Agent and model-based simulation framework for deep space navigation analysis and design
Anzalone, Evan John
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As the number of spacecraft in simultaneous operation continues to grow, there is an increased dependency on ground-based navigation support. The current baseline system for deep space navigation utilizes Earth-based radiometric tracking, which requires long duration, often global, observations to perform orbit determination and generate a state update. The age, complexity, and high utilization of the assets that make up the Deep Space Network (DSN) pose a risk to spacecraft navigation performance. With increasingly complex mission operations, such as automated asteroid rendezvous or pinpoint planetary landing, the need for high accuracy and autonomous navigation capability is further reinforced. The Network-Based Navigation (NNAV) method developed in this research takes advantage of the growing inter-spacecraft communication network infrastructure to allow for autonomous state measurement. By embedding navigation headers into the data packets transmitted between nodes in the communication network, it is possible to provide an additional source of navigation capability. Simulation results indicate that as NNAV is implemented across the deep space network, the state estimation capability continues to improve, providing an embedded navigation network. To analyze the capabilities of NNAV, an analysis and simulation framework is designed that integrates navigation and communication analysis. Model-Based Systems Engineering (MBSE) and Agent-Based Modeling (ABM) techniques are utilized to foster a modular, expandable, and robust framework. This research has developed the Space Navigation Analysis and Performance Evaluation (SNAPE) framework. This framework allows for design, analysis, and optimization of deep space navigation and communication architectures. SNAPE captures high-level performance requirements and bridges them to specific functional requirements of the analytical implementation. The SNAPE framework is implemented in a representative prototype environment using the Python language and verified using industry standard packages. The capability of SNAPE is validated through a series of example test cases. These analyses focus on the performance of specific state measurements to state estimation performance, and demonstrate the core analytic functionality of the framework. Specific cases analyze the effects of initial error and measurement uncertainty on state estimation performance. The timing and frequency of state measurements are also investigated to show the need for frequent state measurements to minimize navigation errors. The dependence of navigation accuracy on timing stability and accuracy is also demonstrated. These test cases capture the functionality of the tool as well as validate its performance. The SNAPE framework is utilized to capture and analyze NNAV, both conceptually and analytically. Multiple evaluation cases are presented that focus on the Mars Science Laboratory's (MSL) Martian transfer mission phase. These evaluation cases validate NNAV and provide concrete evidence of its operational capability for this particular application. Improvement to onboard state estimation performance and reduced reliance on Earth-based assets is demonstrated through simulation of the MSL spacecraft utilizing NNAV processes and embedded packets within a limited network containing DSN and MRO. From the demonstrated state estimation performance, NNAV is shown to be a capable and viable method of deep space navigation. Through its implementation as a state augmentation method, the concept integrates with traditional measurements and reduces the dependence on Earth-based updates. Future development of this concept focuses on a growing network of assets and spacecraft, which allows for improved operational flexibility and accuracy in spacecraft state estimation capability and a growing solar system-wide navigation network.