Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2607.25235

Progressive Replacement Sequence Planning for Laser-Enhanced BeiDou Navigation Constellations

Huan Yan·Juan A. Fraire·Ziqi Yang·Kanglian Zhao·Wenfeng Li·Jinjun Zheng·Ziyu Wang·Changgang Zheng
View original source ↗Read source document ↗Export metadata ↓

Abstract

The early satellites of the third-generation BeiDou Navigation Satellite System (BDS-3) are approaching the end of their design lifetime, making progressive constellation replacement an inevitable engineering task. Meanwhile, laser inter-satellite links (LISLs) provide high-precision time transfer, sub-millimeter-level ranging, and high-rate data forwarding capabilities, offering a promising upgrade path for future BeiDou satellites. This paper investigates the replacement-sequence planning problem for progressively replacing legacy microwave satellites with laser-enabled satellites from a networking perspective. To address this problem, three main contributions are made: (i) BeiDou progressive replacement is formulated as a network-aware launch sequence planning problem over evolving laser--microwave hybrid constellation states; (ii) an integer linear programming (ILP) model is developed to evaluate the networking gain of each candidate launch action under practical engineering constraints; and (iii) a priority-aware search heuristic is introduced to reduce the action evaluation space for efficient round-wise decision making. Simulations based on the actual BDS-3 constellation demonstrate that the proposed replacement sequence achieves a more favorable hybrid-network evolution than the historical BDS-3 launch order, including earlier formation of a ground-accessible laser-enhanced network structure, better utilization of laser-link resources, fewer residual non-anchor satellites, and lower microwave-layer waiting delay. The proposed framework can serve as a network-aware decision-support tool for future BeiDou evolution, and the resulting replacement sequence can provide direct engineering guidance for BeiDou replacement planning.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Huan Yan, Juan A. Fraire, Ziqi Yang, Kanglian Zhao, Wenfeng Li, Jinjun Zheng, Ziyu Wang, Changgang Zheng. 2026-07-28. Progressive Replacement Sequence Planning for Laser-Enhanced BeiDou Navigation Constellations. https://arxiv.org/abs/2607.25235

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

KEEP EXPLORING

Related papers

arXiv2026

Constrained Feedback Control of Nonlinear Systems via Approximate HJB and Control Barrier Functions

This paper presents a two-stage framework for constrained feedback control of input-affine nonlinear systems. Offline, an approximate value function for the unconstrained problem is computed, for example using Hamilton--Jacobi--Bellman (HJB)-based policy iteration. Online, the proposed quadratic program (QP) minimizes the pre-Hamiltonian evaluated using the approximate value-function gradient subject to safety constraints enforced by control barrier functions (CBFs). This architecture decouples performance optimization from constraint enforcement, allowing constraints to be modified without recomputing the value function. As in CBF-QP architectures based on control Lyapunov functions (CLFs), safety is enforced as a hard constraint; however, the performance objective targets approximate optimality rather than a prescribed Lyapunov decay. Numerical results on a linear 2-state hovercraft and a nonlinear 9-state spacecraft attitude-control problem show agreement with the constrained open-loop optimal control problem (OCP) benchmark in the linear case, and performance close to the OCP benchmark, improving on CLF-based controllers, in the nonlinear case.

eess.SY↗
arXiv2026

Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization technique to infer joint state-parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters, including the measurement-noise covariance, are approximated using particle-based Stein Variational Gradient Descent (SVGD), enabling stable real-time inference. To handle parameters subject to physical constraints, we further introduce constrained variants that enforce them through an alternating direction method of multipliers (ADMM) splitting of the SVGD update, including nonlinear equality constraints that standard particle filters cannot readily handle. We derive a stability bound that relates the approximation error in the parameter posterior to the resulting error in the marginal state distribution. Performance of the proposed filters is validated on three case studies: a fed-batch bioreactor with Haldane kinetics and a damped pendulum, both under physical constraints, and a neural-network-augmented dynamic system. The examples cover parameter estimation under inequality and equality constraints and online neural-network training within a dynamical model.

eess.SY↗
arXiv2026

Firing Rate Neural Network Implementations of Model Predictive Control

Human and animal brains perform planning to enable complex movements and behaviors, a process that can be effectively described using model predictive control (MPC). How could the brain physically implement MPC? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. We propose a constructive method; no training is required. This is done first applying the projected gradient method to the dual problem to derive a baseline neural network implementation. We then use factorization and contraction analysis to systematically generate alternative network architectures; in other words, we systematically generate hypotheses for how planning is done in the brain via neural dynamics. Finally, we present numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand), including one example in which imposing sparse connectivity (a property observed in brain networks) does not degrade control performance.

eess.SY↗