Direct positioning method under unknown mutual coupling condition

Abstract

The invention provides a direct positioning method under an unknown mutual coupling condition, and the method comprises the steps: constructing a non-uniform grid, reducing the number of matrix dimensions in combination with singular value decomposition to reduce the calculation amount, carrying out the iteration of grid refinement and unknown mutual coupling estimation through an expectation maximization algorithm, updating the coordinates of grid points, and correcting the unknown mutual coupling. And array flow pattern errors caused by unknown mutual coupling are eliminated. According to the method, the calculation complexity is effectively reduced, and the problem of false peaks caused by strong correlation between lattice points is solved; through establishment of an off-grid model and updating of grid errors, the problem that the position of a radiation source is not on a grid point is effectively solved, and the positioning precision is improved; a signal subspace is used for replacing a received signal to reduce the matrix dimension so as to reduce the calculation amount; the band-shaped symmetric Toeplitz structure and the expectation maximization algorithm of the uniform linear array mutual coupling matrix are effectively utilized, unknown mutual coupling is updated and corrected, and the positioning precision when mutual coupling exists in the receiving array is improved.

Description

technical field [0001] The invention relates to the technical field of radiation source positioning, in particular to a direct positioning method with unknown mutual coupling. Background technique [0002] Compared with super-resolution direct positioning methods such as the multiple signal classification MUSIC algorithm and the minimum variance distortion-free response MVDR algorithm, the direct positioning algorithm based on sparse Bayesian learning has better robustness at low signal-to-noise ratios. However, in practical applications, the excellent performance of the direct localization algorithm based on sparse Bayesian learning may be greatly degraded due to the unknown mutual coupling between the antenna elements, moreover, in the direct localization, the two-dimensional grid leads to computational complexity The position of the radiation source may not be on the grid point and the strong correlation between the grid points will also reduce the accuracy of direct posi...

AI Technical Summary

Problems solved by technology

However, in practical applications, the excellent performance of the direct localization algorithm based on sparse Bayesian learning may be greatly degraded due to the unknown mutual coupling between the antenna elements, moreover, in the direct localization, the two-dimensional grid leads to computational complexity Increased, and the position of the radiation source may not be on the grid point and the strong correlation between the grid points will also reduce the accuracy of direct positioning

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