Multi-rotors use multiple electric motors to drive propellers that allow the multi-rotor to fly, turn, bank, etc. The multiple motors and the associated control device produce EM signals that can be used to detect multi-rotors and distinguish them from other devices with electric motors. To drive a brushless motor, the ESC takes DC from the battery and turns it into three phase AC (sinusoidal or trapezoidal wave), and then measures back EMF pulses (sensorless). This allows one to ensure that the three phase AC is being generated at the proper frequency to turn the motor (timing). For each propeller in the multi-rotor there are usually four different correlated signals at multiple frequencies that are used for detection and false alarm rejection.

A driving support method for a vehicle includes: acquiring a sound of a sound source placed outside the vehicle; and displaying, on a display portion, driving support information corresponding to an utterance content of the sound, the utterance content of the sound being recognized by a sound recognition process, in a display mode suggesting a relative position of the sound source from the vehicle, the relative position being specified based on the sound.

Method, node, computer program, and computer program product in a wireless communication network, comprising a network communication unit with a data link sub-layer, said node configured to calculate the Time of Arrival and/or Time of Flight based on a counted time from transmission of a response request message in the medium access control layer of said node to the corresponding arrival of a response to said response request message in the data link sub-layer of said node.

A method to identify a problematic 2D position of a mobile device can include: determining a reported 2D position of the mobile device; determining a piece of information about the mobile device; and comparing the reported 2D position and the piece of information about the mobile device. Upon determining that the reported 2D position and the piece of information about the mobile device are consistent with each other, the reported 2D position of the mobile device is used as an estimate of the actual 2D position of the mobile device, or upon determining that the reported 2D position and the piece of information about the mobile device are not consistent with each other, the reported 2D position is determined to be problematic, and the reported 2D position of the mobile device is removed from a list of reported 2D positions of the mobile device.

This disclosure relates generally to telecom inventory management, and more particularly to telecom inventory management via object recognition and localization using street-view images. In one embodiment, the method includes obtaining street-view images of a geographical area having telecom assets. The telecom assets are associated with corresponding GPS location coordinates. An object recognition model is applied to the street-view images to detect the telecom assets therein. Detecting the telecom assets includes associating the telecom assets with corresponding asset labels. A real-world location of the telecom assets is estimated in the geographical area by applying triangulation method on a set of multi-view images selected from the street-view images. The set of multi-view images are captured from a plurality of consecutive locations in vicinity of the telecom asset in the geographical area. The GPS location coordinates of the telecom assets are validated based at least on the estimated real-world location.

The technique and the system may be used for indoor positioning, where signals of navigation satellites are not available. In accordance with the technique patterns identifying location of the mobile terminal in a specific position may be detected, on the basis of data acquired from at least one inertial and non-inertial sensors in the process of movement of at least one mobile terminal; the path of movement of the above mobile terminal may be detected and saved in the local coordinate system associated with the above position, as well as data acquired from non-inertial sensors; statistically averaged parameters of conversion of local coordinate system of the mobile terminal may be generated in the positions detected in the process of terminal movement; at least one map of distribution of output values of non-inertial sensors may be prepared on the basis of data acquired at the previous step; the position of the above mobile terminal may be detected on the basis of data acquired at the previous step. The system may include a set of sensors of mobile terminal, a computer, a probability computation module, a module for selection of patterns, a data storage package and a coordinate converter.

A positioning apparatus includes: a reference device configured to provide a measured current motion angle of a vehicle; an inertial sensor configured to provide a current input angular rate of the vehicle and associated with at least one inertial sensor behavior parameter dependent on inertial sensor temperature; a temperature sensor configured to provide an input temperature variation of the inertial sensor on a time interval; and a digital estimator configured to recursively computing an estimated current motion angle of the vehicle and at least one previously estimated inertial sensor behavior parameter as function of: the measured current motion angle, a previously estimated motion angle, the current input angular rate, and the input temperature variation.

A method and system for using GPS signals and a magnetic field to track an underground magnetic field source. A tracker having an antenna for detecting the magnetic field and a GPS receiver is coupled to a processor. The magnetic field is used by the antenna to direct the tracker to a field null point. Once multiple measurements of the field are taken, the changes in signal strength as the absolute position of the tracker is changed, are used to determine whether the closest field null point is in front of or behind the underground beacon. The position and depth of the beacon can then be estimated.

An identification system includes an identification device and a communication device. The identification device is attached to an article and including a sensor. The identification device is configured to operate in a plurality of modes and configured to switch between modes of the plurality of modes based on one or more conditions of a surrounding environment sensed by the sensor. The identification device is configured to send data to a server. The communication device of a user of the identification system is configured to receive data including location data of the identification device from the server using short message service protocols or messages through a signaling channel.

A method and system for combining data obtained by sensors, having particular application in the field of navigation systems, are disclosed. The techniques provide significant improvement over state-of-the-art Markovian methods that use statistical noise filters such as Kalman filters to filter data by comparing instantaneous data with the corresponding instantaneous estimates from a model. In contrast, the techniques disclosed herein use multiple time periods of various lengths to process multiple sensor data streams, in order to combine sensor measurements with motion models at a given time epoch with greater confidence and accuracy than is possible with traditional single epoch methods. The techniques provide particular benefit when the first and/or second sensors are low-cost sensors (for example as seen in smart phones) which are typically of low quality and have large inherent biases.