CN111983599A - Target two-dimensional DOA estimation method based on azimuth-pitch dictionary - Google Patents

Target two-dimensional DOA estimation method based on azimuth-pitch dictionary Download PDF

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CN111983599A
CN111983599A CN202010828937.3A CN202010828937A CN111983599A CN 111983599 A CN111983599 A CN 111983599A CN 202010828937 A CN202010828937 A CN 202010828937A CN 111983599 A CN111983599 A CN 111983599A
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azimuth
dictionary
echoes
pitch
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杨长生
胡丽萍
梁红
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Northwestern Polytechnical University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a target two-dimensional DOA estimation method based on an azimuth-pitch dictionary, which comprises the steps of receiving target echoes from different azimuths and pitch angles by utilizing a planar array, constructing an azimuth-pitch joint dictionary by utilizing the received target echoes, receiving linear superposition of the echoes of a target to be detected on the different azimuths and the pitch angles, detecting the target echoes, estimating the target two-dimensional DOA by utilizing the dictionary, solving the position of the maximum value in a sparse vector alpha, and converting the position into an azimuth-pitch estimation result of the target. The method has the advantages of small calculation amount and high precision, different dictionaries can be established under different interferences aiming at different environments, so that the target azimuth pitching position can be accurately detected under various environmental conditions, the real-time requirement can be met, and the calculation amount is minimized and the resource utilization is maximized by using the different dictionaries under different environments.

