An Enumerative NonLinear Programming approach to direction finding with a general spatially spread electromagnetic vector sensor array

In this paper, the geometry of a general spatially spread electromagnetic vector sensor (SSEMVS) array is firstly presented. This array has a special cross product structure of the array manifold vector. The gg parameters are then defined to uniquely characterize the cross product. With gg parameters, the identifiability analysis of the cross product vector based direction finding algorithms of the general SSEMVS array is carried out. The analysis shows that the array has to be carefully designed to avoid ambiguity directions. Finally, a cross-product based Enumerative NonLinear Programming (ENLP) algorithm for the direction of arrival (DOA) estimation with the general SSEMVS array is proposed. The algorithm finds the optimum estimation of DOA in least square sense. Extensive simulations show that the proposed algorithm outperforms existing algorithms, and can approach the CRB effectively.

► A general SSEMVS array geometry is proposed.
► The gg parameters uniquely characterize the cross product of the array manifold.
► Identification analysis helps the design of the array.
► An estimation algorithm for the general SSEMVS array geometry is proposed.