Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.15949

Distributed Observation Scheduling and Sensor Fusion For Large Agile Constellations

Nicholas Niziolek·Ryan Draves·Nolan Ales·Nolan Stevenson·Eli Jones·Eric Frew·Nisar Ahmed
View original source ↗Read source document ↗Export metadata ↓

Abstract

With the rapid increase in interest towards large scale earth observing satellite constellations, the problem of properly commanding and utilizing these systems has become increasingly relevant. This work focuses on a complete distributed approach to this problem, incorporating an observation task allocation and scheduling algorithm in addition to a sensor data fusion algorithm for end-to-end target coverage and processing. Observation task allocation and scheduling is performed in this work through the combination of the bid warped consensus based bundle algorithm (BWCBBA) and a lightweight observation scheduler for bid scoring. This method produces a unique mapping of tasks to command agents when allowed to iterate sufficiently. The approach to distributed data fusion in this work occurs primarily through an ownership model in which one satellite takes ownership of a system track in the constellation. After local information is shared between the sensing nodes to create the system tracks, the dedicated fusion nodes assume ownership for further information fusion and track information is disseminated throughout the constellation for data association. This paper analyzes the performance of these two algorithms over a set of increasingly decentralized architectures to highlight the performance losses compared to more traditional centralized methods.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nicholas Niziolek, Ryan Draves, Nolan Ales, Nolan Stevenson, Eli Jones, Eric Frew, Nisar Ahmed. 2026-10-11. Distributed Observation Scheduling and Sensor Fusion For Large Agile Constellations. https://arxiv.org/abs/2610.15949

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Age of Trust (AoT): A Continuous Verification Framework for Wireless Networks

Zero Trust is a new security vision for 6G networks that emphasises the philosophy of never trust and always verify. However, there is a fundamental trade-off between the wireless transmission efficiency and the trust level, which is reflected by the verification interval and its adaptation strategy. More importantly, the mathematical framework to characterise the trust level of the adaptive verification strategy is still missing. Inspired by this vision, we propose a concept called age of trust (AoT) to capture the characteristics of the trust level degrading over time, with the definition of the time elapsed since the last verification of the target user's trust plus the initial age, which depends on the trust level evaluated at that verification. The higher the trust level, the lower the initial age. To evaluate the trust level in the long term, the average AoT is used. We then investigate how to find a compromise between average AoT and wireless transmission efficiency with limited resources. In particular, we address the bi-objective optimization (BOO) problem between average AoT and throughput over a single link with arbitrary service process, where the identity of the receiver is constantly verified, and we devise a periodic verification scheme and a Q-learning-based scheme for constant process and random process, respectively. We also tackle the BOO problem in a multiple random access scenario, where a trust-enhanced frame-slotted ALOHA is designed. Finally, the numerical results show that our proposals can achieve a fair compromise between trust level and wireless transmission efficiency, and thus have a wide application prospect in various zero-trust architectures.

eess.SY↗
arXiv2026

LiDAL-Assisted RLNC-NOMA in OWC Systems

Optical wireless communication (OWC) is a key enabler for immersive indoor data transmission in future wireless networks. However, managing multi-user interference remains challenging in dense indoor OWC deployments with multiple optical access points (APs) serving multiple users. In this paper, we propose a dual-function OWC system for communication and localization. For data transmission, we design non-orthogonal multiple access (NOMA) combined with random linear network coding (RLNC): NOMA serves multiple users simultaneously via power-domain multiplexing, while RLNC improves decoding robustness under residual noise and processing impairments. The system is assisted by a light detection and localization (LiDAL) module that estimates users' spatio-temporal presence and location to enable dynamic user association. LiDAL also supports channel state information (CSI) estimation in realistic indoor scenarios where CSI may be noisy and/or highly correlated. We evaluate RLNC-NOMA by analyzing the probability of successful decoding and comparing it with conventional NOMA and orthogonal schemes. In addition, we derive the Cramer-Rao lower bound (CRLB) to evaluate the localization accuracy. The results indicate that RLNC-NOMA increases the probability of successful decoding, and that the proposed unbiased location estimator achieves 44% better CSI accuracy than the baseline CSI estimation and improves the overall system performance.

eess.SY↗
arXiv2026

Ensemble-MIX: Enhancing Sample-Efficiency in Multi-Agent RL via Selective Ensemble-Based Exploration

Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large joint action space and the high variance intrinsic to MARL environments. To tackle these issues, we propose a novel algorithm that combines a decomposed centralized critic with decentralized ensemble learning, incorporating several key contributions. The main component in our scheme is a selective exploration method that leverages ensemble kurtosis. We extend the global decomposed critic with a diversity-regularized ensemble of individual critics and utilize its excess kurtosis to guide exploration toward high-uncertainty states and actions. To improve sample efficiency, we train the centralized critic with a novel truncated variation of the TD($λ$) algorithm, enabling efficient off-policy learning with reduced variance. On the actor side, our suggested algorithm adapts the mixed samples approach to MARL, mixing on-policy and off-policy loss functions for training the actors. This approach balances between stability and efficiency and outperforms purely off-policy learning. The evaluation shows our method outperforms state-of-the-art baselines on standard MARL benchmarks, including a variety of SMAC II maps.

eess.SY↗