Description

Target two-dimensional DOA estimation method based on azimuth-pitch dictionary
Technical Field
The invention relates to the field of target parameter estimation, in particular to a method for solving two-dimensional DOA of a target.
Background
At present, more traditional target parameter estimation methods include methods utilizing preformed beam orientation, split beam orientation, interpolation method orientation, multi-beam orientation and the like. The traditional target parameter estimation method solves the problem by measuring the acoustic path difference or phase difference between array elements when a target signal reaches a matrix according to the principle. The theoretical research and the practical application of the method are mature, but the method has many defects, such as obvious precision reduction under severe conditions, higher precision requirement of measuring equipment, poor fault tolerance, larger error influence of the environment, and limited application occasions. In order to improve the target measurement accuracy under different environments, the measurement method is simpler to operate and wider in application, and the invention is very necessary.
The invention patent CN201611201932.8 discloses a target positioning and identifying method. The method mainly utilizes a magnetic anomaly technology to carry out measurement, but the method has higher requirement on the signal to noise ratio, the convergence speed and the convergence of the actual magnetic anomaly signal cannot be ensured, the actual application is difficult, and the method is not suitable for large-scale application.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a target two-dimensional DOA estimation method based on an azimuth-pitching dictionary, which is a solving algorithm method for estimating the azimuth-pitching of a target by establishing an azimuth-pitching joint dictionary to process target echoes, and the target two-dimensional DOA is jointly estimated by the azimuth-pitching dictionary, so that the target can be better estimated in azimuth-pitching under different environments, the error is small, the operation is greatly simplified compared with the original algorithm, and the defects of the original algorithm are overcome.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
1) receiving target echoes from different azimuths and pitch angles by using a planar array;
assuming that the planar array is composed of L array elements, the azimuth angle is divided into NθEach is respectively
Figure BDA0002637209620000011
Divide pitch angle into
Figure BDA0002637209620000012
A is prepared from
Figure BDA0002637209620000013
Receiving target echoes from different directions and pitch angles by using a planar array;
2) constructing an azimuth-elevation joint dictionary by using the received target echo;
column m of the azimuth-elevation joint dictionary Ψ is represented as:
Figure BDA0002637209620000021
wherein m ═ L × n ═ j-1) + k,
Figure BDA0002637209620000022
1≤k≤L×n，
Figure BDA0002637209620000023
Figure BDA0002637209620000024
wherein the content of the first and second substances,
Figure BDA0002637209620000025
for the L-th array element at azimuth angle of
Figure BDA0002637209620000026
A pitch angle of
Figure BDA0002637209620000027
The nth point of the received target echo;
3) receiving an echo y (t) of the target to be detected, and expressing the echo y (t) as the linear superposition of the echoes received in different directions and pitch angles in the step 1):
Figure BDA0002637209620000028
wherein the content of the first and second substances,
Figure BDA0002637209620000029
indicating that each array element is oriented at thetaiA pitch angle of
Figure BDA00026372096200000210
Sequentially connecting the received target echoes, and normalizing the received echoes to obtain echoes;
4) detecting a target echo, the target echo being expressed as:
y＝Ψα
wherein alpha is sparse representation of the echo in a dictionary;
5) estimating a target two-dimensional DOA by utilizing a dictionary, acquiring sparse expression of echo signals by a waveform dictionary, and introducing l in a convex optimization theory1The norm minimization method solves for a sparse vector α that minimizes the following equation:
Figure BDA00026372096200000211
Figure BDA0002637209620000031
wherein, σ is the noise variance, p is the potential of the dictionary, γ represents a weight coefficient, the magnitude increases with the noise enhancement, the obtained sparse vector α contains the azimuth-elevation information of the target, and the position of the maximum value in the sparse vector α is solved to be converted into the azimuth-elevation estimation result of the target.
The positioning method has the advantages that the calculation amount is small, the precision is high, different dictionaries can be established under different interferences aiming at different environments, the target azimuth pitching position can be accurately detected under various environmental conditions, the real-time requirement can be met, and the positioning method achieves a very good effect through test verification. The method can effectively solve the problem that the environmental interference in the traditional method influences the measurement, has wide application prospect and can be directly put into use. The dictionaries established in different environments can be repeatedly used, measurement is carried out for multiple times, and different dictionaries are used in different environments, so that the calculated amount is minimum, and the resource utilization is maximum.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a perspective view of an azimuth-elevation joint dictionary and its coherence analysis, which are established by an embodiment of the present invention, wherein fig. 2(a) is a coherence characteristic of the azimuth-elevation joint dictionary, and fig. 2(b) is a-3 dB contour diagram of the azimuth-elevation joint dictionary.
Fig. 3 shows two-dimensional DOA estimation conditions of different targets during the experiment of the present invention, where fig. 3(a) shows two target azimuth-pitch joint estimation results with target positions of (azimuth-20 °, pitch 4 °) and (azimuth-20 °, pitch 4.5 °), respectively, and fig. 3(b) shows two target azimuth-pitch joint estimation results with target positions of (azimuth-20 °, pitch 3 °) and (azimuth-19 °, pitch 3.5 °), respectively.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The basic idea of the invention is to establish an azimuth-elevation dictionary, and estimate the target two-dimensional direction of arrival by applying a convex optimization theory according to the received target echo.
The following description of the embodiments is made with reference to the accompanying drawings:
the method comprises the following steps: and reading target echoes at all azimuth-elevation joint angles, and finishing in a silencing water pool, wherein the transducer is positioned at 2m below water, and the distance between the transmitting array element and the center of the receiving array is 5 m. The range of the azimuth angle of the rotation of the receiving array is-20 degrees to 20 degrees, the step length is 0.5 degree, the range of the pitch angle is-20 degrees to 20 degrees, and the step length is 1 degree.
Step two: establishing an azimuth-elevation dictionary by using the received data, wherein the received echo signals E (t) can be expressed as linear superposition of various echoes on different azimuth-elevations, and the mth column of the azimuth-elevation joint dictionary is represented as follows:
Figure BDA0002637209620000041
wherein m ═ L × n ═ j-1) + k,
Figure BDA0002637209620000042
1≤k≤L×n，
Figure BDA0002637209620000043
the dictionary is as follows:
Figure BDA0002637209620000044
selecting a double sparse random array as a receiving array, wherein the aperture of the array is 1m, and the distance between a transmitting array element and the midpoint of two array elements is 5 m; in the azimuth dimension, the range of the azimuth angle is-20 degrees to 20 degrees, and the step length is 0.5 degrees; in the pitch dimension, the pitch angle ranges from-20 degrees to 20 degrees, and the step length is 1 degree; finally, the coherent characteristic analysis of the azimuth-elevation joint dictionary shown in FIG. 2 is obtained.
As can be seen from fig. 2, the azimuth-elevation joint dictionary is jagged, which is determined by the construction method of the dictionary; the coherence of the azimuth-elevation joint dictionary has 1 value on the diagonal, the other values are small, the width of the main lobe is narrow, and the height of the side lobe is low; the-3 dB contour map is a thin bright line, which shows that the difference between atoms in the azimuth-pitch dictionary is large, and the estimation precision is higher when the target azimuth-pitch is estimated.
Step three: and jointly estimating the azimuth angle and the pitch angle of the target by using the dictionary.
The value range of gamma is more than 0 and less than gammamax＝||ΨTx||∞And an empirical formula:
Figure BDA0002637209620000045
the formula σ is the noise variance, and p is the dictionary potential.
The method can be solved by an L1 norm minimization method in convex optimization theory, namely solving:
Figure BDA0002637209620000046
the finally solved sparse vector alpha contains target azimuth-elevation information.
Under the condition that the signal-to-noise ratio is-10 dB, the dictionary is used for finishing the joint estimation of two target two-dimensional DOAs. Assuming that the two target positions are (4 °, -20 °) and (4.5 °, -20 °), respectively, the estimation result is shown in fig. 3(a) of the specification; the two target positions are (3 °, -20 °) and (4 °, -20 °), respectively, and the estimation result is shown in fig. 3 (b).

