Ambient noise correlation-based imaging with moving sensors

Fink, Mathias; Garnier, Josselin

doi:10.3934/ipi.2017022

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…When the observer is moving, the autocorrelation function of the recorded signal depends in a complicated and interesting way of the environment. In [10] the situation in which a receiver is moving along a circular trajectory is addressed. It is shown that the autocorrelation function of the recorded signal is related to the matrix of Green's function between pairs of points along the trajectory, more exactly to a diagonal band of this matrix whose thickness is determined by the velocity of the receiver.…”

Section: Introductionmentioning

confidence: 99%

How a moving passive observer can perceive its environment ? The Unruh effect revisited

Fink

Garnier

2020

Wave Motion

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We consider a point-like observer that moves in a medium illuminated by noise sources with Lorentz-invariant spectrum. We show that the autocorrelation function of the signal recorded by the observer allows it to perceive its environment. More precisely, we consider an observer with constant acceleration (along a Rindler trajectory) and we exploit the recent work on the emergence of the Green's function from the cross correlation of signals transmitted by noise sources. First we recover the result that the signal recorded by the observer has a constant Wigner transform, i.e. a constant local spectrum, when the medium is homogeneous (this is the classical analogue of the Unruh effect). We complete that result by showing that the Rindler trajectory is the only straight-line trajectory that satisfies this property. We also show that, in the presence of an obstacle in the form of an infinite perfect mirror, the Wigner transform is perturbed when the observer comes into the neighborhood of the obstacle. The perturbation makes it possible for the observer to determine its position relative to the obstacle once the entire trajectory has been traversed.

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Section: Introductionmentioning

confidence: 99%