A method for estimating angle of arrival (AoA) of signals in a wireless communication system applied in an apparatus can estimate multiple AOAs of multiple paths on one channel tap using a transmitting scheme of beamformed multiple transmissions at the transmitting side. In the transmitting scheme, the equivalent channels for paths with multiple AoAs can be viewed as random, and a subspace-based algorithm is applied for AoA estimation.

This disclosure relates generally to field agnostic source localization. Conventional state-of-the-art methods perform source localization for near-field scenario by estimating carrier frequency and direction of arrival (DOA) at or above Nyquist sampling rate. Embodiments of the present disclosure provide a method for source localization at sub Nyquist sampling rate. The method estimates parameters such as range, carrier frequency and DOA of source signals from data sources in a mixed field scenario. i.e., the data sources may reside in far-field as well as near-field. The method considers a delay channel to a sensor receiver architecture for estimating the parameters. The disclosed method can be used in applications like cognitive radio to determine the carrier frequency, DOA and range of various source signals from data sources in mixed field.

A system includes a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive, from a radar sensor of a vehicle, radar data indicative of a stationary object proximate to the radar sensor; receive, from a non-radar sensor of the vehicle, vehicle state data indicative of a vehicle state, the vehicle state data indicative of at least a longitudinal velocity and a yaw rate of the vehicle; determine an orientation estimate and an offset estimate of the radar sensor based on the radar data and the vehicle state data; and determine whether to actuate a vehicle system based on at least one of the orientation estimate or the offset estimate.

A system includes a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive, from a radar sensor of a vehicle, radar data indicative of a stationary object proximate to the radar sensor; receive, from a non-radar sensor of the vehicle, vehicle state data indicative of a vehicle state, the vehicle state data indicative of at least a longitudinal velocity and a yaw rate of the vehicle; determine an orientation estimate and an offset estimate of the radar sensor based on the radar data and the vehicle state data; and determine whether to actuate a vehicle system based on at least one of the orientation estimate or the offset estimate.

A sound source localization method, applied in a sound system comprising a microphone array, is provided. The sound source localization method comprises the microphone array receiving a received signal; building a cost function according to the received signal; forming a plurality of particles, wherein the plurality of particles is a plurality of virtual particles; computing a plurality of update positions of the plurality of particles according to a plurality of current positions and the cost function, and obtaining at least a sound source location according to the plurality of update positions.

An autonomous system with no Customer Network Investment is described, wherein the system is configurable to operate on in a band in addition to the LTE band. Such system allows the definition of hybrid operations to accommodate the positioning reference signals (PRS) of LTE and already existing reference signals. The system can operate with PRS, with other reference signals such as cell-specific reference signals (CRS), or with both signal types. As such, the system provides the advantage of allowing network operator(s) to dynamically choose between modes of operation depending on circumstances, such as network throughput and compatibility. The system further enables information collected at a network device to be processed at a locate server without involving any processing at the network device.

An autonomous system with no Customer Network Investment is described, wherein the system is configurable to operate on in a band in addition to the LTE band. Such system allows the definition of hybrid operations to accommodate the positioning reference signals (PRS) of LTE and already existing reference signals. The system can operate with PRS, with other reference signals such as cell-specific reference signals (CRS), or with both signal types. As such, the system provides the advantage of allowing network operator(s) to dynamically choose between modes of operation depending on circumstances, such as network throughput and compatibility. The system further enables information collected at a network device to be processed at a locate server without involving any processing at the network device.

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.

For example, a radar processor may be configured to determine a first 1D AoA spectrum corresponding to a first dimension of an Azimuth-Elevation domain based on radar Rx data, to determine a second 1D AoA spectrum corresponding to a second dimension of the Azimuth-Elevation domain based on the radar Rx data, to detect one or more first object hypotheses in the first dimension based on the first 1D AoA spectrum, to detect one or more second object hypotheses in the second dimension based on the second 1D AoA spectrum, to determine a plurality of 2D object hypotheses corresponding to the Azimuth-Elevation domain based on the first object hypotheses and the second object hypotheses, and to generate 2D AoA information based on a 2D AoA spectrum analysis of the radar Rx data according to the plurality of 2D object hypotheses.

For example, a radar processor may be configured to determine a first 1D AoA spectrum corresponding to a first dimension of an Azimuth-Elevation domain based on radar Rx data, to determine a second 1D AoA spectrum corresponding to a second dimension of the Azimuth-Elevation domain based on the radar Rx data, to detect one or more first object hypotheses in the first dimension based on the first 1D AoA spectrum, to detect one or more second object hypotheses in the second dimension based on the second 1D AoA spectrum, to determine a plurality of 2D object hypotheses corresponding to the Azimuth-Elevation domain based on the first object hypotheses and the second object hypotheses, and to generate 2D AoA information based on a 2D AoA spectrum analysis of the radar Rx data according to the plurality of 2D object hypotheses.