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Space-Time-Frequency Orientation Estimation Method Based on Joint Diagonalization of Jacobian Rotation

A technology of joint diagonalization and azimuth estimation, applied in systems for determining direction or offset, direction finders using ultrasonic/sonic/infrasonic waves, etc., which can solve the problem of space-time-frequency distribution matrix azimuth estimation only using a single one.

Active Publication Date: 2018-01-12
HEILONGJIANG INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that most of the existing azimuth estimation methods only use two-dimensional statistical information in the space-time domain, but do not use the frequency domain information of the source and most of the methods for azimuth estimation using the space-time-frequency distribution matrix only Using the problem of a single time-frequency distribution point information, a space-time-frequency orientation estimation method based on Jacobi rotation joint diagonalization is proposed

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  • Space-Time-Frequency Orientation Estimation Method Based on Joint Diagonalization of Jacobian Rotation
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  • Space-Time-Frequency Orientation Estimation Method Based on Joint Diagonalization of Jacobian Rotation

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specific Embodiment approach 1

[0046] Specific implementation mode 1: The space-time-frequency orientation estimation method based on Jacobian rotation joint diagonalization in this implementation mode is specifically prepared according to the following steps:

[0047] Step 1. According to the array received signal X(t), construct the space-time-frequency distribution matrix D of the array received signal XX (t,f);

[0048] Among them, t represents the time variable, f represents the frequency variable, and the subscript X represents the array receiving signal, and the expression form using two subscripts X is the space-time-frequency distribution matrix D XX The expression (2) of (t, f) is related, and the conjugate X of the signal X and X is used in the expression (2). * Two sets of data, where X * can also be obtained from X, so use D XX (t, f) represents the space-time-frequency distribution matrix

[0049]

[0050] x * ( ) represents the complex conjugate matrix of X( ); l is an intermediate va...

specific Embodiment approach 2

[0083] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in step 1, according to the array received signal X(t), the space-time-frequency distribution matrix D of the array received signal is constructed XX (t, f) is specifically:

[0084] Step one, such as figure 1 As shown in , it is assumed that there is a uniform linear array in space, the number of array elements is N, and the distance between array elements is d; there are M narrowband source signals in the far field of the uniform linear array, and the incident angle of the mth narrowband signal source is θ m , then the steering vector a(θ m )for:

[0085]

[0086] Among them, f m Indicates the frequency of the mth narrowband signal, c indicates the spatial signal propagation velocity, [ ] T Represents the matrix transpose, j represents the imaginary unit; m=1,...,M; e is a natural constant; M is the total number of narrowband source signals;

[0087] Thus, the a...

specific Embodiment approach 3

[0098] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the coordinates corresponding to the coordinate index numbers extracted in step 3 are (p, p), (p, q), (q, p) and (q,q); elements according to coordinates (p,p) elements of coordinates (p,q) elements of coordinates (q,p) and elements with coordinates (q,q) Specifically:

[0099] Formula (7) is the kth space-time-frequency distribution matrix D XX (t k ,f k ), let the matrix D XX (t k ,f k ) elements with the symbol a k to represent, then the element in row p and column p in the matrix is (that is, the elements of coordinates (p,p) are ), the elements in row p and column q are expressed as (that is, the elements of the coordinates (p,q) are ), the elements in the qth row and the pth column are expressed as (that is, the elements of the coordinates (q,p) are ), the elements in the qth row and the qth column are expressed as (that is, the elements of the coordinates...

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Abstract

The invention relates to a spatial time-frequency DOA estimation method based on Jacobi rotation joint diagonalization, belongs to spatial time-frequency DOA estimation methods, and aims at solving the problem that only single time-frequency distribution information but not frequency domain information of an information source is utilized in a present DOA estimation method. The method comprises the steps that 1) a spatial time-frequency distribution matrix of array reception signals is constructed; 2) k different spatial time-frequency distribution matrix systems of array reception signals are obtained; 3) elements are extracted; 4) a vector of a kth time-frequency point is constructed; 5) a new matrix Re(GGH) is obtained; 6) a characteristic vector v is obtained; 7) the angles theta and phi of Jacobi rotation are obtained; 8) a new matrix system is constructed; 9) D'XX (tk, fk) is obtained; 10) an array flow type matrix A (theta) is obtained; 11) a joint characteristic value is extracted; 12) an array output power is obtained; and 13) direction of arrival waves of a sound source is determined. The method is applied to the field of spatial time-frequency DOA estimation.

Description

technical field [0001] The invention relates to a space-time-frequency orientation estimation method, in particular to a space-time-frequency orientation estimation method based on Jacobian rotation joint diagonalization. Background technique [0002] Space source orientation estimation (Direction Of Arrival: DOA) is an important branch of array signal processing technology research, widely used in radar, sonar, medical imaging and other fields of national economy and national defense construction. After nearly 50 years of development, many researchers have proposed many classic orientation estimation methods, including maximum entropy spectrum method (Maximum Entropy: ME), maximum likelihood method (Maximum Likelihood: ML) and characteristic subspace method (H. Krim and M. Viberg. Two decades of array signal processing research. IEEE signal processing magazine. 1996,13(4):67-94.). With the deepening of array signal processing technology research, the orientation estimation...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S3/802
CPCG01S3/802
Inventor 宋海岩秦进平刁鸣杨昌益高洪元刘伯胜时洁
Owner HEILONGJIANG INST OF TECH