2023 Assessment of the DEVCOM Army Research Laboratory (2024)
Chapter: Appendix D: Army Research Laboratory Technical Assessment Board Statement of Task
D
Army Research Laboratory Technical Assessment Board Statement of Task
An ad hoc committee to be named the Army Research Laboratory Technical Assessment Board (ARLTAB), to be overseen by the Laboratory Assessments Board, will be appointed to continue the function of providing annual assessments of the scientific and technical quality of the Army Research Laboratory (ARL). These assessments will include findings and recommendations related to the quality of ARL’s research, development, and analysis programs. While the primary role of the ARLTAB is to provide peer assessment, it may offer advice on related matters when requested by the ARL Director. The ARLTAB will provide assessments over a 4-year cycle. Years 1–3 will each examine ARL’s work related to three to four different technical “competencies” for which ARL is responsible, producing in each of those years an interim report that provides an assessment of a portion of ARL’s program. In year 4 the ARLTAB may produce, when requested, an interim report on selected cross-cutting aspects of ARL’s work, plus a final report that summarizes the 4-year assessment. The ARLTAB will be assisted by up to 11 separately appointed panels that will focus on particular portions of the ARL program.
E
New Multi-Modality Data-Fusion Approach
An exemplar for multi-modality electronic warfare, radar, and electro-optical/infrared (EO/IR) data-fusion approach enabled by artificial intelligence (AI) and machine learning (ML) is depicted in Figure E-1. These three traditional individual electromagnetic functions are represented by the horizontal pipelines within the dashed box. The input for each function has a different data format and different legacy processing stovepipes, where the actual physics is often approximated and its complete characterization may be lost. The red and green boxes in the research column indicate radio frequency (RF) and optical functions, respectively. As opposed to traditional approaches that fuse the outputs of each function or modality, Figure E-1 depicts the use of raw data across the entire electromagnetic spectrum (pink path) to define ontological concepts and categories (orange box) from the fundamental physics of each modality (research column) as inputs to autonomy engines (tan box). Some AI and ML engines are listed. This approach can be applied more generally to any number of functionalities and across different sensing modalities.
