Pulsar-Leveraged Autonomous Navigation Testbed System (PLANTS): A Low-Cost Software-Hardware Hybrid Testbed for Pulsar-based Autonomous Navigation (XNAV) Positioning, Navigation, and Timing (PNT) Solutions

Abstract

The increasing number of private and public actors interested in space-based missions has driven need for greater flexibility and reliability in regards to navigation. Autonomous navigation in space will reduce reliance on ground-based systems and high operational costs due to crowded communication networks. Further, there is a clear need for autonomous navigation solutions in GPS-denied environments, as well as deep-space regions in which traditional GPS methods are infeasible. One promising approach for achieving autonomous navigation in the dynamic landscape of space is X-ray pulsar-based navigation (XNAV). XNAV capitalizes on the periodicity of pulsar-emitted X-rays for positioning, navigation, as well as determining and responding to timing error (PNT). In this paper, a novel, flexible pulsar simulation framework for the testing, and validation of XNAV systems is presented. Pulsar-Leveraged Autonomous Navigation Testbed System (PLANTS) is a low-cost software-hardware hybrid testbed for XNAV PNT solutions. PLANTS simulates high-fidelity pulsar X-ray events along desired flight trajectories over a user-defined mission timeline, which can be used to optimize XNAV hardware and mission planning components (such as spacecraft attitude and X-ray detector orientation planning, based on output pulsar viewing schedules and angles over time). Ultimately, this testbed provides a flexible platform for a wide array of future XNAV research and development efforts aimed at the goal of mission-readiness and sustained space operations. The goal of the PLANTS framework is to develop a system for XNAV project teams which is cost-efficient, algorithm-agnostic (i.e. supports interoperability with current and emerging software toolkits), and incorporates hardware-in-the-loop (HWIL). This paper describes the first iteration of PLANTS, which leverages software-defined radios (SDRs), coupled with a number of software utilities including the Python-based PINT pulsar timing software package. Initial results exhibit successful outputs of pulsar data extraction, transformation, and loading (ETL), flight plans, timing models, and light curves portraying photon arrival events. The future of XNAV will require the development of effective, intelligent navigation algorithms and accessible testing facilities with HWIL. The PLANTS framework meets these needs and empowers advancement of the state-of-the-art in autonomous space navigation.