Direction Finding Based on Cuckoo Search Algorithm in the Strong Impulse Noise

In order to resolve the difficulty of direction finding in the strong impulse noise, based on maximum likelihood (ML) function and infinite norm, a infinite-norm maximum likelihood (IML) approach is proposed. The proposed approach can reduce the impact of impulse noise and improve the performance of the original ML algorithm significantly. In order to obtain the global optimal solution of the proposed IML approach, cuckoo search (CS) algorithm is applied to solve the objective function of IML, which can enhance the capability of searching and improve the relationship between exploration and exploitation, and a novel direction finding approach called CSIML is proposed. The proposed CS-IML offers a promising alternative to the conventional approaches in impulse noise, and its merits lie in the fact that it avoids stagnation and has the robust performance. Monte-Carlo simulations have shown that the proposed CS-IML has high success rate of estimation and the capability to find coherent and independent signal sources in the strong impulse noise. Keywords—direction finding; impulse noise; infinite norm; maximum likelihood algorithm; cuckoo search