An exemplary radio fingerprint-based indoor localization method and system is disclosed that is resistant to spoofing or jamming attacks (e.g., at nearby radios, e.g., access points), among other types of interference. The exemplary method and system may be applied in the configuring of a secured convolutional neural network (S-CNNLOC) or secured deep neural network configured for attack-resistant fingerprint-based indoor localization.

Systems, methods, and computer readable media that schedules requests for location data of a mobile device, where the methods include selecting a first positioning system based on a power requirement, a latency requirement, and an accuracy requirement, and determining whether a first condition is satisfied for querying the first positioning system. The method further comprises in response to a determination that the first condition is satisfied, querying the first positioning system for first position data. The method further comprises in response to a determination that the first condition is not satisfied, selecting a second positioning system based on the power requirement, the latency requirement, and the accuracy requirement, determining whether a second condition is satisfied for querying the second positioning system, and in response to a determination that the second condition is satisfied, querying the second positioning system for second position data.

Systems, methods, and computer readable media that schedules requests for location data of a mobile device, where the methods include selecting a first positioning system based on a power requirement, a latency requirement, and an accuracy requirement, and determining whether a first condition is satisfied for querying the first positioning system. The method further comprises in response to a determination that the first condition is satisfied, querying the first positioning system for first position data. The method further comprises in response to a determination that the first condition is not satisfied, selecting a second positioning system based on the power requirement, the latency requirement, and the accuracy requirement, determining whether a second condition is satisfied for querying the second positioning system, and in response to a determination that the second condition is satisfied, querying the second positioning system for second position data.

To automatically determine geographic positions of signal sources in areas with limited satellite coverage, a system receives signal data collected by a receiver moving along a path through a geographic area with limited satellite coverage, the signal data being indicative of changes, over a period of time, in strength of respective signals detected by the moving receiver and emitted by multiple signal sources statically disposed along the path. The system determines a time it takes for a length of a vehicle to pass by the signal source at the determined speed. The system then calculates static positions of the signal sources using the signal data and the determined time, including associating the location of each signal source with a time when the signal source was directly over the roof of the vehicle in which the moving receiver is travelling.

A system includes a plurality of wireless transmitters and a processor configured to: receive, from a mobile device, signal strengths of signals from the wireless transmitters as detected by the mobile device. The processor is also configured to determine a location of the mobile device in a vehicle, based on a distance from the mobile device to each of the wireless transmitters, as indicated by the received signal strengths and store the location of the mobile device as an occupant location.

Systems and methods for WiFi mapping an industrial facility are disclosed. The system comprises a self-driving vehicle having a WiFi transceiver. The self-driving vehicle communicates with a fleet-management using the WiFi transceiver, via a WiFi access point. The self-driving vehicle receives a mission from the fleet-management system, and moves to a destination location based on the mission, using autonomous navigation. While executing the mission, the self-driving vehicle simultaneously measures the received signal strength indication of the WiFi access point and other WiFi access points in the facility, and stores the received signal strength indication in association with the location at which the received signal strength indication was measured.

A system and methods for estimating the location of a mobile device are disclosed. In accordance with one embodiment, a mobile device receives, at a first time t.sub.1, a wireless electromagnetic signal, where the wireless electromagnetic signal comprises a first identifier that identifies a first beacon that transmitted the wireless electromagnetic signal. The mobile device receives, at a second time t.sub.2, t.sub.2>t.sub.1, an ultrasound signal lacking an identification of the source of the ultrasound signal. A time difference of arrival (TODA) t.sub.2−t.sub.1 is compared to a maximum TDOA. A location of the mobile device is estimated based on the TDOA only when the TDOA is less than or equal to the maximum TDOA.

A computer apparatus is provided for determining one or more peripheral device positions. A primary display of the computer apparatus may be provided with two or more signal receivers disposed at different locations in relation to the primary display and may be configured to receive signals from a signal transmitter at a peripheral device. The computer apparatus may include a processor and a memory configured to provide computer program instructions to the processor to execute a method of: determining a distance from the signal transmitter to each of the two or more signal receivers based on a strength of the received signal; using positioning determination to determine a direction and/or distance to a position of the signal transmitter from the primary display; and configuring the peripheral device based on the determined direction and/or distance.

A system and method for classifying a type of calibration or maintenance event of a phone location unit (PLU) within a defined volume, based on at least one sensor, the method comprising: determining a position of the at least one mobile communication device relative to a frame of reference of the defined volume; obtaining at least one sensor measurement related to the at least one mobile communication device, from a sensor located on at least one of: the at least one mobile communication device, within the defined volume, or outside of the defined volume; and using a computer processor to classify the type of calibration or maintenance event of the PLU, into one of a plurality of predefined types of calibration or maintenance event, based on the position and the at least one sensor measurement.

A system and method for classifying a mode of operation of a mobile communication device within a defined volume, based on multiple sensors are provided herein. The method may include the following steps: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on any of: 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 at least one mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; and using a computer processor to classify the mobile communication device into one of many predefined modes of operation of the mobile communication device, based on the position and the sensor readings.