For the restrictions of emitter identification based on single sensor, simply improving the performance of single surveillance platform no longer meets the practical demands in low signal-to-noise ratio(SNR). Thus an algorithm of emitter identification of multisensory fusion based on collaborative representation and Boosting is proposed. By virtue of redundancy and complementarity of multisensory data information, feature extraction is implemented with time-frequency analysis, and multi-branch residuals are obtained through collaborative representation-based classifiers. The minimum classification residuals are acquired according to the weights in the Boosting training phase, in consequence the decision-level fusion identification is implemented. Simulation results show the effectiveness of the proposed algorithm, and show more robustness to noise when the signal-to-noise ratio is relatively low. Meanwhile, the proposed framework is easy to be conducted.