Computational Analysis of EM Wave Diffraction in Highly Irregular and Random Media for Remote Sensing Applications
DOI:
https://doi.org/10.31838/NJAP/07.02.36Keywords:
Electromagnetic waves, diffraction, random media, remote sensing, FDTD, Monte Carlo simulation, wave propagation modelingAbstract
When riding on EM waves through jagged, complicated environments like tangled tree branches, disintegrating urban blocks, or deep drifts of snow, these waves happily mix, scramble, and distort the messages in ways that bewilder most remote sensors. To help overcome this problem, we created a new toolkit for computer use that models wave behaviors with greater fidelity in these diverse, unpredictable scenes. Recall these scenes, with the advice of fluids in the analogy above, are even more complex than simple balls striking someone's head. Starting with commercially standard and known techniques like Finite-Difference Time-Domain (FDTD), we combined it with a Monte Carlo engine that randomly samples surface bumps and volume voids, and then tracked the effect of every scatter on the wavefront. By feeding in actual statistics of tree heights, ruin gaps, or the snow thickness, the kind of spatial randomness present in nature is recreated instead of the perfect grids that lab models usually assume. Results suggest that small changes in roughness can bend, mute, or widen the wavefront in highly original ways, which can alter the signal's phase, amplitude, and angle. We double-checked the results using simpler analytical expressions and found that our hybrid approach is as good as or better than theirs, proving its reliability. These results can immediately enhance radar reading, improve accuracy, and stabilize ground-penetrating systems in uncertain scenes. They will also form part of the foundation for future real-time processors that will adapt on the fly to whatever terrain's on display.
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