Signal tracking beyond the time resolution of an atomic sensor by Kalman filtering

TL;DR

This paper demonstrates that Kalman filtering enables atomic sensors to accurately track rapidly changing signals beyond their intrinsic time resolution, improving sensing capabilities.

Contribution

The study introduces a Kalman filtering approach for atomic sensors that surpasses traditional time resolution limits, applicable to both known and partially-known non-Gaussian signals.

Findings

Kalman filter accurately estimates stochastic signals in atomic sensors.

Waveform details are recoverable beyond the sensor's intrinsic time resolution.

Method improves sensitivity-time resolution trade-off in coherent sensing.

Abstract

We study causal waveform estimation (tracking) of time-varying signals in a paradigmatic atomic sensor, an alkali vapor monitored by Faraday rotation probing. We use Kalman filtering, which optimally tracks known linear Gaussian stochastic processes, to estimate stochastic input signals that we generate by optical pumping. Comparing the known input to the estimates, we confirm the accuracy of the atomic statistical model and the reliability of the Kalman filter, allowing recovery of waveform details far briefer than the sensor's intrinsic time resolution. With proper filter choice, we obtain similar benefits when tracking partially-known and non-Gaussian signal processes, as are found in most practical sensing applications. The method evades the trade-off between sensitivity and time resolution in coherent sensing.