**1.6 Conclusion**

From the above analysis, we can conclude as follows. (I) The proposed algorithm is correct and effective. (II) The norm constraint can improve the robustness of the Capon beamformer. Especially, the equality constraint has the preferable improvement to overcome the steering vector mismatch, and also has good robustness for the samples number. (III) When the norm constraint parameter is selected in the allowable bound, NECCB has the best performance, namely the optimal negative loading has the optimal improvement, this is because that the norm equality constraint is stronger than the norm inequality constraint.
