Sparse planar formation optimization method based on spatial gain

Abstract

The invention provides a sparse planar formation optimization method based on spatial gain. The method comprises the following steps that an array is initialized, a coordinate matrix formed by coordinate vectors of array elements is obtained, and spatial gain corresponding to the coordinate matrix is calculated; iteration parameters are set; iterative optimization is carried out on the initialized array, 5-N array elements in the array are sequentially disturbed in each iterative optimization, new coordinates of the array elements generated after each disturbance is finished are judged, the new coordinates meeting the judge conditions replace old coordinates of the array elements, the coordinate matrix is renewed, the interactive process is repeated until a set interactive total number is reached, an array manifold is received, and a coordinate matrix (x0,y0) which is renewed after iterative optimization is used as an array position of a sparse planar matrix. When an angle-directed face array is needed, a cost function can comprehensively evaluate side-lobe level and main-lobe beam width, and by means of the method, the sparse planar array with narrow main-lobe beam width and low side-lobe level can be generated.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to an optimization method of a sparse plane array manifold. Background technique [0002] Array signal processing technology is widely used in radar, communication, sonar, navigation and radio astronomy and other fields. One of the important factors affecting the array performance is its formation. By optimizing the array formation, the array DOA estimation performance and adaptive anti-jamming ability can be improved. [0003] For methods and research on optimizing the performance of direction of arrival (DOA) estimation through array geometry, the existing research results include: [0004] In the article "Optimizing Antenna Array Geometry for Interference Suppression" published in IEEE Transactions on Antenna and Propagation in 2007, P.J. Bevelacqua proposed to obtain an optimized array manifold through simulated annealing algorithm. However, the optimization object o...

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Problems solved by technology

However, this method takes the minimum side lobe as the constraint condition and cannot take into account the beam width of the main lobe.

[0007] Most of the current formation optimization methods use the minimum sidelobe level ratio or the main lobe beam width as the cost function, which is limited to fixed formations rather than sparse arrays with better adaptive performance. The optimized cost function cannot take into account the main and side lobes The two important parameters of peak ratio and main lobe beam width cannot be comprehensively evaluated at the same time to obtain formation optimization

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