CN112379334A - Adaptive beam forming method and device - Google Patents

Adaptive beam forming method and device Download PDF

Info

Publication number
CN112379334A
CN112379334A CN202011203333.6A CN202011203333A CN112379334A CN 112379334 A CN112379334 A CN 112379334A CN 202011203333 A CN202011203333 A CN 202011203333A CN 112379334 A CN112379334 A CN 112379334A
Authority
CN
China
Prior art keywords
vector
main lobe
obtaining
covariance matrix
module
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011203333.6A
Other languages
Chinese (zh)
Other versions
CN112379334B (en
Inventor
施君南
侯凯强
姜艳娜
蒋洁
李伯达
许彦章
张天键
杜科
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Radio Equipment Research Institute
Original Assignee
Shanghai Radio Equipment Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Radio Equipment Research Institute filed Critical Shanghai Radio Equipment Research Institute
Priority to CN202011203333.6A priority Critical patent/CN112379334B/en
Publication of CN112379334A publication Critical patent/CN112379334A/en
Application granted granted Critical
Publication of CN112379334B publication Critical patent/CN112379334B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Mathematics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Remote Sensing (AREA)
  • Computing Systems (AREA)
  • Algebra (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Variable-Direction Aerials And Aerial Arrays (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a self-adaptive beam forming method and a device, wherein the self-adaptive beam forming method comprises the following steps: acquiring a main lobe guide vector of the antenna according to the incident direction of a target and the number of array elements of the antenna; obtaining a main lobe angle constraint vector of the antenna according to the incident direction and the array element number; acquiring a covariance matrix according to the echo data of the target; and acquiring a main lobe steady self-adaptive beam forming weight according to the main lobe steering vector, the main lobe angle constraint vector and the covariance matrix so as to generate a corresponding antenna directional diagram. The invention can rapidly obtain the steady self-adaptive beam forming weight of the main lobe, thereby improving the processing efficiency of the antenna signal and meeting the real-time requirement of a high maneuvering platform on the antenna signal processing.

