A split architecture is disclosed for determining the location of a wireless device in a heterogeneous wireless communications environment. A detector within the device or another component of the environment receives signals including parameters for a localization signal of the device. The parameters describe known in advance signals within the signals. Additional metadata including each frame start of the signals and assistance data and auxiliary information are also received. The known in advance signals are detected based on the parameters of the localization signal. Samples extracted from the known in advance signals are then processed and compressed and sent with other collect data to a locate server remote from the detector. The location server uses that information as well as similar information about the environment to calculate the location of the device, as well as perform tracking and navigation of the device, and report such results to the environment.

A device for locating a remote RFID transmitter in an environment providing separate paths of propagation of a wirelessly transmitted ultra-wide band signal. The receiver of the device has a module for estimating the impulse response of the plurality of paths of the first channel defined by a first antenna and a module for estimating the impulse response of the plurality of paths of the second channel defined by the second antenna, a module for calculating a plurality of argument differences between each component of the impulse response and each component of the impulse response for the plurality of paths respectively. The device further comprises a module for converting the plurality of argument differences into a plurality of arrival angles of the plurality of paths, a module for determining a location of the transmitter from the plurality of arrival angles.

A device for locating a remote RFID transmitter in an environment providing separate paths of propagation of a wirelessly transmitted ultra-wide band signal. The receiver of the device has a module for estimating the impulse response of the plurality of paths of the first channel defined by a first antenna and a module for estimating the impulse response of the plurality of paths of the second channel defined by the second antenna, a module for calculating a plurality of argument differences between each component of the impulse response and each component of the impulse response for the plurality of paths respectively. The device further comprises a module for converting the plurality of argument differences into a plurality of arrival angles of the plurality of paths, a module for determining a location of the transmitter from the plurality of arrival angles.

The present disclosure relates to a method (100) for determining an angle of arrival, AoA, of received radio frequency, RF, measurement signals. The method (100) comprises obtaining (101) measurement data based on the received RF measurement signals from an antenna array, wherein the RF measurement signals are representative of multiple frequency channels. The method (100) further comprises determining (102) power spectra, comprising determining at least one power spectrum for each of the multiple frequency channels by using the measurement data. The method (100) further comprises providing (105) a machine learning algorithm, which is pre-trained to determine an AoA based on power spectra of multiple frequency channels. The method (100) further comprises determining (106) the AoA of the received RF measurement signals by using the machine learning algorithm and the determined power spectra.

The present disclosure relates to a method (100) for determining an angle of arrival, AoA, of received radio frequency, RF, measurement signals. The method (100) comprises obtaining (101) measurement data based on the received RF measurement signals from an antenna array, wherein the RF measurement signals are representative of multiple frequency channels. The method (100) further comprises determining (102) power spectra, comprising determining at least one power spectrum for each of the multiple frequency channels by using the measurement data. The method (100) further comprises providing (105) a machine learning algorithm, which is pre-trained to determine an AoA based on power spectra of multiple frequency channels. The method (100) further comprises determining (106) the AoA of the received RF measurement signals by using the machine learning algorithm and the determined power spectra.

Disclosed is a method for direction-of-arrival estimation based on sparse reconstruction in the presence of gain-phase error, which comprises the following steps: firstly, estimating a noise power and an gain error from an array received signal by adopting a characteristic decomposition method; then, based on a compensated covariance matrix, transforming a direction-of-arrival estimation problem into a non-convex optimization problem in a sparse frame by a method of sparse reconstruction; finally, estimating a grid angle and a deviation angle by using an alternate optimization method. This estimation method can effectively eliminate the influence of a phase error in direction-of-arrival estimation, and has better adaptability, which improves the resolution and estimation accuracy of the algorithm.

Disclosed is a method for direction-of-arrival estimation based on sparse reconstruction in the presence of gain-phase error, which comprises the following steps: firstly, estimating a noise power and an gain error from an array received signal by adopting a characteristic decomposition method; then, based on a compensated covariance matrix, transforming a direction-of-arrival estimation problem into a non-convex optimization problem in a sparse frame by a method of sparse reconstruction; finally, estimating a grid angle and a deviation angle by using an alternate optimization method. This estimation method can effectively eliminate the influence of a phase error in direction-of-arrival estimation, and has better adaptability, which improves the resolution and estimation accuracy of the algorithm.

Systems and methods for determining a location of one or more user equipment (UE) in a wireless system can comprise receiving reference signals via a location management unit having two or more co-located channels, wherein the two or more co-located channels are tightly synchronized with each other and utilizing the received reference signals to calculate a location of at least one UE among the one or more UE. Embodiments include multichannel synchronization with a standard deviation of less than or equal 10 ns. Embodiments can include two LMUs, with each LMU having internal synchronization, or one LMU with tightly synchronized signals.

Disclosed is a position estimation method for estimating a position of an interference signal source and a position estimation system for performing the method. The position estimation method may implement an indoor delay-space analysis structure by transmitting and receiving a known signal and a virtual array structure-based direction finding algorithm in an indoor environment in which a plurality of reflected waves is present and may increase an estimation probability for the position of the interference signal source.

A spatial approach is provided to mitigate multipath error for an indoor pedestrian localization system using broadband communication signals, such as cellular long-term evolution (LTE) carrier phase measurements. Motion of a receiver may be used to synthesize an antenna array from time-separated elements. Received data may then be combined for synthetic aperture navigation that allows for suppressing multipath error based on determination of direction-of-arrival (DOA) of the incoming communication (e.g., LTE) signals. In one embodiment, navigation observables may be determined based on determined direction of arrival.