Closed-Loop Integration of Neural Ambiguity, Gravity, and Line-of-Sight Estimators for GNSS/IMU/SAL Rocket GNC

This paper presents the closed-loop integration of three neural estimators within a physically based guidance, navigation, and control (GNC) architecture for a canard-controlled rocket. The estimators support Global Navigation Satellite System (GNSS) carrier-phase ambiguity processing, reconstruct the gravity vector in body axes, and correct the terminal line-of-sight (LOS) estimate obtained by fusing GNSS, inertial measurement unit (IMU), and semi-active laser (SAL) quadrant-detector information. Building on earlier specialized or partially integrated studies, the contribution is the simultaneous embedding of the three estimators in a common nonlinear six-degree-of-freedom closed loop and their evaluation, against a matched model-based baseline and an eight-configuration ablation study, under nominal, wind, high-angular-rate, GNSS-degradation, and combined-disturbance scenarios. By combining complementary attitude, gravity, and target-relative information while retaining physical validation and model-based fallback, the architecture is designed to improve navigation resilience and functional coverage across multiple sensor-degradation modes. All 500 closed-loop evaluation trajectories completed without numerical divergence. The overall trajectory-averaged squared vector errors were 4.13×10−3m2/s4 for gravity and 1.59×10−3(dimensionless, corresponding to a 2.3∘ RMS-equivalent angular error) for LOS; under combined disturbances, their mean errors were 3.08 and 2.99 times the corresponding nominal values. Relative to the matched model-based baseline, the integrated architecture improved the GNSS ambiguity fix-success rate from 96.4% to 97.9% and reduced terminal-guidance circular error probable (CEP50) from 0.49 m to 0.35 m (an absolute reduction of 0.140 m; paired-bootstrap 95% CI [0.118,0.156] m), corresponding to a 28.6% relative reduction, and the ablation study indicated a near-additive, monotonically improving contribution from each of the three modules. The results demonstrate simultaneous operation of the three modules as active components of the propagated GNC loop over the tested conditions, with a quantified improvement over the matched conventional configuration.

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Publication Details

Journal
Sensors
Published
2026-09-29
DOI
https://doi.org/10.3390/s26196177
Primary Topic
Inertial Sensor and Navigation
Type
article
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article

Closed-Loop Integration of Neural Ambiguity, Gravity, and Line-of-Sight Estimators for GNSS/IMU/SAL Rocket GNC

Raúl de Celis, Luís Cadarso
Sensors
Inertial Sensor and Navigation
article

Closed-Loop Integration of Neural Ambiguity, Gravity, and Line-of-Sight Estimators for GNSS/IMU/SAL Rocket GNC

Raúl de Celis, Luís Cadarso
article en

Abstract

This paper presents the closed-loop integration of three neural estimators within a physically based guidance, navigation, and control (GNC) architecture for a canard-controlled rocket. The estimators support Global Navigation Satellite System (GNSS) carrier-phase ambiguity processing, reconstruct the gravity vector in body axes, and correct the terminal line-of-sight (LOS) estimate obtained by fusing GNSS, inertial measurement unit (IMU), and semi-active laser (SAL) quadrant-detector information. Building on earlier specialized or partially integrated studies, the contribution is the simultaneous embedding of the three estimators in a common nonlinear six-degree-of-freedom closed loop and their evaluation, against a matched model-based baseline and an eight-configuration ablation study, under nominal, wind, high-angular-rate, GNSS-degradation, and combined-disturbance scenarios. By combining complementary attitude, gravity, and target-relative information while retaining physical validation and model-based fallback, the architecture is designed to improve navigation resilience and functional coverage across multiple sensor-degradation modes. All 500 closed-loop evaluation trajectories completed without numerical divergence. The overall trajectory-averaged squared vector errors were 4.13×10−3m2/s4 for gravity and 1.59×10−3(dimensionless, corresponding to a 2.3∘ RMS-equivalent angular error) for LOS; under combined disturbances, their mean errors were 3.08 and 2.99 times the corresponding nominal values. Relative to the matched model-based baseline, the integrated architecture improved the GNSS ambiguity fix-success rate from 96.4% to 97.9% and reduced terminal-guidance circular error probable (CEP50) from 0.49 m to 0.35 m (an absolute reduction of 0.140 m; paired-bootstrap 95% CI [0.118,0.156] m), corresponding to a 28.6% relative reduction, and the ablation study indicated a near-additive, monotonically improving contribution from each of the three modules. The results demonstrate simultaneous operation of the three modules as active components of the propagated GNC loop over the tested conditions, with a quantified improvement over the matched conventional configuration.

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Closed-Loop Integration of Neural Ambiguity, Gravity, and Line-of-Sight Estimators for GNSS/IMU/SAL Rocket GNC — Raúl de Celis, Luís Cadarso · Sensors (2026) | TGRS Research Map | TGRS