Description

Adaptive beam forming method and device
Technical Field
The invention relates to the technical field of radars, in particular to a self-adaptive beam forming method and a self-adaptive beam forming device suitable for a high-mobility platform.
Background
The adaptive beam forming method is used as an important method for array signal processing of the radar antenna, and can adaptively change the beam forming weight of the antenna array element through information such as the incident direction of a target, echo data and the like, so that the radar antenna has better interference suppression capability and stronger resolution. The existing adaptive beam method for the high maneuvering platform comprises a minimum mean square error method, a linear constraint minimum variance method, a maximum signal-to-interference-and-noise ratio method and the like, but the method cannot overcome the problem of target position mismatch caused by external errors.
At present, the domestic self-adaptive beam forming method aiming at the high maneuvering platform is mostly realized on a DSP (digital Signal processing) chip, but the method has certain limitation. For example, since the relative speed of the high-speed moving platform is very fast, the interference direction and the incident direction of the target change rapidly, and the time for implementing the adaptive beamforming method based on the DSP chip does not meet the requirement of fast response of the high-speed maneuvering platform. The acquisition and the beam forming processing of the echo data are completed on different hardware platforms in a sequential cascading mode, a large amount of temporary data are added in data interaction between different platforms in the data processing mode, the data interaction time is prolonged, the antenna signal processing efficiency is reduced, the requirement of a signal processor on a storage space is increased, and the method is contrary to the actual situation that the hardware resources and the computing resources of a high-mobility platform are in short supply. When the echo data volume is large, in order to meet the requirement of signal processing instantaneity, a multi-channel multi-task processing technology is usually adopted for the radar antenna, however, the parallel processing capacity of the DSP chip to the multi-channel multi-task is limited, the number of the DSP chips must be increased to meet the requirement of multi-channel multi-task signal processing, the development cost is high, the economic benefit is reduced, the power consumption volume is a huge challenge, and the radar antenna is not suitable for being used on a high-mobility platform.
Disclosure of Invention
The invention aims to provide a self-adaptive beam forming method and a self-adaptive beam forming device, which overcome the defects of the traditional beam forming method and the realization process thereof, and are suitable for a high-mobility platform.
In order to achieve the purpose, the invention is realized by the following technical scheme:
an adaptive beamforming method, comprising:
and acquiring a main lobe guide vector of the antenna according to the incident direction of the target and the number of the array elements of the antenna.
And acquiring a main lobe angle constraint vector of the antenna according to the incident direction and the array element number.
And acquiring a covariance matrix according to the echo data of the target.
And acquiring a main lobe steady self-adaptive beam forming weight according to the main lobe steering vector, the main lobe angle constraint vector and the covariance matrix so as to generate a corresponding antenna directional diagram.
Preferably, the step of obtaining the main lobe steering vector of the antenna according to the incident direction of the target and the number of the antenna elements includes:
obtaining a main lobe spread angle range vector according to the incident direction and the number of preset main lobe spread angles;
obtaining an array element vector according to the number of the array elements;
and calculating the main lobe guide vector according to the main lobe spread angle range vector and the array element vector.
Preferably, the step of obtaining the main lobe angle constraint vector of the antenna according to the incident direction and the number of the array elements includes:
obtaining a first vector according to the incidence direction and the main lobe spread angle range vector;
obtaining a second vector according to the first vector and the array element vector;
element summation is carried out on the second vector to obtain a third vector;
calculating a phase angle of the third vector to obtain a fourth vector;
and calculating the main lobe angle constraint vector according to the fourth vector.
Preferably, the step of acquiring a covariance matrix from the echo data of the target includes:
obtaining a first matrix according to the echo data;
and calculating the covariance matrix according to the first matrix and the echo data.
Preferably, the step of obtaining the mainlobe robust adaptive beamforming weight according to the mainlobe steering vector, the mainlobe angle constraint vector, and the covariance matrix includes:
obtaining a second matrix according to the main lobe guide vector and the covariance matrix;
and calculating the mainlobe robust self-adaptive beam forming weight according to the second matrix, the mainlobe guide vector, the mainlobe angle constraint vector and the covariance matrix.
In another aspect, the present invention further provides an adaptive beamforming apparatus, including:
and the guide vector module is used for acquiring the main lobe guide vector of the antenna according to the incident direction of the target and the number of the array elements of the antenna.
And the angle constraint vector module is used for acquiring the main lobe angle constraint vector of the antenna according to the incidence direction and the array element number.
And the covariance matrix module is used for acquiring a covariance matrix according to the echo data of the target.
And the beam forming weight module is used for acquiring a main lobe steady self-adaptive beam forming weight according to the main lobe steering vector, the main lobe angle constraint vector and the covariance matrix so as to generate a corresponding antenna directional diagram.
