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We study the problem of jointly sparse support recovery with 1-bitcompressive measurements in a sensor network. Sensors are assumed to observesparse signals having the same but unknown sparse support. Each sensorquantizes its measurement vector element-wise to 1-bit and transmits thequantized observations to a fusion center. We develop a computationallytractable support recovery algorithm which minimizes a cost function defined interms of the likelihood function and the $l_{1,\infty}$ norm. We observe thateven with noisy 1-bit measurements, jointly sparse support can be recoveredaccurately with multiple sensors each collecting only a small number ofmeasurements.

Joint Sparsity Pattern Recovery with 1-bit Compressive Sensing in Sensor Networks