Lossy bosonic channels play an important role in a number of quantum information tasks, since they well approximate thermal dissipation in an experiment. Here, we characterize their metrological power in the idler-free and entanglement-assisted cases, using respectively single- and two-mode Gaussian states as probes. In the problem of estimating the loss parameter, we study the power-constrained quantum Fisher information (QFI) for generic temperature and loss parameter regimes, showing qualitative behaviours of the optimal probes. We show semi-analytically that the two-mode squeezed-vacuum state optimizes the QFI for any value of the loss parameter and temperature. We discuss the optimization of the {\it total} QFI, where the number of probes is allowed to vary by keeping the total power constrained. In this context, we elucidate the role of the ``shadow-effect'', or passive signature, for reaching a quantum advantage. Finally, we discuss the implications of our results for the quantum illumination and quantum reading protocols.

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In this study, spatial separation of the radar transmitter and receiver units is considered, as a means of reducing the masking effect in noise radars. A bistatic radar system is constructed, with emphasis on a lightweight transmitter unit that can be mounted on a commercial Unmanned Aerial Vehicle (UAV). The system uses pseudo-random noise, generated digitally at the receiver and transmitter units. Correlation losses, due to nonlinearities in the transmitter and receiver units, are measured to 0.1 dB. This study shows that by separating the transmitter and receiver unit the masking effect is significantly reduced, compared to a monostatic setup. This reduction is enough for the system to detect a slow flying UAV. Thus, bistatic separation should be considered as a practical tool to reduce the masking effect. By processing clutter with an extended CLEAN algorithm, the correlation noise floor is further suppressed.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.