The steering vector module, the angle constraint vector module, the covariance matrix module and the beam forming weight module are all arranged in an FPGA module library so as to obtain the steady self-adaptive beam forming weight of the main lobe based on the FPGA.
Preferably, the steering vector module includes:
obtaining a main lobe spread angle range vector according to the incident direction and the number of preset main lobe spread angles;
obtaining an array element vector according to the number of the array elements;
and calculating the main lobe guide vector according to the main lobe spread angle range vector and the array element vector.
Preferably, the angle constraint vector module comprises:
obtaining a first vector according to the incidence direction and the main lobe spread angle range vector;
obtaining a second vector according to the first vector and the array element vector;
element summation is carried out on the second vector to obtain a third vector;
calculating a phase angle of the third vector to obtain a fourth vector;
and calculating the main lobe angle constraint vector according to the fourth vector.
Preferably, the covariance matrix module comprises:
obtaining a first matrix according to the echo data;
and calculating the covariance matrix according to the first matrix and the echo data.
Preferably, the beamforming weight module includes:
obtaining a second matrix according to the main lobe guide vector and the covariance matrix;
and calculating the mainlobe robust self-adaptive beam forming weight according to the second matrix, the mainlobe guide vector, the mainlobe angle constraint vector and the covariance matrix.
Compared with the prior art, the invention has at least one of the following advantages:
according to the self-adaptive beam forming method and device provided by the invention, the main lobe steady self-adaptive beam weight can be obtained, the corresponding antenna directional diagram is generated, the main lobe steady self-adaptive beam weight can be obtained based on the FPGA, and the real-time performance is higher.
In the process of calculating the main lobe steady self-adaptive beam forming weight, the main lobe guide vector, the main lobe angle constraint vector and the covariance matrix can be calculated simultaneously, so that the calculation time is greatly reduced, the quick acquisition of the main lobe steady self-adaptive beam forming weight is realized, and the antenna signal processing efficiency is improved and reduced.
The steering vector module, the angle constraint vector module, the covariance matrix module and the beam forming weight module are all arranged in the FPGA module library, and the robust self-adaptive beam forming weight of the main lobe can be quickly obtained by utilizing the parallel processing advantage of the FPGA, so that the processing efficiency of the antenna signal is improved, and the real-time requirement of a high maneuvering platform on the antenna signal processing is met.
The self-adaptive beam forming device obtains the main lobe steady self-adaptive beam forming weight based on the FPGA, can reduce the requirement on hardware resources, has the characteristics of less power consumption, low development cost and easy realization, and is suitable for high-mobility platforms with limited volume and power consumption resources.
The invention can also improve the multi-channel multi-task processing capacity of the high maneuvering platform and increase the data throughput of the high maneuvering platform, thereby improving the performance of the high maneuvering platform.
Drawings
Fig. 1 is a flowchart of an adaptive beamforming method according to an embodiment of the present invention;
fig. 2 is a specific flowchart of an adaptive beamforming method according to an embodiment of the present invention;
fig. 3 is a block diagram of an adaptive beamforming apparatus according to an embodiment of the present invention;
fig. 4 is a comparison result between a directional antenna diagram corresponding to a main lobe robust adaptive beamforming weight and an actual directional antenna diagram according to an embodiment of the present invention;
fig. 5 is an error distribution of a directional antenna diagram corresponding to a main lobe robust adaptive beamforming weight and an actual directional antenna diagram according to an embodiment of the present invention;
fig. 6 is a diagram of a resource occupation ratio when an adaptive beamforming apparatus according to an embodiment of the present invention operates.
Detailed Description
The following describes an adaptive beamforming method and apparatus according to the present invention in detail with reference to the accompanying drawings and the detailed description. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise scale for the purpose of facilitating and distinctly aiding in the description of the embodiments of the present invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any structural modification, ratio relationship change or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
With reference to fig. 1-2, the present embodiment provides an adaptive beamforming method, including: s101, obtaining a main lobe guide vector of the antenna according to the incident direction of a target and the number of array elements of the antenna; step S102, obtaining a main lobe angle constraint vector of the antenna according to the incidence direction and the array element number; step S103, acquiring a covariance matrix according to the echo data of the target; and step S104, obtaining a main lobe steady self-adaptive beam forming weight according to the main lobe guide vector, the main lobe angle constraint vector and the covariance matrix so as to generate a corresponding antenna directional diagram.
Referring to fig. 1 and fig. 2, the step S101 includes: obtaining a main lobe spread angle range vector according to the incident direction and the number of preset main lobe spread angles; obtaining an array element vector according to the number of the array elements; and calculating the main lobe guide vector according to the main lobe spread angle range vector and the array element vector.
