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Compressed sensing is a novel technology on signal information processing. It offers a new wide-band spectrum detection scheme in cognitive radio. A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative detection scheme based on sequential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named SSAMP is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required measurements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effectiveness of the estimation and detection performance of our cooperative scheme. Meanwhile, the performance of SSAMP algorithm and SOMP algorithm is evaluated by MSE and detection time.