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7 May 2019 Modular Algorithm Testbed Suite (MATS): an open architecture for automatic target recognition
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Here we discuss an open software architecture to develop, test, and evaluate machine learning algorithms for target detection and classification. This architecture, known as the Modular Algorithm Testbed Suite (MATS), aids developers by defining interfaces for various portions of the automatic target detection processing chain. There are several key advantages to this approach. First, with “plug and play” modules for detection, feature extraction, and classification, developers can mix and match different approaches and focus on particular portions of the processing chain that yield the most performance benefit. Second, since some portions of the processing chain may be more agnostic to the sensor data type than others, e.g. target features may change but the pattern classification approach is the same, MATS enables quick ATR development for similar data types. Finally, since developers can insert "black boxes" into the ATR processing chain, MATS allows for independent blind testing of algorithms without compromising intellectual property. In this paper, we will discuss the MATS architecture and review several case studies where MATS enabled rapid demonstration and transition of ATR algorithms to Navy mine countermeasure (MCM) post-mission analysis software.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Derek Kolacinski, Bradley Marchand, and Tory Cobb "Modular Algorithm Testbed Suite (MATS): an open architecture for automatic target recognition", Proc. SPIE 11015, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2019, 110150E (7 May 2019);

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