Publications

Increased data sharing and interoperability has created challenges in maintaining a level of trust and confidence in Department of Defense (DoD) systems. As tightly-coupled, unique, static, and rigorously validated mission processing solutions have been supplemented with newer, more dynamic, and complex counterparts, mission effectiveness has been impacted. On the one hand, newer deeper processing with more diverse data inputs can offer resilience against overconfident decisions under rapidly changing conditions. On the other hand, the multitude of diverse methods for reaching a decision may be in apparent conflict and decrease decision confidence. This has sometimes manifested itself in the presentation of simultaneous, divergent information to high-level decision makers. In some important specific instances, this has caused the operators to be less efficient in determining the best course of action. In this paper, we will describe an approach to more efficiently and effectively leverage new data sources and processing solutions, without requiring redesign of each algorithm or the system itself. We achieve this by instrumenting the processing chains with an enterprise blockchain framework. Once instrumented, we can collect, verify, and validate data processing chains by tracking data provenance using smart contracts to add dynamically calculated metadata to an immutable and distributed ledger. This noninvasive approach to verification and validation in data sharing environments has the power to improve decision confidence at larger scale than manual approaches, such as consulting individual developer subject matter experts to understand system behavior. In this paper, we will present our study of the following: 1. Types of information (i.e., knowledge) that are supplied and leveraged by decision makers and operational contextualized data processes (Figure 1) 2. Benefits to verifying data provenance, integrity, and validity within an operational processing chain 3. Our blockchain technology framework coupled with analytical techniques which leverage a verification and validation capability that could be deployed into existing DoD data-processing systems with insignificant performance and operational interference.

This paper investigates the feasibility of detecting plastic antipersonnel land mines buried in lossy, dispersive, rough soils using a stepped-frequency ultra wideband (WB) ground-penetrating radar (GPR). Realistic land mine scenarios were modeled using a two-dimensional (2D) finite difference firequency domain (FDFD) technique. Assuming normal incidence plane wave excitation, the scattered fields were generated over a large frequency bandwidth (.5 to 5 GHz) for a variety of mine-like shapes, different soil types, and multiple receiver locations. The simulation results showed that for a ground penetration sensor located just above the soil surface, the strong reflection signals received from the rough ground surface obscured the buried target's fiquency response signal. The simulated GPR WB frequency response data at each receiver location was transformed to the time domain using the fast fourier transform. Time domain processing permits high resolution measurement of target features that are invariant to the ground roughness and also that are dependent on the soil characteristics as well as the burial depth and size of the mine, Specifically, two or more characteristic timing peaks are observed in the simulation results suggesting that the ultra-wideband spectral radar response may yield particular advantages not exploited by currently employed detection systems. It is also shown that by using time-gating to remove the strong ground reflection signals, the target signals are selectively enhanced (as expected), but more surprisingly, the target frequency response signature is almost completely recovered.