In an array antenna having a plurality of subarrays, a direction finding system and technique includes receiving signals at an array antenna and capturing data with a plurality of groups of subarrays. Each group of subarrays may capture data during a selected one of a plurality of different dwell times. The method further includes generating a plurality of dwell spatial sample covariance matrices (SCMs) using data corresponding to one or more of the plurality of groups of subarrays and combining the plurality of dwell spatial SCMs in complex form to generate an aggregate covariance matrix (ACM). The ACM may then be used in subsequent processing with MINDIST technique to estimate a direction of a received signal based on the combined data.

In an array antenna having a plurality of subarrays, a direction finding system and technique includes receiving signals at an array antenna and capturing data with a plurality of groups of subarrays. Each group of subarrays may capture data during a selected one of a plurality of different dwell times. The method further includes generating a plurality of dwell spatial sample covariance matrices (SCMs) using data corresponding to one or more of the plurality of groups of subarrays and combining the plurality of dwell spatial SCMs in complex form to generate an aggregate covariance matrix (ACM). The ACM may then be used in subsequent processing with MINDIST technique to estimate a direction of a received signal based on the combined data.

An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.

An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.

Systems, methods, and apparatus for processing signals using a sensor arrays are disclosed. In one aspect, an apparatus comprising a sensor array and a computing device is provided. The computing device may comprise one or more processors configured to generate a data matrix based on one or more signals received at each of the plurality of sensor and to determine a covariance matrix based on the data matrix. The one or more processors may also be configured to decompose the covariance matrix into a matrix of eigenvalues and a matrix of eigenvectors and to determine a projected data matrix based on applying the data matrix to the eigenvector matrix. Further, the one or more processors may be configured to determine a denoised projected data matrix based on denoising the projected data matrix and to determine a denoised data matrix based on the denoised projected data matrix.

In an array antenna having a plurality of subarrays, a direction finding system and technique includes receiving signals at an array antenna and capturing data with a plurality of groups of subarrays. Each group of subarrays may capture data during a selected one of a plurality of different dwell times. The method further includes generating a plurality of dwell spatial sample covariance matrices (SCMs) using data corresponding to one or more of the plurality of groups of subarrays and combining the plurality of dwell spatial SCMs in complex form to generate an aggregate covariance matrix (ACM). The ACM may then be used in subsequent processing with MINDIST technique to estimate a direction of a received signal based on the combined data.

In an array antenna having a plurality of subarrays, a direction finding system and technique includes receiving signals at an array antenna and capturing data with a plurality of groups of subarrays. Each group of subarrays may capture data during a selected one of a plurality of different dwell times. The method further includes generating a plurality of dwell spatial sample covariance matrices (SCMs) using data corresponding to one or more of the plurality of groups of subarrays and combining the plurality of dwell spatial SCMs in complex form to generate an aggregate covariance matrix (ACM). The ACM may then be used in subsequent processing with MINDIST technique to estimate a direction of a received signal based on the combined data.

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.

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.

Facilitation of determination of detailed location of a source signal is provided. In one embodiment, a device comprises a memory that stores computer executable components; and a processor that executes computer executable components stored in the memory. The computer executable components can comprise: a low-resolution computation logic component that implements a coarse algorithm and determines an approximate direction of arrival (DOA) of a source signal of an input signal, wherein the coarse algorithm uses both a coarse spatial grid and input data received from the input signal to determine the approximate DOA; and an error estimation logic component that estimates an estimation error of the coarse algorithm, and wherein the error estimation logic component uses the estimation error and the approximate DOA to determine a spatial interval range.