Unified Orbit-Attitude Estimation and Sensor Tasking Framework for Autonomous Cislunar Space Domain Awareness Using Multiplicative Unscented Kalman Filter

• URL: http://arxiv.org/abs/2603.20579v1
• Date: Sat, 21 Mar 2026 00:25:34 GMT
• Title: Unified Orbit-Attitude Estimation and Sensor Tasking Framework for Autonomous Cislunar Space Domain Awareness Using Multiplicative Unscented Kalman Filter
• Abstract summary: The cislunar regime departs from near-Earth orbital behavior through strongly non-linear, non-Keplerian dynamics.<n> challenges arise from long-range observation requirements, restrictive sensor-target geometry and illumination conditions.<n>This work introduces an advanced framework for cislunar space domain awareness.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The cislunar regime departs from near-Earth orbital behavior through strongly non-linear, non-Keplerian dynamics, which adversely affect the accuracy of uncertainty propagation and state estimation. Additional challenges arise from long-range observation requirements, restrictive sensor-target geometry and illumination conditions, the need to monitor an expansive cislunar volume, and the large design space associated with space/ground-based sensor placement. In response to these challenges, this work introduces an advanced framework for cislunar space domain awareness (SDA) encompassing two key tasks: (1) observer architecture optimization based on a realistic cost formulation that captures key performance trade-offs, solved using the Tree of Parzen Estimators algorithm, and (2) leveraging the resulting observer architecture, a mutual information-driven sensor tasking optimization is performed at discrete tasking intervals, while orbital and attitude state estimation is carried out at a finer temporal resolution between successive tasking updates using an error-state multiplicative unscented Kalman filter. Numerical simulations demonstrate that our approach in Task 1 yields observer architectures that achieve significantly lower values of the proposed cost function than baseline random-search solutions, while using fewer sensors. Task 2 results show that translational state estimation remains satisfactory over a wide range of target-to-observer count ratios, whereas attitude estimation is significantly more sensitive to target-to-observer ratios and tasking intervals, with increased rotational-state divergence observed for high target counts and infrequent tasking updates. These results highlight important trade-offs between sensing resources, tasking cadence, and achievable state estimation performance that influence the scalability of autonomous cislunar SDA.

Related papers

• Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling [50.176885352114674]
  Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing.<n>This paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives.
  arXiv  Detail & Related papers  (2026-08-25T12:16:28Z)
• PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks [4.61832722799375]
  We propose a patch-based graph Transformer designed for efficient forecasting on irregular sensor networks.<n>We evaluate the proposed PatchSTG on real-world data from Rhode Island and additional large-scale datasets.
  arXiv  Detail & Related papers  (2026-06-02T09:53:28Z)
• MSACT: Multistage Spatial Alignment for Stable Low-Latency Fine Manipulation [4.439585594082787]
  Real-world fine manipulation, particularly in bimanual manipulation, requires low-latency control and stable visual localization.<n>Existing approaches make different trade-offs: action-chunking policies such as ACT enable low-latency execution and data efficiency but rely on dense visual features without explicit spatial consistency.<n>We introduce a multistage spatial attention module that extracts stable 2D attention points and jointly predicts future attention sequences with a temporal alignment loss.
  arXiv  Detail & Related papers  (2026-05-01T07:35:15Z)
• Event-based SLAM Benchmark for High-Speed Maneuvers [68.148886127117]
  Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution.<n>Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations.<n>We introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms.
  arXiv  Detail & Related papers  (2026-04-27T04:36:41Z)
• Uncertainty Estimation for Deep Reconstruction in Actuatic Disaster Scenarios with Autonomous Vehicles [68.82821445619759]
  principled uncertainty quantification is critical for active sensing strategies.<n>Evidential Deep Learning achieves the best reconstruction accuracy and uncertainty calibration across all sensor configurations.<n>These findings support Evidential Deep Learning as the preferred method for uncertainty-aware field reconstruction in real-time autonomous vehicle deployments.
  arXiv  Detail & Related papers  (2026-04-07T19:17:44Z)
• When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift [64.37959940809633]
  We study robustness of Proximal Policy Optimization (PPO) under temporally persistent sensor failures.<n>We show Transformer-based sequence policies substantially outperform, RNN, and SSMs in robustness, maintaining high returns even when large fractions of sensors are unavailable.
  arXiv  Detail & Related papers  (2026-03-04T22:21:54Z)
• Predict and Resist: Long-Term Accident Anticipation under Sensor Noise [14.759047709239788]
  Accident anticipation is essential for proactive and safe autonomous driving.<n>Two key challenges hinder real-world deployment: noisy or degraded sensory inputs from weather, motion blur, or hardware limitations.<n>We propose a unified framework that integrates diffusion-based denoising with a time-aware actor-critic model to address these challenges.
  arXiv  Detail & Related papers  (2025-11-10T11:41:43Z)
• Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing [2.5465367830324905]
  Event-based eye tracking holds significant promise for fine-grained cognitive state inference.<n>We introduce a model-agnostic, inference-time refinement framework to enhance the output of existing event-based gaze estimation models.
  arXiv  Detail & Related papers  (2025-06-14T14:48:11Z)
• Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation [60.80423207808076]
  Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation.<n>We propose the Dynamic Visual State Space (DVSS) block, which augments visual state space models with multi-scale convolutional operations.<n>We build HRVMamba, a novel model for efficient high-resolution representation learning.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers [5.4381914710364665]
  This study addresses posed by sparse sensor deployment and unreliable data by framing the problem as an environmental challenge. A graphkriformer model, Kriformer, estimates data at locations without sensors by mining spatial and temporal correlations, even with limited resources.
  arXiv  Detail & Related papers  (2024-09-23T11:01:18Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark [49.58762201363483]
  We introduce RatPose, a bio-robot motion prediction dataset constructed by considering the influence factors of individuals and environments. We propose a Dual-stream Motion-Scenario Decoupling framework that effectively separates scenario-oriented and motion-oriented features. We demonstrate significant performance improvements of the proposed textitDMSD framework on different difficulty-level tasks.
  arXiv  Detail & Related papers  (2023-05-17T14:14:31Z)
• Neural Networks with Quantization Constraints [111.42313650830248]
  We present a constrained learning approach to quantization training. We show that the resulting problem is strongly dual and does away with gradient estimations. We demonstrate that the proposed approach exhibits competitive performance in image classification tasks.
  arXiv  Detail & Related papers  (2022-10-27T17:12:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.