A method and device for determining a time of arrival (TOA), a terminal device, and a non-transitory computer-readable storage medium are disclosed. The method may include: determining a detection start time on a correlation waveform based on a leading edge detection threshold; determining a noise threshold on the correlation waveform, and determining a quasi-TOA according to the detection start time; and determining the TOA according to data information in a target area of the correlation waveform and the noise threshold, where the target area is determined based on the quasi-TOA and a detection length.

Embodiments relate to a sensor node communication system that determines the presence of an object. The system includes man-portable nodes that communicate via a self-organizing LAN. Each node includes a control circuit that has multiple cores, multithreading, and/or parallel processing. The control circuit establishes the LAN, captures an image, determines a presence of a predetermined object in the image using a machine learning algorithm, generates a first notification when presence of the predetermined object is determined, and generates a second notification when the presence is not determined. The control circuit detects an electromagnetic environment, determines unused frequency bands, and adapts a radio working parameter to broadcast in the unused frequency band. Determining the unused frequency bands includes the use of a spectrum sensing method. The control circuit detects an electromagnetic environment, determines unused frequency bands, and adapts a radio working parameter to broadcast in unused frequency bands.

Embodiments relate to a sensor node communication system that determines the presence of an object. The system includes man-portable nodes that communicate via a self-organizing LAN. Each node includes a control circuit that has multiple cores, multithreading, and/or parallel processing. The control circuit establishes the LAN, captures an image, determines a presence of a predetermined object in the image using a machine learning algorithm, generates a first notification when presence of the predetermined object is determined, and generates a second notification when the presence is not determined. The control circuit detects an electromagnetic environment, determines unused frequency bands, and adapts a radio working parameter to broadcast in the unused frequency band. Determining the unused frequency bands includes the use of a spectrum sensing method. The control circuit detects an electromagnetic environment, determines unused frequency bands, and adapts a radio working parameter to broadcast in unused frequency bands.

Disclosed are a method and an apparatus for determining time of arrival, a signal receiving device, and a non-transitory computer readable storage medium. The method for determining time of arrival includes: generating time of arrival correction information according to front edge information of theoretical autocorrelation waveforms of positioning signals having different fading values; calculating a front edge slope corresponding to a front edge reference point of a correlation waveform of an actually received signal; and determining target correction information from the time of arrival correction information according to the front edge slope, and determining the time of arrival of the actually received signal based on the target correction information.

Disclosed are a method and an apparatus for determining time of arrival, a signal receiving device, and a non-transitory computer readable storage medium. The method for determining time of arrival includes: generating time of arrival correction information according to front edge information of theoretical autocorrelation waveforms of positioning signals having different fading values; calculating a front edge slope corresponding to a front edge reference point of a correlation waveform of an actually received signal; and determining target correction information from the time of arrival correction information according to the front edge slope, and determining the time of arrival of the actually received signal based on the target correction information.

Disclosed are techniques for positioning. A receiver measures a time of arrival (ToA) of a positioning reference signal (PRS) transmission of a first PRS sequence transmitted by a first transmitter, measures a ToA of a PRS transmission of a second PRS sequence transmitted by a second transmitter, and determines an observed time difference of arrival (OTDOA) as a difference between the ToA of the PRS transmission of the first PRS sequence and the ToA of the PRS transmission of the second PRS sequence, wherein the OTDOA is less than half a maximum differential delay expected between the PRS transmission of the first PRS sequence and the PRS transmission of the second PRS sequence, and wherein a repetition duration of the first PRS sequence and the second PRS sequence is greater than 10 milliseconds (ms) and at least twice the maximum differential delay.

Embodiments of a method and an apparatus for wireless localization are disclosed. In an embodiment, a method for wireless localization involves obtaining, by an Ultra-Wideband (UWB) radio of a localization device, UWB timing data from UWB anchors, transmitting, via a non-UWB transceiver of the localization device, the UWB timing data to a localization engine, and determining, by the localization engine, a location of the localization device using the UWB timing data.

Embodiments of a method and an apparatus for wireless localization are disclosed. In an embodiment, a method for wireless localization involves obtaining, by an Ultra-Wideband (UWB) radio of a localization device, UWB timing data from UWB anchors, transmitting, via a non-UWB transceiver of the localization device, the UWB timing data to a localization engine, and determining, by the localization engine, a location of the localization device using the UWB timing data.

A system and method for classifying a type of interaction between a human user and a mobile communication device within a defined volume, based on multiple sensors. The method may include: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; repeating the obtaining, to yield a time series of sensor readings; and using a computer processor to classify the type of interaction into one of many predefined types of interactions, based on the position and the time series of sensor readings.

Methods and devices for determining a load vector on an object are disclosed herein. An example method includes collecting location observations related to the object. The example method further includes filtering the location observations to determine an estimated model path. The example method further includes outputting a set of data from the estimated model path, wherein the set of data includes a model location, a model velocity, a model acceleration, and a model jerk. The example method further includes calculating a load vector from the set of data, scaling the load vector via a scaling index, and transmitting the scaled load vector to a remote device.