论文标题
贝叶斯政权中的嘈杂传感
Noisy distributed sensing in the Bayesian regime
论文作者
论文摘要
我们考虑对标量信号的非本地传感，在贝叶斯政权中具有特定的空间依赖性。我们设计的方案使人们可以实现最佳缩放率，并且可以免疫与信号不同的空间依赖性噪声源。这是通过使用空间分离传感器的传感器阵列并构建多维抗逆性子空间来实现的。尽管在具有敏锐先验和多个测量值的Fisher制度中，仅光谱范围$δ$很重要，但单发感觉具有广泛的先验数量，可用的能量水平$ L $至关重要。我们在中间场景中研究了$ L $和$δ$的影响，并证明可以在我们的环境中分别优化这些数量。这为我们提供了一种灵活的方案，可以适应不同的情况，并且对给定的噪声源不敏感。
We 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.
