Fast Multi-Constellation GNSS Satellite Selection: Convex and Mixed-Integer Programming Approaches

A set of fast optimization-based procedures for the selection of an optimal subset of satellites from those within view of a receiver is provided. Resource limitations, the low marginal utility of using an all-in-view navigation solution, and the need for solution separation procedures in safety-cri...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on vehicular technology Ročník 74; číslo 6; s. 8492 - 8507
Hlavní autori: Zewge, Natnael S., Bang, Hyochoong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Atmospheric measurements
BeiDou Navigation Satellite System
Extraterrestrial measurements
Genetic algorithms
Geometry
Global navigation satellite system
Integer programming
Mixed integer
Mixed-integer programming
multi-constellation GNSS
Navigation systems
optimal experiment design
Optimization
Position measurement
Programming
Receivers
Safety critical
Satellite broadcasting
Satellite constellations
Satellite navigation systems
satellite selection
Satellites
Searching
second-order cone programming (SOCP)
Semidefinite programming
semidefinite programming (SDP)
weighted geometric dilution of precision (WGDOP)
ISSN:0018-9545, 1939-9359
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:A set of fast optimization-based procedures for the selection of an optimal subset of satellites from those within view of a receiver is provided. Resource limitations, the low marginal utility of using an all-in-view navigation solution, and the need for solution separation procedures in safety-critical navigation systems necessitate the use of a smaller subset from a collection of visible satellites. The goal of the present research is the fast selection of <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> satellites from among <inline-formula><tex-math notation="LaTeX">m</tex-math></inline-formula> that are available such that the selection is maximally informative (having minimum covariance measure) of user position. With many GNSS satellites currently deployed and more than four satellites required for integrity monitoring purposes, brute-force search through all <inline-formula><tex-math notation="LaTeX">^{m}C_{k}</tex-math></inline-formula> options is not a reasonable approach. Satellite geometry and measurement precision are essential metrics in satellite selection; however, much of the literature on this topic has focused on geometry alone. In this work, we jointly account for satellite geometry and measurement precision in a single optimization framework. We present a semidefinite programming (SDP) formulation that considers both geometry and measurement quality. Afterward, we provide a reduction of the SDP form to that of a second-order cone program (SOCP). The SOCP formulation yields significant speed improvements while providing the same solution as the SDP form. Furthermore, we demonstrate exact satellite selection using a mixed-integer SOCP formulation, a first in the literature. Additionally, we show how context-specific constraints for GNSS can be easily incorporated into our selection framework. To illustrate the proposed set of methods, experiments are conducted on real-world multi-constellation GNSS data from GPS, GLONASS, BeiDou, Galileo, and QZSS satellites. For a comparative perspective, we offer an efficient greedy search technique, a genetic algorithm, a globally optimal exhaustive search (where applicable), and an all-in-view strategy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2025.3534631