US 12,252,274 B2
Space vehicle geometry based machine learning for measurement error detection and classification
Songwon Jee, San Jose, CA (US); Yunxiang Liu, Kirkland, WA (US); and William Morrison, San Francisco, 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. 17/804,849.
Prior Publication US 2023/0382565 A1, Nov. 30, 2023
Int. Cl. B64G 1/28 (2006.01); B64G 1/24 (2006.01); G01S 5/02 (2010.01); G01S 19/42 (2010.01)
CPC B64G 1/28 (2013.01) [B64G 1/244 (2019.05); G01S 5/0218 (2020.05); G01S 19/42 (2013.01); B64G 1/245 (2023.08); G01S 19/428 (2013.01)] 30 Claims
OG exemplary drawing
 
1. An apparatus for position estimation, comprising:
memory; and
at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to:
determine for each space vehicle (SV) of a set of SVs at least a geometric orientation with respect to a user equipment (UE);
determine, based on a machine learning (ML) classifier and the determined geometric orientation with respect to the UE for each SV of at least one subset of the set of SVs, a relative pseudorange (PR) weight for each SV of the set of SVs, wherein the relative PR weight for one SV of the set of SVs is provided by the ML classifier based on whether a spherical distance between the one SV and each SV of the at least one subset of the set of SVs is below a distance threshold; and
estimate a position of the UE based on PR measurements of each SV of the set of SVs and the relative PR weight for each SV of the set of SVs.