Noisy distributed sensing in the Bayesian regime
Sabine Wölk, Pavel Sekatski, Wolfgang Dür · elib (German Aerospace Center) · 2020
AbstractWe consider non-local sensing of scalar signals with specific spatial dependence in the Bayesian regime. We design schemes that allow one to achieve optimal scaling and are immune to noise sources with a different spatial dependence than the signal. This is achieved by using a sensor array of spatially separated sensors and constructing a multi-dimensional decoherence free subspace. While in the Fisher regime with sharp prior and multiple measurements only the spectral range Δ is important, in single-shot sensing with broad prior the number of available energy levels L is crucial. We study the influence of L and Δ also in intermediate scenarios, and show that these quantities can be optimized separately in our setting. This provides us with a flexible scheme that can be adapted to different situations, and is by construction insensitive to given noise sources.