Specifically, the number of the preset main lobe spread angles may be 5, and the main lobe spread angle range vector may be calculated by using the following formula:
θs＝[θ0-2°,θ0-1°,θ0,θ0+1°,θ0+2°] (1)
wherein theta issRepresenting the main lobe spread angular range vector; theta0Representing the direction of incidence of the target.
The array element vector is a row vector with the step length of 1, and the calculation formula is as follows:
N＝[0,1,…,n-1] (2)
wherein N represents the array element vector and N represents the number of array elements.
After the main lobe spread angle range vector and the array element vector are obtained by calculation according to formulas (1) and (2), the main lobe guide vector can be calculated by adopting the following formula:
C＝exp(jπNΤsin(θs)) (3)
wherein C represents the main lobe steering vector; n is a radical ofTRepresenting a transpose of the array element vector N; thetasRepresenting the main lobe spread angular range vector.
In the present embodiment, the incident direction θ of the target0May be 0, the number n of array elements may be 16, and the main lobe steering vector C may be a 16 × 4 matrix.
Referring to fig. 1 and fig. 2, the step S102 includes: obtaining a first vector according to the incidence direction and the main lobe spread angle range vector; obtaining a second vector according to the first vector and the array element vector; element summation is carried out on the second vector to obtain a third vector; calculating a phase angle of the third vector to obtain a fourth vector; and calculating the main lobe angle constraint vector according to the fourth vector.
Specifically, in this embodiment, the calculation formula of the first vector is as follows:
t1(i)＝sin(θs(i))-sinθ0 (4)
wherein t is1Representing the first vector; theta0Representing the direction of incidence of the target; thetas(i) Vector θ representing the main lobe spread angular rangesThe ith element of (1); i is more than or equal to 1 and less than or equal to 5.
The calculation formula of the second vector is as follows:
t2(i)＝exp(jπNΤt1(i)) (5)
wherein t is2Representing the second vector; n is a radical ofTRepresenting a transpose of the array element vector N; t is t1Representing the first vector.
The calculation formula of the third vector is as follows:
t3(i)＝sum(t2(i)) (6)
wherein t is3Representing the third vector; t is t2Representing the second vector.
The calculation formula of the fourth vector is as follows:
Figure BDA0002756176300000071
wherein t is4Representing the fourth vector; t is t3Representing the third vector;
Figure BDA0002756176300000072
a companion matrix representing the third vector.
After the fourth vector is obtained by calculation according to formulas (4), (5), (6) and (7), the main lobe angle constraint vector can be calculated by using the following formula:
Figure BDA0002756176300000073
wherein F represents the mainlobe angle constraint vector; t is t4Representing the fourth vector.
In this embodiment, the main lobe angle constraint vector F is a 1 × 4 matrix.
With continuing reference to fig. 1 and fig. 2, the step S103 includes: obtaining a first matrix according to the echo data; the covariance matrix is calculated from the first matrix and the echo data.
Specifically, the calculation formula of the first matrix is as follows:
M＝rj×rj H (9)
wherein M represents the first matrix; r represents the echo data, the dimensionality of the echo data r is n multiplied by k, n represents the number of echo channels, namely the number of array elements, and k is the number of samples; r isjA jth sample representing the echo data; r isj HIs represented by rjA conjugate matrix of (a); j is more than or equal to 1 and less than or equal to k.
The covariance matrix can then be calculated using the following formula:
Figure BDA0002756176300000074
R(j)＝R(j-1)+M，R(0)＝0 (11)
wherein R represents the covariance matrix; m represents the first matrix.
In this embodiment, the echo data may be generated by simulation using MATLAB software; the number of the echo channels is consistent with the number of the array elements, and the number of the echo channels is 16; the number of samples is 128 points; the covariance matrix R is a 16 × 16 matrix.
With continuing reference to fig. 1 and fig. 2, the step S104 includes: obtaining a second matrix according to the main lobe guide vector and the covariance matrix; and calculating the mainlobe robust self-adaptive beam forming weight according to the second matrix, the mainlobe guide vector, the mainlobe angle constraint vector and the covariance matrix.
Specifically, the calculation formula of the second matrix is as follows:
T＝CHR-1C (12)
wherein T represents the second matrix; c represents the main lobe steering vector; cHA conjugate matrix representing the main lobe steering vector C; r-1An inverse matrix representing the covariance matrix R.
The main lobe robust adaptive beamforming weight may be calculated by using the following formula:
W＝R-1CT-1FH (13)
wherein W represents the mainlobe robust adaptive beamforming weights; r-1An inverse matrix representing the covariance matrix R; c represents the main lobe steering vector; t is-1An inverse matrix representing the second matrix T; fHA conjugate matrix representing the main lobe angle constraint vector F.
In this embodiment, the main lobe robust adaptive beamforming weight W is a 16 × 1 matrix, and a corresponding antenna pattern may be generated according to the main lobe robust adaptive beamforming weight. In the process of calculating the main lobe robust adaptive beam forming weight, the main lobe steering vector, the main lobe angle constraint vector and the covariance matrix can be calculated simultaneously instead of being obtained in a sequential cascading mode, so that the time consumption of calculation can be greatly reduced, the main lobe robust adaptive beam forming weight can be quickly obtained, the antenna signal processing efficiency is improved and reduced, and the real-time requirement of a high-mobility platform on antenna signal processing is further met.
