Cascaded residual neural network-based orientation estimation algorithm for double-base co-prime MIMO array

Abstract

The invention discloses a cascaded residual neural network-based DOA and DOD joint estimation algorithm for a double-base co-prime MIMO array. The method comprises the steps of estimating the number of targets and estimating orientation angles of the targets. According to the method, a data processing part in traditional DOA and DOD estimation of double-base MIMO is improved. Compared with a traditional signal processing algorithm, a deep learning method has higher timeliness and better performance in the aspects of low signal-to-noise ratio, low snapshot, large orientation angle and robustness under coherent target conditions. The deep neural network adopted in the algorithm adopts a cascaded network structure. Firstly, a signal received by the array is subjected to DFT processing and then is subjected to correlation processing; the processed signal is input to the neural network to obtain DOA information of the signal; the DOA information is used as prior information to be input to the cascaded network to obtain DOD information of the signal; and finally, the DOA and DOD matching estimation of the signal is completed.

Description

technical field [0001] The present invention relates to the technical field of DOA and DOD estimation of dual base mutual prime MIMO arrays, in particular to a dual base mutual prime MIMO array DOA and DOD joint estimation algorithm based on cascaded residual neural network. Background technique [0002] Direction of Arrival DOA estimation can determine the azimuth angle position information of multiple space targets, has high resolution, and is widely used in communication, radar, sonar, earthquake sensing and other fields. The coprime array proposed in recent years has outstanding advantages in the determination of the position of the array element and the coupling and mutual interference of adjacent array elements, and has gradually become a hot spot of attention. [0003] The multiple-transmission and multiple-reception MIMO array is mainly used to detect targets that do not have the ability to radiate signals or that do not have a stable external radiation source. The ...

AI Technical Summary

Problems solved by technology

The degrees of freedom of target DOA estimation based on MIMO arrays are determined by the number of virtual array elements in the "virtual difference synergistic array" corresponding to the "virtual and synergistic array". " and "virtual differential synergy array" have multiple virtual array elements at the same position, that is, there is a large amount of redundancy, resulting in the loss of DOA estimation degrees of freedom

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