Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An acoustic "particle velocity sensor" (a.k.a. a geophone) exhibits a gain-response with a cosine-like directivity. The particle velocity sensor may be realized in hardware by two "pressure sensors" (of isotropic directivity) displaced in space, and by computing the spatial first-order finite difference beten the data of the two isotropic component-sensors. As each component-sensor's data are degraded by additive noises (modeled here with much generality as stochastically distributed as "stable" (a.k.a. "alpha stable" or "$\alpha$ stable"), and not restricted to being Gaussian), the particle velocity sensor as a whole would also experience noise, the statistics of which is analytically derived here. Furthermore, beyond this particle velocity sensor involving a first-order finite difference, the work presented here also derives the composite noise statistics of higher order difference realizations of sensors of higher order directivity in their gain responses.
['Andriy Yakovych Olenko', 'Kainam Thomas Wong']
I am writing a research paper on a idea Noise Statistics of a Higher Order Directional Sensor, Realized by Computing Finite Differences Spatially Across Multiple Isotropic Sensors
[{"from": "human", "value": "I am writing a research paper on a idea Noise Statistics of a Higher Order Directional Sensor, Realized by Computing Finite Differences Spatially Across Multiple Isotropic Sensors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An acoustic \"particle velocity sensor\" (a.k.a. a geophone) exhibits a gain-response with a cosine-like directivity. The particle velocity sensor may be realized in hardware by two \"pressure sensors\" (of isotropic directivity) displaced in space, and by computing the spatial first-order finite difference beten the data of the two isotropic component-sensors. As each component-sensor's data are degraded by additive noises (modeled here with much generality as stochastically distributed as \"stable\" (a.k.a. \"alpha stable\" or \"$\\alpha$ stable\"), and not restricted to being Gaussian), the particle velocity sensor as a whole would also experience noise, the statistics of which is analytically derived here. Furthermore, beyond this particle velocity sensor involving a first-order finite difference, the work presented here also derives the composite noise statistics of higher order difference realizations of sensors of higher order directivity in their gain responses."}]