A computer program has been developed that analyzes Deep Space Network monitor data, looking for changes of trends in critical parameters. This program represents a significant improvement over the previous practice of manually plotting data and visually inspecting the resulting graphs to identify trends. This program uses proven numerical techniques to identify trends. When a statistically significant trend is detected, then it is characterized by means of a symbol that can be used by pre-existing model-based reasoning software. The program can perform any of the following functions:
- Given an expectation that data in a given list should exhibit an upward, downward, constant, or unknown trend, it can determine whether the data do or do not follow such a trend.
- Given a list of data, it can identify which of the aforementioned trends the data follow.
- Given two lists of data, it can determine whether or not both follow the same trend.
This program can be executed on a variety of computers. It can be distributed in either source code or binary code form. It must be run in conjunction with any one of a number of Lisp compilers that are available commercially or as shareware.
This program was written by Mark James of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free online at www.techbriefs.com /tsp under the Software category.
This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-42107.
This Brief includes a Technical Support Package (TSP).
Identifying Trends in Deep Space Network Monitor Data
(reference NPO-42107) is currently available for download from the TSP library.
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Overview
The document outlines a Technical Support Package from NASA regarding the software developed for identifying trends in Deep Space Network (DSN) monitor data, designated as NPO-42107. This software represents a significant advancement in the analysis of DSN data, which is crucial for both manned and unmanned space missions.
The primary function of the software is to analyze DSN monitor data for changes in critical parameters. It employs proven numerical techniques to detect statistically significant trends and then translates these numeric results into a symbolic form. This symbolic characterization allows for integration with existing model-based reasoning tools, enhancing the interpretability and usability of the data.
Historically, trend analysis in DSN data relied on manual plotting and visual inspection, which was time-consuming and prone to human error. The introduction of this software marks a substantial improvement, as it automates the process and provides a more reliable means of trend detection. The software has been tested extensively, analyzing thousands of hours of real-time and archived monitor data, with results verified by experts in the field.
The software is designed to be versatile, running on various platforms, including SUN, HP, Intel, and Apple MACs, as well as flight processors. It can be distributed in both source code and binary formats and requires a LISP compiler for operation. Its memory requirements are flexible, depending on the applications it supports, and it is implemented as a library package that integrates into different environments.
This capability is particularly valuable for the Common Automation Engine (CAE) at Goldstone, where it is currently in 24/7 use in a shadow mode. The algorithms used in the software are domain-independent, making them applicable to a wide range of different applications beyond just DSN data analysis.
In summary, the document highlights a groundbreaking software tool that enhances the analysis of DSN monitor data, improving the efficiency and accuracy of trend detection in space exploration. This innovation not only streamlines data analysis but also supports the broader goals of NASA's aerospace-related developments, with potential applications in various scientific and commercial fields.
