The TU Delft Astrodynamics Toolbox (Tudat) is a free open-source software (FOSS) suite geared towards research and education in computational astrodynamics. It has been applied primarily to numerical simulation of the dynamics of objects in space, ranging from optimization of re-entry vehicle dynamics to the modeling of planetary spacecraft tracking and the dynamics of natural solar system bodies. The powerful and versatile estimation module of Tudat has been used for a broad range of studies for both current and future space missions. It has the capability to combine optical and radiometric tracking data from multiple spacecraft with Earth-based observations into a comprehensive estimation of the dynamics of both natural and artificial solar system bodies, as well as physical parameters of interest. Building upon this general and adaptable framework, recent developments have focused on incorporating the necessary functionality required for real tracking data analysis. In this paper, we present the integration of these capabilities into Tudat’s fully open-source framework, with a combined focus on planetary missions and Space Situational Awareness (SSA). At present, the software provides capabilities to process several categories of observational data: (i) deep-space Doppler and range tracking data of planetary missions collected by the Deep Space Network (DSN) and ESA’s ESTRACK, supporting multiple formats such as IFMS, ODF, and TNF; (ii) deep-space Doppler and VLBI tracking data of planetary missions collected by the Planetary and Radio Interferometry and Doppler Experiment (PRIDE) with radio (astronomy) telescopes; (iii) optical astrometry and radar tracking archived by the Minor Planet Center (MPC) and the Natural Satellite Data Center (NSDC). By computing observation residuals using existing orbital solutions as references, we show that our observation models are accurate to the intrinsic quality of the data (e.g., better than 0.05 mm/s for typical deep-space Doppler data). Additionally, we demonstrate that our dynamical models possess the level of fidelity necessary to enable precise orbit estimation, effectively leveraging the high quality of the available tracking data. Tudat is unique in providing modular and flexible open-source high-fidelity modeling across a broad range of orbital regimes, enabling interdisciplinary applications. We provide an overview of the data processing and estimation capabilities and give examples from various mission domains. These include high-precision orbit estimation using deep-space Doppler tracking data, orbit determination of cis-lunar/xGEO space debris in highly non-linear regimes (specifically targeting upper stages of lunar missions) from astrometric data, and estimation of small solar system bodies using astrometric data.

To ensure the secure operation of space assets, it is crucial to employ ground and/or space-based surveillance sensors to observe a diverse array of anthropogenic space objects (ASOs). This enables the monitoring of abnormal behavior and facilitates the timely identification of potential risks, thereby enabling the provision of continuous and effective Space Situational Awareness (SSA) services. One of the primary challenges in this endeavor lies in optimizing the tasking of surveillance sensors to maximize SSA capabilities. However, the complexity of the space environment, the vast number of ASOs, and the limitations imposed by available sensor resources present significant obstacles to effective sensor management. To tackle these challenges, various sensor tasking methods have been developed over the past few decades. In this paper, we comprehensively outline the fundamental characteristics of sensor tasking missions, and later examine the corresponding objective functions and algorithms employed for efficient optimization, respectively. Furthermore, we explore the practical application of sensor tasking methods in diverse organizations and provide insights into potential directions for future research, aiming to stimulate further advancements in this field.