Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

Embodiments provide an unmanned aerial vehicle comprising a receiver and a position determiner. The receiver is configured to receive two periodic wideband signals transmitted from two spaced apart base stations of a navigation system for unmanned aerial vehicles, wherein the two periodic wideband signals are time-synchronized. The position determiner is configured to determine a position of the unmanned aerial vehicle relative to the two base stations based on a difference between reception times of the two periodic wideband signals and based on reception intensities of the two periodic wideband signals.

Embodiments include methods, systems and computer readable storage medium for determining an attack on a vehicle network and an estimated source location of an attacker. The method includes receiving, by a processor, a plurality of messages. The method further includes analyzing, by the processor, each of the plurality of messages to determine that each of the plurality of messages is suspicious. The method further includes determining, by the processor, that an attack is occurring in response to a determination that multiple messages of the plurality of messages are suspicious. The method further includes localizing, by the processor, a source location for the attack using an angle of arrival associated with each of the plurality of suspicious messages to determine a source intersection. The method further includes notifying, by the processor, one or more vehicles of the attack.

A method of combined direction finding (DF) and fine timing measurement (FTM) positioning in a wireless location area network (WLAN) is proposed. A multiple antenna IEEE 802.11 transmitting device (AP) can transmit signal preamble containing multiple Long Training Field (LTF) symbols in a radio frame from multiple antennas, which allows a receiving device (STA) to resolve multiple DF sounding signals transmitted from the multiple antennas and thereby estimating angle of departure (AoD). On the other hand, the AP can estimate angle of arrival (AoA) from radio signals transmitted from the STA. When the radial resolution error of AoD or AoA positioning increases, DF positioning and fine-timing measurement (FTM) ranging can be jointly applied to reduce the radial resolution error and extends the AoD/AoA service area with positing accuracy.

A method of combined direction finding (DF) and fine timing measurement (FTM) positioning in a wireless location area network (WLAN) is proposed. A multiple antenna IEEE 802.11 transmitting device (AP) can transmit signal preamble containing multiple Long Training Field (LTF) symbols in a radio frame from multiple antennas, which allows a receiving device (STA) to resolve multiple DF sounding signals transmitted from the multiple antennas and thereby estimating angle of departure (AoD). On the other hand, the AP can estimate angle of arrival (AoA) from radio signals transmitted from the STA. When the radial resolution error of AoD or AoA positioning increases, DF positioning and fine-timing measurement (FTM) ranging can be jointly applied to reduce the radial resolution error and extends the AoD/AoA service area with positing accuracy.

A method for autonomous vehicle localization. The method may include receiving, by an autonomous vehicle, millimeter-wave signals from at least two 5G transmission points. Bearing measurements may be calculated relative to each of the 5G transmission points based on the signals. A vehicle velocity may be determined by observing characteristics of the signals. Sensory data, including the bearing measurements and the vehicle velocity, may then be fused to localize the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.

An unmanned aerial system (UAS) detection device includes a sensor having programmed instructions to cause the sensor to scan energy in an electromagnetic spectrum; process the energy in the electromagnetic spectrum into bursts; determine whether the bursts are valid UAS bursts based on burst criteria; and correlate the bursts into a single signal.

An improved remote keyless entry (RKE) system for a vehicle to provide enhanced capabilities to a user. The improved RKE system may utilize directional antennas in the vehicle and in the FOB. Directional antennas may utilize devices equipped with orientation-independent antennas to determine the location. The system can exist in many different customizable configurations, sometimes utilizing orientation-independent antennas embedded in the vehicle or the FOB. The devices, systems and methods described herein may be used for an RKE in personal or commercial vehicles.

Methods and devices useful in performing precise indoor localization and tracking are provided. By way of example, a method includes locating and tracking, via a first wireless electronic device, a plurality of other wireless electronic devices within an indoor environment. The method also includes performing front-back detection, performing stationary node detection, performing angle of arrival (AoA) error correction, and performing field of view (FOV) filtering. Performing indoor localization and tracking of the plurality of other wireless electronic devices includes providing an indication of a physical location of the plurality of other wireless electronic devices within the indoor environment.

Methods and devices useful in performing precise indoor localization and tracking are provided. By way of example, a method includes locating and tracking, via a first wireless electronic device, a plurality of other wireless electronic devices within an indoor environment. The method also includes performing front-back detection, performing stationary node detection, performing angle of arrival (AoA) error correction, and performing field of view (FOV) filtering. Performing indoor localization and tracking of the plurality of other wireless electronic devices includes providing an indication of a physical location of the plurality of other wireless electronic devices within the indoor environment.