Compressive noise radar imaging involves the inversion of a linear system using l1-based sparsity constraints. This linear system is characterized by the circulant system matrix generated by the transmit waveform. The imaging problem is solved using convex optimization. The characterization of imaging performance in the presence of additive noise and other random perturbations remains an important open problem. Computational studies designed to be generalizable suggest that uncertainties related to multiplicative noise adversely affect detection performance. Multiplicative noise occurs when the recorded transmit waveform is an inaccurate version of the actual transmitted signal. The actual transmit signal leaving the antenna is treated as the signal. If the recorded version is considered as a noisy version of this signal, then, generalizable numerical experiments show that the signal to noise ratio of the recorded signal should be greater than about 35 dB for accurate signal recovery.