US 12,248,082 B2
Position-grid based machine learning for GNSS warm-start position accuracy improvement
William Morrison, San Francisco, CA (US); and Songwon Jee, San Jose, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on May 31, 2022, as Appl. No. 17/804,809.
Prior Publication US 2023/0384412 A1, Nov. 30, 2023
Int. Cl. H04W 64/00 (2009.01); G01S 5/02 (2010.01); G06F 18/20 (2023.01); G06F 18/243 (2023.01)
CPC G01S 5/0284 (2013.01) [G06F 18/24323 (2023.01); G06F 18/285 (2023.01); H04W 64/006 (2013.01)] 30 Claims
OG exemplary drawing
 
1. An apparatus for position estimation, comprising:
at least one memory; and
at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor is configured to:
determine, for each grid point within a range of an initial position of a user equipment (UE), a set of pseudorange (PR) residuals based on PRs for each space vehicle (SV) of a set of SVs;
determine, based on a machine learning (ML) classifier, a likelihood of whether the UE is approximate to a grid point based on a distribution pattern of PR residuals in a corresponding set of PR residuals; and
determine an estimated position of the UE based on the likelihood of whether the UE is approximate to the grid point.