Null broadening method for controlling sidelobe based on sparsity constraint

Abstract

The invention relates to a null broadening method for controlling sidelobe based on a sparsity constraint, mainly directed to desired signal mismatch and interference disturbance. The null broadeningmethod comprises steps of acquiring a covariance matrix of data received by an antenna array, extending and reconstructing an interference interval known in advance by using a Capon spectrum to obtaina covariance matrix of interference plus noise; obtaining a covariance matrix of interference by conducting steering vector integral on the extended interference interval; obtaining feature vectors corresponding to large feature values by a feature space method to construct a linear constraint condition; constructing a weight vector solution formula to solve the weight vector. The method of the invention has good robustness to the desired signal mismatch and achieves null broadening, and has better null depth and lower sidelobe.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a zero trap widening method for controlling side lobes based on sparse constraints. Background technique [0002] Array signal processing technology has been widely used in many fields such as radar and communication. Its biggest advantage is that it can perform beamforming in the direction of interest to increase the energy of the signal, and form a gain null in the interference direction. Traditional adaptive beamforming algorithms are mostly proposed for ideal conditions. However, the actual environment is very complex and there are various errors, especially when the interference direction shakes and the beam pointing deviates from the desired signal direction. The forming algorithm not only cannot effectively suppress the interference, but also produces nulls in the direction of the desired signal, which seriously reduces the output SINR. [0003] Aiming at the pr...

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Problems solved by technology

Among them, the diagonal loading algorithm will make the zero-sink of the pattern shallower and the loading amount is difficult to control, the linear constraint method will consume too many degrees of freedom of the array, the beamforming method based on the feature space is sensitive to the dimension of the signal subspace, and the covariance-based Robust beamforming algorithms for matrix reconstruction and steering vector estimation have good robustness to steering vector mismatches, but shallow null-trap depth

For the fluctuation of the interference direction caused by the vibration of the antenna platform or the fast movement of the interference, there is currently a CMT (covariance matrix weighted) algorithm, but the CMT algorithm will lead to a shallower null depth while widening the null.

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