Motion linear sparse array optimization method based on underdetermined signal source Cramer-Rao bound

A technology of sparse array and optimization method, which is applied in the direction of direction determination, genetic rules, gene models, etc., can solve the problem of insufficient performance of direction of arrival estimation

Inactive Publication Date: 2020-10-23
XIDIAN UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

That is, the CRB of these arrays with regard to the direction of arrival angle under the underdetermined source is not the smallest among all the arrays that satisfy the constraints of the aperture and the number of array elements, so that the direction of arrival estimation performance of the array is not good enough

Method used

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  • Motion linear sparse array optimization method based on underdetermined signal source Cramer-Rao bound
  • Motion linear sparse array optimization method based on underdetermined signal source Cramer-Rao bound
  • Motion linear sparse array optimization method based on underdetermined signal source Cramer-Rao bound

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Embodiment 1

[0033]In the prior art, the applications of sparse arrays are mostly limited to linear sparse arrays with closed expressions, such as nested arrays and coprime arrays. These arrays have one thing in common, that is, when the aperture and the number of array elements are determined, the structure of the array is fixed immediately. When the signal environment changes and the estimation performance of the original array decreases, there are generally two methods to restore the original estimation performance. One is to increase the array aperture or the number of array elements, and improve the degree of freedom of the array from the physical structure; Mounted on a motion platform, the motion characteristics of the array are used to improve the degree of freedom of the array; however, the disadvantages of these two methods are obvious. Leaving aside whether the aperture of the array and the number of array elements can be increased infinitely, from the perspective of factors aff...

Embodiment 2

[0056] The motion linear sparse array optimization method based on the undetermined source Cramereau bound is the same as that in embodiment 1, and the Cramero bound CRB(θ) of the linear sparse array under the underdetermined source with respect to the direction of arrival angle is calculated in step 2) of the present invention , including the following steps:

[0057] (2.1): Calculate the differential array flow pattern matrix and its augmented matrix of the linear sparse array: let θ q is the direction of arrival angle of the qth signal source, q=1,...,Q, then the differential array steering vector of the qth signal source is

[0058]

[0059] Considering all signal sources, the steering vector is extended to array flow pattern matrix, then the differential array flow pattern matrix is

[0060]

[0061] The corresponding augmented matrix is e 0 defined as follows

[0062]

[0063] γ Represents the γth element of the vector, m is the difference array D c The ...

Embodiment 3

[0084] The motion linear sparse array optimization method based on the undetermined source Kramero bound is the same as that of embodiment 1-2, and the improved genetic algorithm is used in step 3) of the present invention to optimize the linear sparse array, including the following steps:

[0085] Order N p Indicates the population size, that is, the total number of individuals in the population, p s Indicates the selection probability, N c Indicates the number of crossovers, p m Indicates the mutation probability; N i Indicates the total number of iterations, μ indicates the initial CRB value, p c Indicates the probability of optimal selection; the specific steps of linear sparse array optimization based on the improved genetic algorithm are as follows:

[0086] (3.1): Initialize the first generation population P 0 : Randomly generate N p non-repetitive linear sparse arrays with CRB smaller than μ, let the current iteration number i=0.

[0087] (3.2): Enter the algori...

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Abstract

The invention discloses a design method of an optimal motion linear sparse array under an underdetermined signal source, and solves the problem of obtaining a motion sparse array with the minimum CRB(Cramer-Rao bound) under the underdetermined signal source, wherein the motion sparse array meets the requirements of aperture and array element number. The method comprises the following implementation steps: obtaining a differential array Dc of a motion linear sparse array and an array flow pattern matrix Ac of the differential array Dc; calculating CRB of the motion linear sparse array with respect to direction of arrival estimation; and optimizing the linear sparse array by using an improved genetic algorithm to obtain an optimal array structure Sopt. The invention provides a Cramer-Rao bound expression of a motion linear sparse array relative to direction-of-arrival estimation under an underdetermined signal source. A fancy competition selection method is introduced, and a preferred strategy is adopted in the crossover mutation process so that local convergence is avoided. The CRB of the obtained optimal array relative to direction-of-arrival estimation is smaller, and DOA estimation performance is improved. The optimal array structure can be easily adjusted when the signal environment changes. The method is used for high-precision DOA estimation.

Description

technical field [0001] The invention belongs to the technical field of direction-of-arrival estimation, and mainly relates to the design of a sparse array for direction-of-arrival estimation, in particular to a motion linear sparse array optimization method based on an underdetermined source Cramerau bound. It is applicable to the optimal motion linear sparse array design problem under the underdetermined source. Background technique [0002] The direction finding technology based on sparse array structure has a wide range of applications in communication, radar, sonar, satellite navigation, radio telescope and other fields. Compared with uniform arrays, sparse arrays have higher degrees of freedom and larger array apertures when the number of sensors is the same. Commonly used sparse arrays include coprime arrays, minimal redundant arrays (MRA), minimal hole arrays (MHA), and nested arrays (NA). When designing nested arrays and coprime arrays, it is generally considered t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S3/00G06F17/15G06N3/12
CPCG01S3/00G06F17/15G06N3/126
Inventor 秦国栋刘韦辰鲍丹武斌蔡晶晶刘高高李鹏
Owner XIDIAN UNIV
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