A device includes a memory and processing circuitry coupled to the memory. The processing circuitry, in operation, generates an indication of a predicted difference in a direction of arrival (DoA) of a signal using a trained autoregressive model. A predicted indication of a DoA of the signal is generated based on a previous indication of the DoA of the signal and the indication of the predicted difference in the DoA of the signal. The processing circuitry actuates or controls an antenna array based on predicted indications of the DoA of the signal.

A method for jointly estimating gain-phase error and direction of arrival (DOA) based on an unmanned aerial vehicle (UAV) array includes: equipping each UAV with an antenna, and forming a receive array through a swarm of multiple UAVs to receive source signals; when an observation baseline of the swarm remains unchanged, changing array manifold through movement of the UAVs, and re-sensing the source signals; for each sensed source signals, calculating a covariance matrix, and obtaining a corresponding noise subspace through eigenvalue decomposition; and constructing a quadratic optimization problem based on the noise subspace and array steering vector, constructing a cost function, and implementing joint estimation of the gain-phase error and the DOA through spectrum peak search. The method can jointly estimate the DOA and gain-phase error and calibrate the gain-phase error, thereby improving accuracy of passive positioning.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention includes a method for calibrating at low frequency and in-flight a goniometry apparatus including an antenna array, on board an air carrier. The method includes for an angular position of reception, calibrating the airborne goniometry apparatus at a given frequency, comprising transmitting, by means of a calibration transmitter, at the given frequency and in the direction of the goniometry apparatus, at least two calibration signals, with polarizations orthogonal to each other. The method also includes measuring a response of the antenna array for each of the signals. The invention also includes a system implementing such a method.

Embodiments of the disclosure are drawn to apparatuses, systems, and methods for radio direction finding with an iterative ambiguity resolution algorithm. An antenna array may receive an emitted signal. Two or more phase shifts in the received emitted signal may be determined between two or more pairs of antennas of the antenna array. A set of possible expected phase shifts may be generated from at least two of the measured phase shift. To determine the proper one of the set of expected phase shifts, a set of initial guesses for parameters of a fitting equation may be generated and then each may be optimized to determine optimized fitting parameters. From these optimized fitting parameters a direction of arrival of the emitted signal may be determined.

Embodiments of the disclosure are drawn to apparatuses, systems, and methods for radio direction finding with an iterative ambiguity resolution algorithm. An antenna array may receive an emitted signal. Two or more phase shifts in the received emitted signal may be determined between two or more pairs of antennas of the antenna array. A set of possible expected phase shifts may be generated from at least two of the measured phase shift. To determine the proper one of the set of expected phase shifts, a set of initial guesses for parameters of a fitting equation may be generated and then each may be optimized to determine optimized fitting parameters. From these optimized fitting parameters a direction of arrival of the emitted signal may be determined.

Two antennas receive three kinds of radio waves with different frequencies. A computation unit determines the arrival direction of the three kinds of radio waves arriving at the two antennas after propagating along two mutually different paths from a single transmit point in accordance with receive signals of the three kinds of radio waves with different frequencies received individually by the two antennas.

An apparatus for generating a mosaic for a wireless communication system. The apparatus includes memory and a server. The server is programmed to receive first information from an associated access point that is indicative of a first receiver time stamp and a second receiver time stamp. The server is further programmed to determine a first difference between the first receiver time stamp and the second receiver time stamp to generate a first difference value and to receive second information that is indicative of a third receiver time stamp and a fourth receiver time stamp. The server is further programmed to determine a second difference and to generate a second difference value. The server is further programmed to determine that packets as transmitted by the mobile device are the same based on the first difference value and the second difference value being within a predetermined receiver time error range.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.