With reference to fig. 3 to 6, based on the same inventive concept, the present embodiment further provides an adaptive beam forming apparatus, including: the guide vector module 101 is configured to obtain a main lobe guide vector of the antenna according to an incident direction of a target and the number of array elements of the antenna; an angle constraint vector module 102, configured to obtain a main lobe angle constraint vector of the antenna according to the incident direction and the number of array elements; a covariance matrix module 103, configured to obtain a covariance matrix according to the echo data of the target; a beam forming weight module 104, configured to obtain a main lobe robust adaptive beam forming weight according to the main lobe steering vector, the main lobe angle constraint vector, and the covariance matrix, so as to generate a corresponding antenna pattern; the steering vector module 101, the angle constraint vector module 102, the covariance matrix module 103, and the beamforming weight module 104 are all disposed in the FPGA module library 10, so as to obtain the mainlobe robust adaptive beamforming weight based on the FPGA.
With continued reference to fig. 3, the steering vector module 101 includes: obtaining a main lobe spread angle range vector according to the incident direction and the number of preset main lobe spread angles; obtaining an array element vector according to the number of the array elements; and calculating the main lobe guide vector according to the main lobe spread angle range vector and the array element vector.
Specifically, the incident direction of the target is input to the guide vector module 101 as a first input parameter, and the guide vector module 101 may calculate the main lobe spread angle range vector according to the incident direction, the preset main lobe spread angle number, and a formula (1). The steering vector module may further calculate to obtain the array element vector according to the array element number and the formula (2), calculate to obtain the main lobe steering vector according to the main lobe spread angle range vector, the array element vector and the formula (3), and output the main lobe steering vector to the beam forming weight module 104, so as to perform subsequent calculation of the main lobe robust adaptive beam forming weight, but the present invention is not limited thereto.
In this embodiment, the steering vector module 101 may be an IP core; formulas (1), (2) and (3) are realized by writing C codes, constraints are added for optimization, and a guide vector IP core can be generated after joint simulation and correct result and time sequence verification. The guide vector IP core has the function of calculating the main lobe guide vector and can be added into an IP core library of the FPGA. The constraint about the spread of the main lobe angle is added to the guide vector IP core, so that the sensitivity of the adaptive beam forming method to the angle error of the target incidence direction can be reduced, and the stability of the adaptive beam forming method is improved.
With continued reference to fig. 3, the angle constraint vector module 102 includes: obtaining a first vector according to the incidence direction and the main lobe spread angle range vector; obtaining a second vector according to the first vector and the array element vector; element summation is carried out on the second vector to obtain a third vector; calculating a phase angle of the third vector to obtain a fourth vector; and calculating the main lobe angle constraint vector according to the fourth vector.
Specifically, the incident direction of the target is input to the angle constraint vector module 102 as a first input parameter, the angle constraint vector module 102 may calculate the first vector according to the incident direction, the main lobe spread angle range vector and formula (4), then calculate the second vector according to the first vector, the array element vector and formula (5), then calculate the third vector according to the second vector and formula (6), then calculate the fourth vector according to the third vector and formula (7), and finally calculate the main lobe angle constraint vector according to the fourth vector and formula (8) by the angle constraint vector module 102, and output the main lobe angle constraint vector to the beam forming module 104 for subsequent calculation of the main lobe robust adaptive beam forming weight, however, the present invention is not limited thereto.
More specifically, regarding the main lobe spread angle range vector and the array element vector, the angle constraint vector module may calculate itself according to the incident direction and formulas (1) and (2), rather than obtaining from the steering vector module 101, so that the angle constraint vector module 102 and the steering vector module 101 may operate in parallel, rather than using a sequential cascade manner, to improve the signal processing efficiency, but the invention is not limited thereto.
In this embodiment, the angle constraint vector module 102 may be an IP core; formulas (1), (2), (4), (5), (6), (7) and (8) are realized by writing C codes, constraints are added for optimization, and an angle constraint vector IP core can be generated after joint simulation and correct result and time sequence verification. The angle constraint vector IP core has the function of calculating the main lobe angle constraint vector and can be added into an IP core library of the FPGA.
With continued reference to fig. 3, the covariance matrix module 103 includes: obtaining a first matrix according to the echo data; and calculating the covariance matrix according to the first matrix and the echo data.
Specifically, the echo data of the target is input to the covariance matrix module 103 as a second input parameter, where the dimensionality of the echo data may be n × k, n represents the number of echo channels, that is, the number of array elements, and k is the number of samples; the covariance matrix module 103 can calculate the first matrix according to the echo data and formula (9). The covariance matrix module 103 then calculates the covariance matrix according to the first matrix, the echo data, and formulas (10) and (11), and outputs the covariance matrix to the beamforming weight module 104 for subsequent calculation of the main lobe robust adaptive beamforming weight, which is not limited in the present invention.
