The influence of noise and the effects of ill-conditioning of the measurement
matrix on the effectiveness of $\ell_1$-based recovery of sparse signals in
high dimensional spaces is investigated. Even for moderately ill-conditioned
systems it is found that such a method performs much worse than for random
matrix based problems that are often presented. On the other hand, when
considering different noise levels for a fixed condition number, the relative
accuracy remains constant.