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

A technology of orientation estimation and residual error, applied in direction finders using radio waves, reflection/reradiation of radio waves, directional multi-channel systems using radio waves, etc., can solve DOA estimation freedom loss, redundancy, etc. question

Inactive Publication Date: 2019-08-16
CHENGDU UNIVERSITY OF TECHNOLOGY
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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

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

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[0111] The present invention will be further described below in conjunction with drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not as limitations of the present invention.

[0112] Such as figure 1 as shown, figure 1 It is a coprime transmitting and receiving array structure, and a black solid triangle represents a physical array element. The dual-base coprime MIMO array DOA and DOD joint estimation algorithm based on the cascaded residual neural network of the present embodiment specifically includes the following steps:

[0113] (1) Construct a coprime emission array,

[0114] The emission coordinates of the array elements of a sub-array are A 1 ={A 1 i |i=0,Qd,...(P-1)Qd};

[0115] The emission coordinates of the array elements of another sub-array are A 2 ={A 2 i |i=Pd,2Pd,...(2Q-1)Pd};

[0116] Among them, P and Q are mutually prime relationship, Q

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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 ...

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Application Information

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IPC IPC(8): G01S3/74G01S13/02
CPCG01S3/74G01S13/02
Inventor 贾勇郭勇肖钧友钟晓玲晏超宋瑞源陈胜亿王刚胡月杨
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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