US 11,658,712 B1
Computer implemented method for reducing adaptive beamforming computation using a Kalman filter
Cameron Musgrove, Bixby, OK (US); and Jason Keen, Huntsville, AL (US)
Assigned to lERUS Technologies, Inc., Huntsville, AL (US)
Filed by Cameron Musgrove, Bixby, OK (US); and Jason Keen, Huntsville, AL (US)
Filed on Mar. 31, 2022, as Appl. No. 17/709,457.
Int. Cl. H04L 1/02 (2006.01); H04B 7/0456 (2017.01); H04B 7/0452 (2017.01); H01Q 3/26 (2006.01)
CPC H04B 7/0456 (2013.01) [H01Q 3/2682 (2013.01); H04B 7/0452 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A computer implemented method for reducing adaptive beamforming computation resources for estimating and updating a model of beam weights, comprising:
a) estimating, through a computer device by a base station, optimal beam pattern weights of an antenna electromagnetic element signal duration, using an adaptive beamforming algorithm for at least 3 time steps of the antenna electromagnetic element signal, wherein a time step is a time interval between reference signal transmissions;
b) creating, through the computer, an initial linear model for either magnitude or phase components of the optimal beam pattern weights computed from the adaptive beamforming algorithm estimates;
c) estimating, through the computer, for each time step, a measurement of the optimal beam pattern weights, using a reduced set of data comprising 5-20% of first samples of data generated by the antenna electromagnetic element signal duration; and
d) computing new beam pattern weights, through the computer, using a magnitude state estimation filter and/or phase state estimation filter, wherein the computation resources required by the adaptive beamforming algorithm to obtain the new beam pattern weights are reduced by 80 to 90%.