The Intelligence Advanced Research Projects Activity (IARPA) seeks information regarding innovative signal processing algorithms and sensing approaches used to characterize the D-region of the ionosphere (60–90 km). Responses are due by 5:00 p.m. Eastern on June 17.
Currently, the D-region is characterized by remote sensing of very low frequency (VLF) signals and comparing that propagation with modeling results. However, this approach is highly nonlinear and fits the definition of an ill-posed problem in that itis quite difficult to solve and does not yield a unique one-to-one correspondence between electron density profiles in the Earth-ionosphere waveguide and signals at the receiver(s). In the past decade, inversions of highly nonlinear, ill-posed problems have been successfully tackled with machine learning and artificial neural networks (ANNs).
While significant progress has been made in signal processing, the data used by these algorithms is obtained by sensors that are uncoordinated and loosely networked. Additionally, the uncoordinated environmental monitoring sensors used to collect data cannot provide real-time estimates of the D-region, needed to support high-accuracy detection, geolocation and classification solutions.
IARPA seeks to characterize and better understand the D-region and the interaction of this region of the ionosphere with electromagnetic (EM) anomalies caused by space weather events, lightning, manmade sources, etc. and whether the resulting D-region characterization can be used to identify, map, and track these EM anomalies. Current capabilities do not allow for the rapid parametrization of the D-region. Any methodology that could lead to this rapid parametrization is of interest as we seek to facilitate a domain awareness capability that currently does not exist.
Responses to this RFI are due no later than 5:00 p.m., Eastern Time, Monday, 17 JUN 2024.
Review the IARPA SIGINT-related RFI.
Source: SAM
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