Modeling Pointing, Acquisition, and Tracking Delays in Free-Space Optical Satellite Networks

TL;DR

This work addresses the challenge of accurately planning contacts in free-space optical satellite networks by modeling the retargeting delays within the Pointing, Acquisition, and Tracking (PAT) process. It develops a tunable, validated framework that spans coarse pointing ($T_{pointing}$), fine pointing and beam searching ($T_{seek}$, $T_{acq}$), and the transition to closed-loop tracking ($T_{acq-to-track}$), using pointing vectors $V_{init}$ and $V_{link}$ and structured beam-search patterns with $N_{steps}$ and a hexagonal layout. Validation against NASA TBIRD, LLCD, DSOC, and ESA Lunar Terminal data reveals multimodal PAT delay distributions and nonlinear scaling with initial pointing uncertainty and the field of uncertainty (FOU), aligning with observed acquisition times (e.g., tens of seconds for LEO and hundreds of seconds for IPN). The model provides a foundation for integrating PAT delays into routing and scheduling, enabling more accurate contact planning and higher utilization in future large-scale optical satellite networks, with open-source implementations to support adoption and benchmarking.

Abstract

Free-space optical inter-satellite links (OISLs) enable high-capacity space communications but require precise Pointing, Acquisition, and Tracking (PAT) between links. Current scheduling approaches often overlook or oversimplify PAT delays, leading to inefficient contact planning and overestimated network capacities. We present a validated model for quantifying retargeting delays, defined as the delay-inducing portion of PAT before data transmission begins, encompassing coarse pointing, fine pointing, and the handover to tracking. The model is grounded in mission data from NASA TBIRD, LLCD, DSOC, and ESA's Lunar Optical Communication Terminal. We find that PAT delays exhibit multimodal distributions based on prior link geometry and scale nonlinearly with initial pointing uncertainty and optical beam width. Integrating these delay models into routing and scheduling algorithms will enable more accurate contact planning and higher utilization in optical networks. The proposed model provides a foundation for evaluating performance and designing algorithms for future large-scale optical satellite networks.

Figures (5)

• Figure 1: Example of the angle change for Node A when slewing its optical head from an optical ground station to a deep space satellite, Node B.
• Figure 2: The seek satellite performing a beam search using a hexagonal spiral while the stare satellite detected the angle of incidence of the light using the quad-cell based track sensor.
• Figure 3: PDF overlayed with a histogram shows clear characterization of different types of optical links based on pointing and link acquisition delays.
• Figure 4: Average pointing delay increases exponentially for LEO-to-LEO links as CPA slew rate decreases, while IPN links have little difference.
• Figure 5: Both LEO and IPN acquisition delays increase exponentially as the FOU increases.