Abstract
The constant irruption of new sensors is a challenge for software systems that do not rely on generic data models able to manage change or innovation. Several data modeling standards exist. Some of these address the problem from a generic perspective but are far too complex for the kind of applications targeted by this work, while others focus strictly on specific kinds of sensors. These approaches pose a problem for the maintainability of software systems dealing with sensor data. This work presents ASTROLABE, a generic and extensible data model specifically devised for trajectory determination systems working with sensors whose error distributions may be fully modeled using means and covariance matrices. A data model relying on four fundamental entities (observation, state, instrument, mathematical model) and related metadata is described; two compliant specifications (for file storage and network communications) are presented; a portable C++ library implementing these specifications is also briefly introduced. STROLABE, integrated in CTTC’s trajectory determination system NAVEGA, has been extensively used since 2009 in research and production (real-life) projects, coping successfully with a significant variety of sensors. Such experience helped to improve the data model and validate its suitability for the target problem. The authors are considering putting ASTROLABE in the public domain.
Highlights
In the context of this paper, Trajectory Determination Systems (TDS) [1] are software components that compute trajectories using a variety of sensor measurements as input
This work presents ASTROLABE, a generic and extensible data model devised for trajectory determination systems working with sensors whose error distributions may be fully modeled using means and covariance matrices
Over the past years we have developed a number of TDS [13,14,15] that achieved extensibility through genericity
Summary
In the context of this paper, Trajectory Determination Systems (TDS) [1] are software components that compute trajectories using a variety of sensor measurements as input. Over the past years we have developed a number of TDS [13,14,15] that achieved extensibility through genericity For this purpose, we designed internal estimation engines and external interface engines, for a wide class of data and mathematical models in a way that dealing with new data types or new models neither requires to modify the estimation engines nor the input/output ones [14,15], following a plug-and-play paradigm. The main advantage of such kinds of standards, being tailored and optimized for specific sensors, is, at the same time, the reason to avoid them; one of the goals of the research work presented in this paper is to devise a generic data model able to manage diversity and evolution. An example describing ASTROLABE data and metadata used in a real-life project (GAL) may be found in Appendix B
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