  In a multistatic cloud radar system, receive sensors measure signals sent by
a transmit element and reflected from a target and possibly clutter, in the
presence of interference and noise. The receive sensors communicate over
non-ideal backhaul links with a fusion center, or cloud processor, where the
presence or absence of the target is determined. The backhaul architecture can
be characterized either by an orthogonal-access channel or by a non-orthogonal
multiple-access channel. Two backhaul transmission strategies are considered,
namely compress-and-forward (CF), which is well suited for the
orthogonal-access backhaul, and amplify-and-forward (AF), which leverages the
superposition property of the non-orthogonal multiple-access channel. In this
paper, the joint optimization of the sensing and backhaul communication
functions of the cloud radar system is studied. Specifically, the transmitted
waveform is jointly optimized with backhaul quantization in the case of CF
backhaul transmission and with the amplifying gains of the sensors for the AF
backhaul strategy. In both cases, the information-theoretic criterion of the
Bhattacharyya distance is adopted as a metric for the detection performance.
Algorithmic solutions based on successive convex approximation are developed
under different assumptions on the available channel state information (CSI).
Numerical results demonstrate that the proposed schemes outperform conventional
solutions that perform separate optimizations of the waveform and backhaul
operation, as well as the standard distributed detection approach.
