receives the electromagnetic signal and provides a response signal corresponding to the received electromagnetic signal. A detection circuit is configured to determine a time of flight between the transmitter and the receiver based upon the reference signal and the response signal.

Ground-penetrating radar (GPR) technology enables the detection of hidden objects that are underground or behind walls or other such surfaces. Embodiments of the present invention provide a realistic visualization of the hidden objects through so-called augmented reality techniques. Thanks to such visualization, interaction with hidden objects that are hazardous or delicate is easier and less prone to errors. Also, GPR-based data collection can be performed in non-real time, with object visualization occurring at a later time based on stored data that can also comprise annotations. This capability provides greater flexibility for scheduling activities related to the hidden objects.

A hybrid pulse compression RF system is provided herein in which an enhanced noise waveform and a hybrid waveform are generated to detect a target. For example, the system includes a signal generator that generates an LFM waveform and an enhanced waveform in sequence such that a transmitter of the system transmits the waveforms in the generated sequence in a direction of a possible target. The enhanced waveform may be a partially randomized version of the LFM waveform. If a target is present, the waveforms reflect off the target and are captured by the system in the sequence in which the originally generated waveforms are transmitted. Once captured, the reflected waveforms are processed by the system to generate a hybrid waveform for display such that the range and Doppler resolution and detection capabilities are significantly superior to the state of the art LFM or noise waveform RF systems.

A method for generating a calibration curve of asphalt concrete of a known mix. Initially, a single sample of the known asphalt concrete mix is obtained. The single sample has a known percent voids. A dielectric measurement of the single sample is obtained. Using only the dielectric measurement of the single sample, the sample's known percent voids, and a dielectric of air, a theoretical ideal dielectric for the asphalt concrete mix at 0% voids is computed. A dielectric vs. percent voids calibration curve is generated based on the computed ideal dielectric.

One aspect provides a method, including: obtaining sensor data from a ground penetrating radar (GPR) unit; analyzing, using a processor, the sensor data to detect a first object and a second object, the second object being associated with the first object based on location; identifying, with the processor, an underground pipe feature based on the analyzing; associating a position of the underground pipe feature with a location in a pipe network; selecting a subset of the pipe network including a pipe segment associated with the position of the underground pipe feature; and providing the subset of the pipe network as displayable data to a display device. Other aspects are described and claimed.

Trapped or confined individuals may be located and rescued by detecting their vital signs (e.g., chest movement or heart beat) using reflected, radio frequency signals over a range of multiple antenna polarities.

A multi-scale inspection and intelligent diagnosis system and method for tunnel structural defects includes: a traveling section; a supporting section, disposed on the traveling section, and including a rotatable telescopic platform, where two mechanical arms working in parallel are disposed on the rotatable telescopic platform; an inspection section, mounted on the supporting section, and configured to perform multi-scale inspection on surface defects and internal defects in different depth ranges of a same position of a tunnel structure, and transmit inspected defect information to a control section; and the control section, configured to: construct a deep neural network-based defect diagnosis model; construct a data set by using historical surface defect and internal defect information, and train the deep neural network-based defect diagnosis model; and receive multi-scale inspection information in real time, and automatically recognize types, positions, contours, and dielectric attributes of the internal and surface defects.

Methods, devices and computer systems utilize ground penetrating radar (GPR) embedded in or mounted on a ski or other equipment that is in contact with the snow during use device in order to obtain snow profile data from layers of snow or ice. A GPR device may include a trained model capable of deriving snow profile information based on GPR data and may also be capable of uploading GPR data to an online service. The online service can use the received GPR data to derive snow profile information, distribute snow profile information to user devices, and generate and distribute updated trained models. The online service may access online repositories of additional information and refine the snow profile information or the trained model based on such addition information. Generated snow profile information can be used to provide avalanche risk assessments.