  We study the problem of jointly sparse support recovery with 1-bit
compressive measurements in a sensor network. Sensors are assumed to observe
sparse signals having the same but unknown sparse support. Each sensor
quantizes its measurement vector element-wise to 1-bit and transmits the
quantized observations to a fusion center. We develop a computationally
tractable support recovery algorithm which minimizes a cost function defined in
terms of the likelihood function and the $l_{1,\infty}$ norm. We observe that
even with noisy 1-bit measurements, jointly sparse support can be recovered
accurately with multiple sensors each collecting only a small number of
measurements.
