Priors can make sensor information more useful maybe, but that is just knowledge that helps first limit possibilities before taking a measurement. Priors also work against you when you are trying to sense something novel that might indicate a thing you don't expect.

An aside on sparsity priors (which that article uses).. reality is actually a lot less sparse than the researcher models would have you believe. If most dimensions are not truly zero (e.g., have some small noise present) these sparsity methods fall apart. That's why you (never?) see the methods deployed in actual products.

Specifically, the support determination step usually breaks down in epsilon sparse and you also get "noise folding".