They mean signal-to-noise ratio.

Let's say you want to estimate some quantity, so you put a Gaussian prior over it. You then have a bunch of different sensors subject to white (Gaussian) noise, and some link-function connecting the latent value to the mean of the sensor reading with fixed precision. Gaussian with Gaussian is a conjugate prior-likelihood pair, so you can calculate the posterior parameters in closed form.

Your posterior mean is going to be a precision-weighted average of your sample mean and your prior mean, while your posterior precision (signal-to-noise) is going to be proportional to the number of sensors you used.