Patent classifications
G01S3/74
RADAR SIGNAL PROCESSING WITH FORWARD-BACKWARD MATRIX
Aspects of the present disclosure are directed to radar signal processing apparatuses and methods. As may be implemented in accordance with one or more embodiments, digital signals representative of received reflections of radar signals transmitted towards a target are mathematically processed to provide or construct a matrix pencil based on or as a function of a forward-backward matrix. Eigenvalues of the matrix pencil are computed and an estimation of the direction of arrival (DoA) of the target is output based on the computed eigenvalues.
Method for optimizing the orientation performance of radiation source orientation system
The present invention relates to a radiation source orientation technology. The invention discloses a method for optimizing the orientation performance of radiation source orientation system, which comprises the following steps: establishing a radiation source orientation matrix; obtaining the non-zero singular value of the orientation matrix; classifying orientation noise that affects the radiation source orientation system according to the distribution characteristic of noise energy; determining the optimal orientation matrix of the radiation source orientation system according to the minimum non-zero singular value σ.sub.min of the orientation matrix and its number of array elements m; determining the optimal orientation array according to the non-zero singular value of orientation matrix considering the distribution of different noise energy. The invention lays a foundation for the optimal design of a non-planar array in a radiation source orientation system. The optimal orientation matrix and array provided by the invention can be used to effectively improve the orientation accuracy of the radiation source orientation system and the resistance of the orientation system to interference.
Radar calibration system
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.
Radar calibration system
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.
METHOD FOR ESTIMATING DIRECTION OF ARRIVAL OF AN L-TYPE COPRIME ARRAY BASED ON COUPLED TENSOR DECOMPOSITION
The disclosure provides a method for estimating a direction of arrival of an L-type coprime array based on coupled tensor decomposition. The method includes: constructing an L-type coprime array with separated sub-arrays and modeling a received signal; deriving a fourth-order covariance tensor of the received signal of the L-type coprime array; deriving a fourth-order virtual domain signal corresponding to an augmented virtual uniform cross array; dividing the virtual uniform cross array by translation; constructing a coupled virtual domain tensor by stacking a translation virtual domain signal; and obtaining a direction of arrival estimation result by coupled virtual domain tensor decomposition. The present invention makes full use of the spatial correlation property of the virtual domain tensor statistics of the constructed L-type coprime array with the separated sub-arrays, and realizes high-precision two-dimensional direction of arrival estimation by coupling the virtual domain tensor processing, which can be used for target positioning.
System for receiving communications
Methods and systems for spatial filtering transmitters and receivers capable of simultaneous communication with one or more receivers and transmitters, respectively, the receivers capable of outputting source directions to humans or devices. The methods and systems use spherical wave field partial wave expansion (PWE) models for transmitted and received fields at antennas and for waves generated by contributing sources. The source PWE models have expansion coefficients expressed as functions of directional coordinates of the sources. For spatial filtering receivers a processor uses the output signals from at least one sensor outputting signals consistent with Nyquist criteria representative of the wave field and the source PWE model to determines directional coordinates of sources (wherein the number of floating point operations are reduced) and outputs the directional coordinates and communications to a reporter configured for reporting information to humans. For spatial filtering transmitters a processor uses known receiver directions and source partial wave expansions to generate signals for transducers producing a composite total wave field conveying communications to the specified receivers. The methods and communications reduce the processing required for transmitting and receiving spatially filtered communications.
System for receiving communications
Methods and systems for spatial filtering transmitters and receivers capable of simultaneous communication with one or more receivers and transmitters, respectively, the receivers capable of outputting source directions to humans or devices. The methods and systems use spherical wave field partial wave expansion (PWE) models for transmitted and received fields at antennas and for waves generated by contributing sources. The source PWE models have expansion coefficients expressed as functions of directional coordinates of the sources. For spatial filtering receivers a processor uses the output signals from at least one sensor outputting signals consistent with Nyquist criteria representative of the wave field and the source PWE model to determines directional coordinates of sources (wherein the number of floating point operations are reduced) and outputs the directional coordinates and communications to a reporter configured for reporting information to humans. For spatial filtering transmitters a processor uses known receiver directions and source partial wave expansions to generate signals for transducers producing a composite total wave field conveying communications to the specified receivers. The methods and communications reduce the processing required for transmitting and receiving spatially filtered communications.
Leveraging spectral diversity for machine learning-based estimation of radio frequency signal parameters
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.
Leveraging spectral diversity for machine learning-based estimation of radio frequency signal parameters
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.
AoX Multipath Detection
A system and method for detecting a multipath environment is disclosed. A first pseudospectrum based on azimuth angle and elevation angle is created. The result of this first pseudospectrum are used to create a second pseudospectrum based on polarization and field ratio. The sharpness of the results for these two pseudospectrums is determined and may be used to detect whether a multipath environment exists. If a multipath environment is believed to exist, the results from this device are ignored in determining the spatial position of the object.