DOA Estimation of Far-Field Sources by Exploiting Second Order Statistics of Bi-level Nested Arrays Using Biological Flower Pollination Algorithm

The immense degree of freedom (DOF), high array aperture, non-uniform linear arrays, and reduced mutual coupling have developed interest in the estimations of the direction of arrival (DOA). Due to complex previous structures, this paper investigates the bi-level sparse linear nested array (SNA) concepts to discuss element spacing and different ranges on uniform DOF. Then features of flower pollination algorithm is applied to the proposed two-level SNA to generalize and enhance the proposed structure further. In order to boost DOF, it is also investigated local and global minima of highly non-linear functions. The proposed technique for quantifying the DOA is reviewed analytically using evaluation parameters like cumulative distributive function, accuracy, root mean square error, and robustness against noise and snapshots. The simulation findings prove its validation with the analytical model and target the accuracy with fewer separations and the minimum number of physical sensors in relation to particle swarm optimization. Moreover, the strength of the proposed study further validated by comparing with Cramer Rao Bound for minimum variance which shows that the FPA outperforms.