In this embodiment, the number of the echo channels is 16, which is consistent with the number of the array elements; the number of samples is 128 points. The echo data may be buffered from 16 echo channels into corresponding 16 FIFO memories, and the echo data is input to the covariance matrix module 103 by the corresponding FIFO memories. The covariance matrix module 103 may be an IP core; by writing C codes to realize formulas (9), (10) and (11) and adding constraints for optimization, a covariance matrix IP core can be generated after joint simulation and correct result and timing verification. The covariance matrix IP core has the function of calculating the covariance matrix and can be added into the IP core library of the FPGA.
Referring to fig. 3, the beam forming weight module 104 includes: obtaining a second matrix according to the main lobe guide vector and the covariance matrix; and calculating the mainlobe robust self-adaptive beam forming weight according to the second matrix, the mainlobe guide vector, the mainlobe angle constraint vector and the covariance matrix.
Specifically, the main lobe steering vector, the covariance matrix, and the main lobe angle constraint vector are respectively input to the beam forming weight module 104; the beam forming weight module 104 may obtain the second matrix according to the mainlobe steering vector, the covariance matrix, and formula (12), and then calculate and output the mainlobe robust adaptive beam forming weight according to formula (13), which is not limited in the present invention.
In this embodiment, the beam forming weight module 104 may be an IP core; formulas (12) and (13) are realized by writing C codes, constraints are added for optimization, and a beam forming weight IP core can be generated after joint simulation and correct result and time sequence verification. The beam forming weight IP core has the function of calculating the main lobe steady self-adaptive beam forming weight and can be added into an IP core library of the FPGA.
In addition, in this embodiment, the steering vector module 101, the angle constraint vector module 102, the covariance matrix module 103, and the beam forming weight module 104 are all disposed in the FPGA module library 10, and the steering vector module 101, the angle constraint vector module 102, and the covariance matrix module 103 may perform parallel operations based on the FPGA and all output the operation results to the beam forming weight module 104; the beam forming weight module 104 calculates the main lobe robust adaptive beam forming weight based on the FPGA. More specifically, the FPGA may call the steering vector IP core, the angle constraint vector IP core, the covariance matrix IP core, and the beamforming weight IP core, standardize and package the four IP cores into a general IP core having a function of calculating a main lobe robust adaptive beamforming weight, name the general IP core as a main lobe robust adaptive beamforming weight calculation IP core, and add the general IP core into an IP core library of the FPGA; the FPGA calculates an IP core and sets IP core parameters by calling the main lobe robust adaptive beamforming weight, the incident direction and the echo data of the target are input at the input end, the main lobe robust adaptive beamforming weight can be output at the output end, and therefore the calculation of the main lobe robust adaptive beamforming weight is completed based on the FPGA.
As shown in fig. 4 to 6, when the incidence direction of the target is 0, the echo data of the target can be generated by MATLAB software simulation, the number of the elements of the antenna is 16, and when the chip model of the FPGA is K7-410T, the error between the antenna pattern corresponding to the weight calculated by the adaptive beam forming method and the actual antenna pattern is very small, which indicates that the adaptive beam forming method has high processing accuracy on the antenna signal (as shown in fig. 4 and 5), meanwhile, the adaptive beamforming device occupies less resources (as shown in fig. 6) and consumes 637.71us of time when calculating the main lobe robust adaptive beamforming weights based on the FPGA, if the FPGA with a chip model of V7-690T is adopted, the occupation proportion of the DSP resources is reduced to 35%, and the use proportion of the rest resources is also greatly reduced. Therefore, the self-adaptive beam forming device based on the FPGA can quickly acquire the main lobe steady self-adaptive beam forming weight, so that the real-time requirement of a high maneuvering platform on antenna signal processing is met; the requirement on hardware resources can be reduced, the multi-channel multi-task processing capacity of the mobile platform can be improved, and the data throughput of the mobile platform can be increased.
In summary, the adaptive beam forming method and apparatus provided in this embodiment can obtain the main lobe steering vector and the main lobe angle constraint vector of the antenna according to the incident direction of the target and the number of antenna elements; acquiring a covariance matrix according to echo data of a target; and finally, obtaining a main lobe steady self-adaptive beam forming weight according to the main lobe steering vector, the main lobe angle constraint vector and the covariance matrix, and generating a corresponding antenna directional diagram. The guide vector module, the angle constraint vector module, the covariance matrix module and the beam forming weight module in the adaptive beam forming device provided by the embodiment are all arranged in an FPGA module library, the parallel processing advantage of FPGA can be utilized, the robust adaptive beam forming weight of a main lobe is rapidly acquired, thereby improving the processing efficiency of antenna signals, meeting the real-time requirement of a high maneuvering platform on antenna signal processing, simultaneously reducing the requirement on hardware resources, improving the multi-channel multi-task processing capability of the high maneuvering platform, and increasing the data throughput of the high maneuvering platform.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (10)

