A system for exploiting a transmitted signal from an aircraft or drone to determine parameters of the aircraft or drone's motion. The system includes at least one antenna for receiving the transmitted signal from the aircraft, and an analysis system for analyzing the transmitted signal as compared with stored characteristic anomalies associated with any of the aircraft or drone, and the at least one antenna, for confirming parameters of the aircraft or drone's motion.

A method for configuring a neural network which is designed to map measured data to one or more output variables. The method includes: transformation(s) of the measured data is/are specified which when applied to the measured data, is/are meant to induce the output variables supplied by the neural network to exhibit an invariant or equivariant behavior; at least one equation is set up which links a condition that the desired invariance or equivariance be given with the architecture of the neural network; by solving the at least one equation a feature is obtained that characterizes the desired architecture and/or a distribution of weights of the neural network in at least one location of this architecture; a neural network is configured in such a way that its architecture and/or its distribution of weights in at least one location of this architecture has/have all of the features ascertained in this way.

This application discloses a calibration method for navigation of an unmanned aerial vehicle (UAV), a non-transitory computer-readable storage medium and a UAV implementing the same. The calibration method includes: collecting, during a flight of the UAV, reference data during two measurements of a reference vector performed by a vector sensor; acquiring a zero-point offset M.sub.0 of the vector sensor according to the reference data; acquiring original data R.sub.k of any vector measured by the vector sensor; acquiring valid data V.sub.k according to the zero-point offset M.sub.0 and the original data R.sub.k; and control headings and postures of the UAV according to the valid data V.sub.k. With the calibration method in this application, the valid data V.sub.k is defined as a vector data acquired after a zero-point error of the original data R.sub.k is eliminated, which is more closely approximated to an actual value of a to-be-measured vector.

This application discloses a calibration method for navigation of an UAV including a vector sensor. The calibration method includes: collecting, during a flight of the UAV, a current correction value and current data during a current measurement performed by the vector sensor; acquiring previous data during a previous measurement performed by the vector sensor; acquiring an adjustment quantity according to the current data and the previous data; acquiring a next correction value according to the current correction value and the adjustment quantity; and acquiring next original data during a next measurement performed by the vector sensor, acquiring next valid data according to the next original data and the next correction value , and controlling headings and postures of the UAV according to the next valid data. With the calibration method of this application, the next valid data V.sub.k+1 more closely approximated to a true value can be obtained.

A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.

Methods and systems for automatically deploying an autonomous drone from a vehicle in response to a triggering event or accident so that data associated with the triggering event or accident may be automatically obtained are described. In one embodiment, a method for deploying an autonomous drone in response to a triggering event is described. The method includes providing an autonomous drone in a vehicle. The method also includes detecting a triggering event associated with the vehicle. Upon detection of the triggering event, the method includes automatically deploying the autonomous drone from the vehicle. The method further includes implementing, by the autonomous drone, a plurality of automatic actions, including recording data associated with the vehicle in which the autonomous drone is provided.

Techniques and systems are described for generating and using a sound localization model. A described technique includes obtaining for a building a sound sensor map indicating locations of first and second sound sensor devices in respective first and second rooms of the building; causing an autonomous device to navigate to the first room and to emit, during a time window, sound patterns at one or more frequencies within the first room; receiving sound data including first and second sound data respectively from the first and second sound sensor devices that are observed during the time window; and generating and storing a sound localization model based on the sound sensor map, autonomous device location information, and the received sound data, the model being configured to compensate for how sounds travels among rooms in at least a portion of the building such that an origin room of a sound source is identifiable.

A parcel delivery system is provided for use in complex having a plurality of tenant units each having a tenant address, wherein the complex further comprising an intake facility, a delivery facility, and parcel storage boxes, and wherein the intake facility comprises a scanning station and a first automated ground delivery vehicle in communication with the scanning station and wherein the delivery facility comprises a second automated ground delivery vehicle configured to travel between the delivery station and tenant units. In accordance with embodiments, a computer system interfaces with the parcel delivery system to notify tenants when they have a parcel and to further notify tenants when delivery of the parcel has been completed.

Provided is a user equipment for controlling a drone. The user equipment analyzes an original video to control the drone to photograph a reproduction video giving a feeling identical to or similar to the original video. An electronic device may be connected to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G service, and the like.

An information processing device determines, based on location information of a plurality of delivery destinations for package delivery: one or a plurality of first delivery destinations for package delivery by a vehicle; and one or a plurality of second delivery destinations for package delivery by a drone mounted on the vehicle. In addition, the information processing device determines: a travel route including a route for the vehicle to perform package delivery to the one or plurality of first delivery destinations; a first point on the travel route for the drone to start flying from the vehicle to the one or plurality of second delivery destinations; and a second point on the travel route for the drone to return to the vehicle.