Claims (1)

1. A target two-dimensional DOA estimation method based on an azimuth-elevation dictionary is characterized by comprising the following steps:
1) receiving target echoes from different azimuths and pitch angles by using a planar array;
assuming that the planar array is composed of L array elements, the azimuth angle is divided into NθEach is respectively
Figure FDA0002637209610000011
Divide pitch angle into
Figure FDA00026372096100000113
A is prepared from
Figure FDA0002637209610000012
Receiving target echoes from different directions and pitch angles by using a planar array;
2) constructing an azimuth-elevation joint dictionary by using the received target echo;
column m of the azimuth-elevation joint dictionary Ψ is represented as:
Figure FDA0002637209610000013
wherein m ═ L × n ═ j-1) + k,
Figure FDA0002637209610000014
1≤k≤L×n，
Figure FDA0002637209610000015
Figure FDA0002637209610000016
wherein the content of the first and second substances,
Figure FDA0002637209610000017
for the L-th array element at azimuth angle of
Figure FDA0002637209610000018
A pitch angle of
Figure FDA0002637209610000019
The nth point of the received target echo;
3) receiving an echo y (t) of the target to be detected, and expressing the echo y (t) as the linear superposition of the echoes received in different directions and pitch angles in the step 1):
Figure FDA00026372096100000110
wherein the content of the first and second substances,
Figure FDA00026372096100000111
indicating that each array element is oriented at thetaiA pitch angle of
Figure FDA00026372096100000112
Sequentially connecting the received target echoes, and normalizing the received echoes to obtain echoes;
4) detecting a target echo, the target echo being expressed as:
y＝Ψα
wherein alpha is sparse representation of the echo in a dictionary;
5) estimating a target two-dimensional DOA by utilizing a dictionary, acquiring sparse expression of echo signals by a waveform dictionary, and introducing l in a convex optimization theory1The norm minimization method solves for a sparse vector α that minimizes the following equation:
Figure FDA0002637209610000021
Figure FDA0002637209610000022
wherein, σ is the noise variance, p is the potential of the dictionary, γ represents a weight coefficient, the magnitude increases with the noise enhancement, the obtained sparse vector α contains the azimuth-elevation information of the target, and the position of the maximum value in the sparse vector α is solved to be converted into the azimuth-elevation estimation result of the target.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112924925A (en) * 2021-01-25 2021-06-08 西安电子科技大学 Airborne three-dimensional heterogeneous array DOA estimation method based on sparse Bayesian learning
CN113075633A (en) * 2021-03-26 2021-07-06 西北工业大学 Target positioning method based on distance-pitching joint dictionary

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140111372A1 (en) * 2012-10-22 2014-04-24 Saab-Sensis Corporation Sensor system and method for determining target location using sparsity-based processing
CN110196427A (en) * 2019-05-29 2019-09-03 西北工业大学 A kind of target location algorithm based on apart from orientation dictionary

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140111372A1 (en) * 2012-10-22 2014-04-24 Saab-Sensis Corporation Sensor system and method for determining target location using sparsity-based processing
CN110196427A (en) * 2019-05-29 2019-09-03 西北工业大学 A kind of target location algorithm based on apart from orientation dictionary

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
陈玉龙;黄登山;: "基于冗余字典稀疏表示的二维DOA估计", 计算机工程与应用, no. 29 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112924925A (en) * 2021-01-25 2021-06-08 西安电子科技大学 Airborne three-dimensional heterogeneous array DOA estimation method based on sparse Bayesian learning
CN113075633A (en) * 2021-03-26 2021-07-06 西北工业大学 Target positioning method based on distance-pitching joint dictionary

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