A subsurface imaging technique using distributed sensors is introduced. Instead of monostatic transceivers employed in conventional ground penetrating radars, the proposed technique utilizes bi-static transceivers to sample the reflected signals from the ground at different positions and create a large two-dimensional aperture for high resolution subsurface imaging. The coherent processing of the samples in the proposed imaging method eliminates the need for large antenna arrays for obtaining high lateral resolution images. In addition, it eliminates the need for sampling on a grid which is a time-consuming task in imaging using ground penetration radar. Imaging results show that the method can provide high-resolution images of the buried targets using only samples of the reflected signals on a circle with the center at the transmitter location.

A method and control system for generating and updating digital maps using a plurality of passages along a road portion by at least one road vehicle is provided. The method comprises obtaining positioning data and sensor data of each passage from the at least one road vehicle. Further, the method comprises forming a sub-map representation of the surrounding environment at each obtained longitudinal position based on the obtained sensor data, and estimating a longitudinal error for each obtained longitudinal position within each segment. Furthermore, the method comprises determining a new plurality of longitudinal positions of each road vehicle for each passage by applying the estimated longitudinal error on each corresponding obtained longitudinal position, and applying the determined new plurality of longitudinal positions on associated sensor data in order to generate a first layer of a map representation of the surrounding environment along the road portion.

A method and control system for generating and updating digital maps using a plurality of passages along a road portion by at least one road vehicle is provided. The method comprises obtaining positioning data and sensor data of each passage from the at least one road vehicle. Further, the method comprises forming a sub-map representation of the surrounding environment at each obtained longitudinal position based on the obtained sensor data, and estimating a longitudinal error for each obtained longitudinal position within each segment. Furthermore, the method comprises determining a new plurality of longitudinal positions of each road vehicle for each passage by applying the estimated longitudinal error on each corresponding obtained longitudinal position, and applying the determined new plurality of longitudinal positions on associated sensor data in order to generate a first layer of a map representation of the surrounding environment along the road portion.

According to an example aspect of the present invention, there is provided a method comprising, transmitting by a wireless device, during a first phase, a first probe signal associated with a user and receiving a reflected version of the first probe signal, transmitting by the wireless device, during the first phase, the reflected version of the first probe signal to a ground truth classifier, transmitting by the wireless device, during a second phase, a second probe signal associated with the user and receiving a reflected version of the second probe signal and transmitting by the wireless device, during the second phase, the reflected version of the second probe signal to a trusted apparatus.

Deep learning to improve or gauge the performance of a surface-penetrating radar (SPR) system for localization or navigation. A vehicle may employ a terrain monitoring system including SPR for obtaining SPR signals as the vehicle travels along a route. An on-board computer including a processor and electronically stored instructions, executable by the processor, may analyze the acquired SPR images and computationally identify subsurface structures therein by using the acquired image as input to a predictor that has been computationally trained to identify subsurface structures in SPR images.

Deep learning to improve or gauge the performance of a surface-penetrating radar (SPR) system for localization or navigation. A vehicle may employ a terrain monitoring system including SPR for obtaining SPR signals as the vehicle travels along a route. An on-board computer including a processor and electronically stored instructions, executable by the processor, may analyze the acquired SPR images and computationally identify subsurface structures therein by using the acquired image as input to a predictor that has been computationally trained to identify subsurface structures in SPR images.

A measurement system is provided for predicting a future status of a refractory lining that is lined over an inner surface of an outer wall of a manufacturing vessel and exposed to an operational cycle during which the refractory lining is exposed to a high-temperature environment for producing a non-metal and the produced non-metal. The system includes one or more laser scanners and a processor. The laser scanners are configured to conduct one or more pre-operational laser scans of the refractory lining prior to the operational cycle to collect data related to pre-operational cycle structural conditions, and one or more post-operational laser scans of the refractory lining after the operational cycle to collect data related to post-operational cycle structural conditions of the refractory lining. The processor is configured to predict future status of the refractory lining after subsequent operational cycles based on the determined exposure impact of the operational cycle.

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

A method for recognizing a human hand comprises: recognizing a human body target by using a plurality of frames of detection information acquired by a millimeter wave radar within a preset time period; determining whether a new detection target satisfying setting conditions exists within a preset range centering on the human body target, according to a current frame of detection information, the setting conditions including: having a radial velocity; if so, determining the new detection target satisfying the setting conditions as a hand corresponding to the human body target; and if not, determining that the hand corresponding to the human body target does not exist in the current frame.

A processing load in a case where a plurality of different sensors is used can be reduced. An information processing apparatus according to an embodiment includes: a recognition processing unit (15, 40b) configured to perform recognition processing for recognizing a target object by adding, to an output of a first sensor (23), region information that is generated according to object likelihood detected in a process of object recognition processing based on an output of a second sensor (21) different from the first sensor.