A method with grid map generation includes: determining position information of a moving object corresponding to a first time step based on a position sensor of the moving object; determining detection information of nearby objects present around the moving object corresponding to the first time step based on a radio detection and ranging (radar) sensor of the moving object; selecting a still object in a moving range of the moving object from among the nearby objects, based on the position information and the detection information; updating a point cloud determined based on the radar sensor in a previous time step of the first time step, based on the position information and on detection information of the still object comprised in the detection information of the nearby objects; and generating a grid map based on an occupancy probability for each grid of the updated point cloud.

A method with grid map generation includes: determining position information of a moving object corresponding to a first time step based on a position sensor of the moving object; determining detection information of nearby objects present around the moving object corresponding to the first time step based on a radio detection and ranging (radar) sensor of the moving object; selecting a still object in a moving range of the moving object from among the nearby objects, based on the position information and the detection information; updating a point cloud determined based on the radar sensor in a previous time step of the first time step, based on the position information and on detection information of the still object comprised in the detection information of the nearby objects; and generating a grid map based on an occupancy probability for each grid of the updated point cloud.

The present disclosure is directed to 3-D visualization of wireless signal propagation representing wireless signal strength and interference in 3-D space. The present technology can identify a plurality of access points (APs) in the 3-D space, determine a wireless signal strength for each of the plurality of APs, and determine an interference with the wireless signal strength of each of the plurality of APs, wherein the interference is caused by a neighboring AP of the plurality of APs in the 3-D space. The present technology can further present a 3-D visualization of a wireless signal propagation pattern representing the wireless signal strength from each of the plurality of APs in the 3-D space and the interference from the neighboring AP.

The present disclosure is directed to 3-D visualization of wireless signal propagation representing wireless signal strength and interference in 3-D space. The present technology can identify a plurality of access points (APs) in the 3-D space, determine a wireless signal strength for each of the plurality of APs, and determine an interference with the wireless signal strength of each of the plurality of APs, wherein the interference is caused by a neighboring AP of the plurality of APs in the 3-D space. The present technology can further present a 3-D visualization of a wireless signal propagation pattern representing the wireless signal strength from each of the plurality of APs in the 3-D space and the interference from the neighboring AP.

An example autonomous or remotely piloted aircraft includes a virtual aperture radar system including a plurality of antennas relationally positioned on one or more surfaces of the aircraft such that individual beams from each of the plurality of antennas scan respective volumes around the aircraft and the respective volumes together substantially form an ellipsoidal field of regard around the aircraft, and a computing device having one or more processors configured to execute instructions stored in memory for performing functions of: combining the respective volumes together to form an image representative of the ellipsoidal field of regard around the aircraft, and identifying one or more objects within the image.

An example autonomous or remotely piloted aircraft includes a virtual aperture radar system including a plurality of antennas relationally positioned on one or more surfaces of the aircraft such that individual beams from each of the plurality of antennas scan respective volumes around the aircraft and the respective volumes together substantially form an ellipsoidal field of regard around the aircraft, and a computing device having one or more processors configured to execute instructions stored in memory for performing functions of: combining the respective volumes together to form an image representative of the ellipsoidal field of regard around the aircraft, and identifying one or more objects within the image.

This document describes techniques and systems for generating a fused object bounding box based on uncertainty. At least two bounding boxes, each associated with a different sensor, is generated. A fused center point and yaw angle as well as length, width, and velocity can be found by mixing the distributions of the parameters from each bounding box. A discrepancy between the center points of each bounding box can be used to determine whether to refine the fused bounding box (e.g., find an intersection between at least two bounding boxes) or consolidate the fused bounding box (e.g., find a union between at least two bounding boxes). This results in the fused bounding box having a confidence level of the uncertainty associated with the fused bounding box. In this manner, better estimations of the uncertainty of the fused bounding box may be achieved to improve tracking performance of a sensor fusion system.

This document describes techniques and systems for generating a fused object bounding box based on uncertainty. At least two bounding boxes, each associated with a different sensor, is generated. A fused center point and yaw angle as well as length, width, and velocity can be found by mixing the distributions of the parameters from each bounding box. A discrepancy between the center points of each bounding box can be used to determine whether to refine the fused bounding box (e.g., find an intersection between at least two bounding boxes) or consolidate the fused bounding box (e.g., find a union between at least two bounding boxes). This results in the fused bounding box having a confidence level of the uncertainty associated with the fused bounding box. In this manner, better estimations of the uncertainty of the fused bounding box may be achieved to improve tracking performance of a sensor fusion system.

A system and method of inspecting a static monitoring installation, installed in a traffic space. An evaluation circuit is able to create an image of the environment from a signal reflected from an object, wherein at least one reference value of a reference image of the environment is stored in the evaluation circuit, and the at least one reference value is formed from the reflected signals of at least one reference point for a reflected signal

A ground map monitor method comprises obtaining positions of communication nodes in a communications network, selecting transmission and reception nodes from the communication nodes, and measuring bistatic signals between the transmission and reception nodes to determine nominal signal performance characteristics for the bistatic signals, including reflected signal time delays, frequency shifts, and power levels. The method further comprises monitoring the bistatic signals for changes to nominal signal performance characteristics. The method uses discriminators between the nominal signal performance characteristics and a current performance level of the bistatic signals, and compares the discriminators against performance thresholds, to determine whether current signal performance characteristics have varied from their nominal levels. An alert signal is broadcast that a section of a navigation map is not useable for navigation of a vehicle if changes in the current performance level of the bistatic signals exceeds the performance thresholds.