1. An adaptive beamforming method, comprising:
acquiring a main lobe guide vector of the antenna according to the incident direction of a target and the number of array elements of the antenna;
obtaining a main lobe angle constraint vector of the antenna according to the incident direction and the array element number;
acquiring a covariance matrix according to the echo data of the target;
and acquiring a main lobe steady self-adaptive beam forming weight according to the main lobe steering vector, the main lobe angle constraint vector and the covariance matrix so as to generate a corresponding antenna directional diagram.
2. The adaptive beamforming method according to claim 1, wherein the step of obtaining the main lobe steering vector of the antenna according to the incident direction of the target and the number of antenna elements comprises:
obtaining a main lobe spread angle range vector according to the incident direction and the number of preset main lobe spread angles;
obtaining an array element vector according to the number of the array elements;
and calculating the main lobe guide vector according to the main lobe spread angle range vector and the array element vector.
3. The adaptive beamforming method according to claim 2, wherein the step of obtaining the main lobe angle constraint vector of the antenna according to the incident direction and the number of elements comprises:
obtaining a first vector according to the incidence direction and the main lobe spread angle range vector;
obtaining a second vector according to the first vector and the array element vector;
element summation is carried out on the second vector to obtain a third vector;
calculating a phase angle of the third vector to obtain a fourth vector;
and calculating the main lobe angle constraint vector according to the fourth vector.
4. The adaptive beamforming method of claim 1, wherein the step of obtaining a covariance matrix from echo data of the target comprises:
obtaining a first matrix according to the echo data;
and calculating the covariance matrix according to the first matrix and the echo data.
5. The adaptive beamforming method according to claim 1 wherein the step of obtaining the mainlobe robust adaptive beamforming weights according to the mainlobe steering vector, the mainlobe angle constraint vector and the covariance matrix comprises:
obtaining a second matrix according to the main lobe guide vector and the covariance matrix;
and calculating the mainlobe robust self-adaptive beam forming weight according to the second matrix, the mainlobe guide vector, the mainlobe angle constraint vector and the covariance matrix.
6. An adaptive beamforming apparatus, comprising:
the guide vector module (101) is used for acquiring a main lobe guide vector of the antenna according to the incident direction of a target and the number of array elements of the antenna;
an angle constraint vector module (102) for obtaining a main lobe angle constraint vector of the antenna according to the incident direction and the number of the array elements;
a covariance matrix module (103) for obtaining a covariance matrix from echo data of the target;
a beam forming weight module (104) for obtaining a main lobe robust adaptive beam forming weight according to the main lobe steering vector, the main lobe angle constraint vector and the covariance matrix to generate a corresponding antenna directional diagram;
the steering vector module (101), the angle constraint vector module (102), the covariance matrix module (103), and the beamforming weight module (104) are all disposed in an FPGA module library (10) to obtain the mainlobe robust adaptive beamforming weight based on the FPGA.
7. The adaptive beamforming apparatus according to claim 6, wherein the steering vector module (101) comprises:
obtaining a main lobe spread angle range vector according to the incident direction and the number of preset main lobe spread angles;
obtaining an array element vector according to the number of the array elements;
and calculating the main lobe guide vector according to the main lobe spread angle range vector and the array element vector.
8. The adaptive beamforming apparatus according to claim 7, wherein the angular constraint vector module (102) comprises:
obtaining a first vector according to the incidence direction and the main lobe spread angle range vector;
obtaining a second vector according to the first vector and the array element vector;
element summation is carried out on the second vector to obtain a third vector;
calculating a phase angle of the third vector to obtain a fourth vector;
and calculating the main lobe angle constraint vector according to the fourth vector.
9. The adaptive beamforming apparatus according to claim 6, wherein the covariance matrix module (103) comprises:
obtaining a first matrix according to the echo data;
and calculating the covariance matrix according to the first matrix and the echo data.
10. The adaptive beamforming apparatus according to claim 6, wherein the beamforming weight module (104) comprises:
obtaining a second matrix according to the main lobe guide vector and the covariance matrix;
and calculating the mainlobe robust self-adaptive beam forming weight according to the second matrix, the mainlobe guide vector, the mainlobe angle constraint vector and the covariance matrix.
CN202011203333.6A 2020-11-02 2020-11-02 Adaptive beam forming method and device Active CN112379334B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011203333.6A CN112379334B (en) 2020-11-02 2020-11-02 Adaptive beam forming method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011203333.6A CN112379334B (en) 2020-11-02 2020-11-02 Adaptive beam forming method and device

