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Single-bit spatial spectrum estimation method based on logistic regression

A technology of spatial spectrum estimation and logistic regression, applied in the field of logistic regression, can solve problems such as large amount of calculation and poor accuracy, and achieve the effect of low requirements, simplified design, and good angle estimation accuracy.

Inactive Publication Date: 2017-05-31
HARBIN INST OF TECH
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to solve the problem that the traditional spatial spectrum estimation algorithm not only has a large amount of calculation, but also has poor precision in the case of single-bit extreme quantization and ultra-large-scale antenna arrays

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  • Single-bit spatial spectrum estimation method based on logistic regression
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  • Single-bit spatial spectrum estimation method based on logistic regression

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specific Embodiment approach 1

[0058] Specific implementation mode one: see figure 1 Describe this embodiment mode, a kind of single-bit spatial spectrum estimation method based on logistic regression described in this embodiment mode, this method comprises the following steps:

[0059] Step 1: Construct a sample model according to the single-bit received data;

[0060]Step 2: For the input and output of the constructed sample model, the logistic regression algorithm is used to establish the convex optimization target of the classification coefficient, and the gradient descent method is used to iteratively update the convex optimization target to obtain the classification coefficient vector t, and the t=[t 1 ,t 2 ,...,t i ,...,t 2m ] T ;

[0061] Step 3: According to the classification coefficient vector t, and the following formula 1:

[0062] S i = t i +j×t i+m (Formula 1);

[0063] Obtain the spatial spectrum S=[S 1 ,S 2 ,...,S m ] T , so as to complete the estimation of the spatial spectr...

specific Embodiment approach 2

[0075] Specific implementation mode two: see figure 1 Describe this embodiment. The difference between this embodiment and the single-bit spatial spectrum estimation method based on logistic regression described in the first embodiment is that the specific process of constructing a sample model according to the single-bit received data in the first step for:

[0076] Step one, for the original sample model:

[0077]

[0078] Perform sparse representation to obtain the original sample model after sparse representation:

[0079] x=FS (Formula 3),

[0080] Steps 1 and 2, perform single-bit quantization on the original sample model after sparse representation, and obtain the model after single-bit quantization:

[0081]

[0082] In step 13, the single-bit quantized model is expressed in the real number domain as,

[0083] q=sign(Φt+e′) (Formula 5),

[0084] The single-bit quantized model is a constructed sample model in the real number domain,

[0085] in,

[0086] x∈...

specific Embodiment approach 3

[0105] Specific embodiment three: the difference between this embodiment and the single-bit spatial spectrum estimation method based on logistic regression described in the second specific embodiment is that in the second step, the output of the constructed sample model is the observation vector q, and the constructed The input to the sample model is the row of the flow pattern matrix Φ.

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Abstract

A single-bit spatial spectrum estimation method based on logistic regression relates to a spatial spectrum estimation field in array signal processing and a logistic regression field in artificial intelligence. The single-bit spatial spectrum estimation method settles problems of high calculation amount and relatively low precision in a traditional spatial spectrum algorithm on the condition of single-bit extreme quantification and ultralarge-scale antenna array. According to the method of the invention, single-bit received data are modeled for obtaining a sample model, and furthermore an observation model is converted to a real number domain for facilitating subsequent processing. After modeling, a spatial spectrum is used as a coefficient of a linear classifier; a flow pattern is used as an input sample; an array observation output is used as the output which corresponds with the input sample; and spatial spectrum estimation is converted to a linear classification problem. According to the algorithm of the invention, the linear classification problem is solved by means of a logistic regression, thereby obtaining a classification coefficient and a spatial spectrum which is generated correspondingly with an array input signal. The single-bit spatial spectrum estimation method is mainly used for spatial spectrum estimation.

Description

technical field [0001] The invention relates to the field of spatial spectrum estimation in array signal processing and the field of logistic regression in artificial intelligence. Background technique [0002] In the fields of radar, communication, sonar, meteorology, etc., array signal processing has extensive and important applications. In array signal processing, spatial spectrum estimation is the basis for beamforming and other array signal processing algorithms. In the research of 5G mobile communication, massive MIMO has become a hot spot of attention. In the case of very large-scale antenna arrays, low-complexity and high-precision spatial spectrum estimation is the basis for massive MIMO to perform other algorithm processing. When a real receiver performs direction finding processing, quantization processing will reduce the accuracy of the algorithm. Consider the single-bit extreme quantization situation, that is, each array element only retains the sign informat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S3/14
CPCG01S3/143
Inventor 高玉龙胡德顺陈艳平许康马永奎
Owner HARBIN INST OF TECH
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