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.

Route planning is provided to a vehicle (or vehicle driver) based at least in part on the type of vehicle and the terrain conditions between the originating location and destination. The vehicle may employ a terrain monitoring system including a surface-penetrating radar (SPR) system for obtaining SPR signals as the vehicle travels along a route; the obtained SPR signals may be used for navigation against reference images associated with the route. In some embodiments, a navigation server bases route selection in part on the terrain associated with various routes and characteristics of the vehicle.

Systems, methods, and other embodiments relate to determining the speed of a vehicle. In one embodiment, a method includes receiving a first frame of data generated by a first sensor of a vehicle, the first frame of data including a first set of angular positions associated with a first set of objects in the environment. The method includes receiving a second frame of data generated by a second sensor of the vehicle, the second frame of data including a second set of angular positions associated with a second set of objects in the environment. The method includes generating a speed estimate for the vehicle in relation to the first set of objects and the second set of objects based at least in part on the first set of angular positions of the first frame of data and the second set of angular positions of the second frame of data.

An RFID detector suitable for use in a passive RFID tag system that employs frequency hopping spread spectrum (FHSS) operation obtains an indication of at least one characteristic of a CW RF signal employing a hopped-to carrier frequency that is being transmitted from an RFID tag reader, e.g., for use in activating the RFID tag to be located, the indication of the characteristic being obtained based on a signal that is received from a source other than the RFID detector. The RFID detector may use the obtained indication of the characteristic of the CW RF signal to determine at least one position related parameter for the RFID tag. A location, e.g., of the tag, of a group of tags, of the RFID detector, or of another RFID detector, may be determined based on the position parameter.

Surface-penetrating radar (SPR) systems provide localization information for provision to a blockchain application. SPR can be used in environments, such as cities, where multipath or shadowing degrades GPS accuracy, or as an alternative to optical sensing approaches that cannot tolerate darkness or changing scene illumination or whose performance can be adversely affected by variations in weather conditions. In particular, SPR can be used to acquire scans containing surface and subsurface features as a vehicle traverses terrain, and the acquired data scans may be compared to reference scan data that was previously acquired within the same environment in order to localize vehicle position within the environment. If the reference scan data has been labeled with geographic location information, a vehicle's absolute location can thereby be determined.

The various technologies presented herein relate to utilizing direction of arrival (DOA) data to determine various flight parameters for an aircraft A plurality of radar images (e.g., SAR images) can be analyzed to identify a plurality of pixels in the radar images relating to one or more ground targets. In an embodiment, the plurality of pixels can be selected based upon the pixels exceeding a SNR threshold. The DOA data in conjunction with a measurable Doppler frequency for each pixel can be obtained. Multi-aperture technology enables derivation of an independent measure of DOA to each pixel based on interferometric analysis. This independent measure of DOA enables decoupling of the aircraft velocity from the DOA in a range-Doppler map, thereby enabling determination of a radar velocity. The determined aircraft velocity can be utilized to update an onboard INS, and to keep it aligned, without the need for additional velocity-measuring instrumentation.

Systems and methods for using velocity measurements to adjust Doppler filter bandwidth are provided herein. In certain embodiments, a method for adjusting bandwidth for at least one Doppler filter in a Doppler beam sharpened radar altimeter comprises receiving a velocity measurement; adjusting the bandwidth of the at least one Doppler filter based on the velocity measurement; and transmitting a radar beam, wherein the radar beam is aimed toward a surface. The method further comprises receiving at least one reflected signal, wherein the at least one reflected signal is a reflection of the radar beam being reflected off of at least one portion of the surface; and filtering the at least one reflected signal with the at least one Doppler filter to form at least one Doppler beam.

Methods and systems are provided for controlling a vehicle action based on a condition of a road on which a vehicle is travelling, including: obtaining first sensor data as to a surface of the road from one or more first sensors onboard the vehicle; obtaining second sensor data from one or more second sensors onboard the vehicle as to a measured parameter pertaining to operation of the vehicle or conditions pertaining thereto; generating a plurality of road surface channel images from the first sensor data, wherein each road surface channel image captures one of a plurality of facets of properties of the first sensor data; classifying, via a processor using a neural network model, the condition of the road on which the vehicle is travelling, based on the measured parameter and the plurality of road surface channel images; and controlling a vehicle action based on the classification of the condition of the road.

A railroad car location, speed and heading sensor system including at least one self-powered, tie-mounted sensor node that is applicable universally to different railroad settings without using track circuits, inductive loops, radar systems, and wheel counters and associated disadvantages. Reliable and relatively low cost deterministic and redundant car presence detection is realized when multiple sensor nodes are arranged in a network, which may be a wireless mesh network, that is not affected by environmental conditions.

A railroad car location, speed and heading sensor system including at least one self-powered, tie-mounted sensor node that is applicable universally to different railroad settings without using track circuits, inductive loops, radar systems, and wheel counters and associated disadvantages. Reliable and relatively low cost deterministic and redundant car presence detection is realized when multiple sensor nodes are arranged in a network, which may be a wireless mesh network, that is not affected by environmental conditions.