A Robust Dynamic Direction Finding Method Based on Minimum Gap Array

An array and gap technology, applied in the field of dynamic direction finding based on the quantum group search mechanism, can solve problems such as convergence speed and convergence accuracy that are difficult for classical intelligent computing methods

Active Publication Date: 2020-04-07
HARBIN ENG UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the impact noise environment, it is difficult for classical intelligent computing methods to get rid of the contradictory constraints between convergence speed and convergence accuracy. New intelligent computing methods need to be designed to solve the robust dynamic direction finding problem in the impact noise environment

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  • A Robust Dynamic Direction Finding Method Based on Minimum Gap Array
  • A Robust Dynamic Direction Finding Method Based on Minimum Gap Array
  • A Robust Dynamic Direction Finding Method Based on Minimum Gap Array

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Embodiment Construction

[0031] The following examples illustrate the present invention further.

[0032] Step 1, set the minimum gap array. The minimum gap array is composed of M isotropic array elements, and the distance between the mth array element and the first array element in the array is set to d m (m=1,2,...,M), where d 1 =02 M , while the set Ω={d m -d z |m,z=1,2,...,M,m>z} is a discontinuous natural number set, and the discontinuous points in this set are the least, and the array element spacing is set to an integer multiple of half wavelength. Assuming that there are P narrow-band point sources in the far field of the array incident by a plane wave with a wavelength of λ, the snapshot data received by the non-uniform special array can be expressed as x(k)=A(θ)s(k)+n(k), Where A(θ)=[a(θ 1 ), a(θ 2 ),...,a(θ P )] is the M×P dimensional signal steering vector, where the pth steering vector is θ=(θ 1 ,θ 2 ,...,θ P ) is the source azimuth vector, x(k)=[x 1 (k),x 2 (k),...,x M (k)...

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Abstract

The present invention provides a robust dynamic direction finding method based on minimum clearance array. The method is carried out through the following steps: first, setting a minimum clearance array; second, initializing the search space; third, defining all members as finders and wanderers before evolution and according to the evolution rules of finders and the wanderers, evolving the searching steps and the quantum positions; fourth, calculating the adaptability of the ith member wherein the member chooses the quantum positions through the use of a greedy strategy; recording the quantum position corresponding to the adaptability function maximum as the global optimal quantum position; fifth, determining whether the maximum number of iterations is reached or not; sixth, performing (k+1)th snapshot sampling; seven, determining whether the maximum number of snapshot sampling is reached or not; and eight, mapping the global optimal quantum position obtained by each snapshot sampling to the global optimal position, or rather, the dynamic target direction value in need of tracking. According to the method of the invention, based on the minimum clearance array and the weighted norm covariance updating rule, a dynamic direction finding method of a quantum group searching mechanism is designed, and a robust dynamic direction finding method is obtained.

Description

technical field [0001] The invention relates to a dynamic direction finding method, in particular to a dynamic direction finding method based on a quantum group search mechanism in an impact noise environment. Background technique [0002] Direction finding, also known as spatial spectrum estimation or DOA (Direction of Arrival) estimation, is an important research field of array signal processing and has a wide range of applications in satellite communication systems, sound source tracking, spectrum estimation, and communications. After continuous research and development, the development of DOA direction-finding theory based on the Gaussian model assumption has matured. However, many random signals and noises encountered in practical applications are not Gaussian distributed, and these signals have significant peaks. And all can be described by SαS (SymmetricαStable) process with different characteristic index α, such as atmospheric lightning noise, instantaneous spike voi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S3/14G01S3/74
CPCG01S3/14G01S3/74
Inventor 高洪元陈梦晗张晓桐杜亚男刁鸣白永珍苏雪
Owner HARBIN ENG UNIV
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