The estimation of acoustic reflection coefficients from in-room measurements using one or more sources and microphone arrays has been addressed in various ways. One particularly illustrative method is the plane wave decomposition. Peaks in this representation can be assigned to the location and strength of mirror image sources. Traditional approaches employ rigid spherical microphone arrays together with modal beamforming. Compressive sensing (CS) techniques aim at spatial undersampling by

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... ing sparsity of the sound field in some given representation. By careful design of the sensing matrix, the number of required measurements can be considerably reduced compared to Nyquist sampling and reconstruction. However, often sparsity of the problem at hand is not given and a sufficient low mutual coherence of the sensing matrix is violated, yielding CS reconstruction with low robustness. We aim at robust detection of early, discrete reflections by means of CS using rigid spherical microphone array data. We discuss the interaction of room characteristics, sensing matrix and technical measures to qualify the detection performance. The influence of practical limitations, like the number of microphones and their self-noise is investigated, as well as the overall gain of applying CS to the problem compared to traditional modal beamforming techniques.