Large Spacing Array with Offset Phase Center Elements for Highly Integrated Applications

A scanning and uniform array architecture with large spacing,low complexity and high scalability is presented for high integration massive array applications.It is constructed by offset phase center elements arranged in a uniform and regular way,but its spacing can be larger than that of traditional arrays.An ideal model of the offset phase center element is established and its far-field distribution is derived.To suppress grating lobes,the phase center of any element is designed to be movable without changing its physical position.Using genetic algorithm(GA),a new constraint condition limiting the number of phase center changes is proposed to solve the objective function of the minimum values of grating lobes(GLs)and side lobes(SLs).It is shown that the optimal results can be achieved by two changes of phase centers.A multimode circular patch is developed and designed,and characteristics of the offset phase center are analyzed and verified.A prototype array of 12×12 offset phase center elements is implemented based on multi-mode circular patches.Full wave simulation results of radiation patterns show that the level of grating lobes is suppressed at least 7dB with 1.12λ spacing,while the scanning angle is 20°.

Low complexity DOA estimation for massive UCA with single snapshot

In this paper, a low complexity direction of arrival(DOA) estimation method for massive uniform circular array(UCA) with single snapshot is proposed.Firstly, the coarse DOAs are estimated by finding the peaks from the circular convolution between a fixed coefficient vector and the received data vector.Thereafter, in order to refine coarse DOA estimates, we reconstruct the direction matrix based on the coarse DOA estimations and take the first order Taylor expansion with DOA estimation offsets into account.Finally, the refined estimations are obtained by compensating the offsets, which are obtained via least squares(LS) without any complex searches.In addition, the refinement can be iteratively implemented to enhance the estimation results.Compared to the offset search method, the proposed method achieves a better estimation performance while requiring lower complexity.Numerical simulations are presented to demonstrate the effectiveness of the proposed method.