The SINS/DVS Integrated Navigation Based on Doppler Velocity of Star (DVS) and Cross-Directional Difference-DVS (CDD-DVS)

The SINS/DVS integrated navigation can effectively correct velocity errors in SINS, while its navigation accuracy is affected by time-varying systematic errors inherent in DVS measurements and the geometric constraint of requiring at least three non-coplanar stars. To address these limitations, a novel SINS/DVS integrated navigation method using both conventional DVS and a newly constructed Cross-Directional Difference DVS (CDD-DVS) is proposed(referred to as SINS/CDD-DVS). The CDD-DVSmeasurementis formed by differencing DVS observations from two distinct directions within the same epoch, which simultaneously eliminates common-mode time-varying systematic biases and synthesizes avirtualvelocity constraint along a third observation direction using only two stars. Unlike traditional same-direction differencing, which suppresses useful motion signals along with errors, the proposed cross-directional strategy preserves essential vehicle motion information while effectively canceling correlated systematic biases. Simulation results demonstrate that the proposed SINS/CDD-DVS method reduces total position error by 45.2% compared to conventional SINS/DVS under star-limited conditions, confirming a significant improvement in navigation accuracy of the integrated navigation system.

Authors

Institutions

Publication Details

Journal
Unmanned Systems
Published
2026-06-30
DOI
https://doi.org/10.1142/s2301385027410093
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The SINS/DVS Integrated Navigation Based on Doppler Velocity of Star (DVS) and Cross-Directional Difference-DVS (CDD-DVS)

Yuqing Yang, Peiyan Zhang, Ruonan Wang, Hao Lifei
Unmanned Systems
Inertial Sensor and Navigation
article

The SINS/DVS Integrated Navigation Based on Doppler Velocity of Star (DVS) and Cross-Directional Difference-DVS (CDD-DVS)

Yuqing Yang, Peiyan Zhang, Ruonan Wang, Hao Lifei
article en

Abstract

The SINS/DVS integrated navigation can effectively correct velocity errors in SINS, while its navigation accuracy is affected by time-varying systematic errors inherent in DVS measurements and the geometric constraint of requiring at least three non-coplanar stars. To address these limitations, a novel SINS/DVS integrated navigation method using both conventional DVS and a newly constructed Cross-Directional Difference DVS (CDD-DVS) is proposed(referred to as SINS/CDD-DVS). The CDD-DVSmeasurementis formed by differencing DVS observations from two distinct directions within the same epoch, which simultaneously eliminates common-mode time-varying systematic biases and synthesizes avirtualvelocity constraint along a third observation direction using only two stars. Unlike traditional same-direction differencing, which suppresses useful motion signals along with errors, the proposed cross-directional strategy preserves essential vehicle motion information while effectively canceling correlated systematic biases. Simulation results demonstrate that the proposed SINS/CDD-DVS method reduces total position error by 45.2% compared to conventional SINS/DVS under star-limited conditions, confirming a significant improvement in navigation accuracy of the integrated navigation system.

Unmanned Systems
Twitter (United States) (US)
Openalex Percentile: Top 4%
Inertial Sensor and Navigation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.