Publications (2)

Publication Number Publication Date
CN112379334A true CN112379334A (en) 2021-02-19
CN112379334B CN112379334B (en) 2023-03-14

Family

ID=74577832

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011203333.6A Active CN112379334B (en) 2020-11-02 2020-11-02 Adaptive beam forming method and device

Country Status (1)

Country Link
CN (1) CN112379334B (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20120094085A (en) * 2009-12-03 2012-08-23 닛본 덴끼 가부시끼가이샤 Antenna beam directivity device and antenna beam directivity method
CN104199053A (en) * 2014-09-22 2014-12-10 哈尔滨工程大学 Robust beam forming method based on constraint of direction of arrival of satellite signal
CN104777468A (en) * 2015-05-12 2015-07-15 上海无线电设备研究所 Method of using sum and difference channels of radar antenna for restraining radar side lobe interception
US20160211577A1 (en) * 2015-01-19 2016-07-21 Raytheon Corporation Side Lobe Modulation System and Related Techniques
CN105929374A (en) * 2016-06-24 2016-09-07 西安电子科技大学 Array radar self-adaptive interference resistance method based on beam shape-preserving
CN106226749A (en) * 2016-07-01 2016-12-14 西安电子科技大学 Based on space-time adaptive process radar and difference beam forming method
US20180331740A1 (en) * 2017-05-11 2018-11-15 Intel Corporation Multi-finger beamforming and array pattern synthesis
CN109639333A (en) * 2018-12-05 2019-04-16 上海神添实业有限公司 A kind of Beamforming Method based on effective reconstruct covariance matrix
CN110456314A (en) * 2019-08-05 2019-11-15 西安电子科技大学 Centralized MIMO radar waveform optimization method based on main lobe broadening

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20120094085A (en) * 2009-12-03 2012-08-23 닛본 덴끼 가부시끼가이샤 Antenna beam directivity device and antenna beam directivity method
CN104199053A (en) * 2014-09-22 2014-12-10 哈尔滨工程大学 Robust beam forming method based on constraint of direction of arrival of satellite signal
US20160211577A1 (en) * 2015-01-19 2016-07-21 Raytheon Corporation Side Lobe Modulation System and Related Techniques
CN104777468A (en) * 2015-05-12 2015-07-15 上海无线电设备研究所 Method of using sum and difference channels of radar antenna for restraining radar side lobe interception
CN105929374A (en) * 2016-06-24 2016-09-07 西安电子科技大学 Array radar self-adaptive interference resistance method based on beam shape-preserving
CN106226749A (en) * 2016-07-01 2016-12-14 西安电子科技大学 Based on space-time adaptive process radar and difference beam forming method
US20180331740A1 (en) * 2017-05-11 2018-11-15 Intel Corporation Multi-finger beamforming and array pattern synthesis
CN109639333A (en) * 2018-12-05 2019-04-16 上海神添实业有限公司 A kind of Beamforming Method based on effective reconstruct covariance matrix
CN110456314A (en) * 2019-08-05 2019-11-15 西安电子科技大学 Centralized MIMO radar waveform optimization method based on main lobe broadening

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
CAMELLIA DOROODY: "SINR improvement using Firefly Algorithm (FA) for Linear Constrained Minimum Variance (LCMV) beamforming technique", 《2015 INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATIONS, AND CONTROL TECHNOLOGY (I4CT)》 *
JUNNAN. SHI: "Robust transmit beamforming method for interference mitigation in MIMO scheme", 《2019 IEEE INTERNATIONAL CONFERENCE ON SIGNAL, INFORMATION AND DATA PROCESSING (ICSIDP)》 *
TANG, WM (TANG, WENMING): "Broadband Power Pattern Synthesis of Arbitrary Arrays with Mutual Coupling", 《2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD)》 *
YAN, HZ (YAN, HONG-ZHE): "An Improved LCMV Beamforming Algorithm in Multi-beam Bathymetry Sonars", 《2018 INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATIONS AND MECHATRONICS ENGINEERING (CCME 2018)》 *
冯阳等: "机载雷达超低空多径目标稳健STAP方法", 《系统工程与电子技术》 *
曹渊等: "适用于任意阵列的鲁棒波束形成算法", 《北京理工大学学报》 *
牟昱舟: "阵列天线广义数字波束成形的研究", 《中国优秀博硕士学位论文全文数据库(硕士)》 *
许彦章: "一种脉冲雷达数字AGC的设计与FPGA实现", 《微型机与应用》 *

Also Published As

Publication number Publication date
CN112379334B (en) 2023-03-14

Similar Documents

Publication Publication Date Title
CN110161452B (en) Direction-of-arrival estimation method based on cross-prime L-shaped electromagnetic vector sensor array
CN108957387B (en) Method and system for estimating two-dimensional arrival angle of satellite signal
CN110673086A (en) Two-dimensional angle super-resolution method based on digital array radar
CN103984676A (en) Rectangular projection adaptive beamforming method based on covariance matrix reconstruction
WO2018161681A1 (en) Method and device for beam sidelobe suppression
CN101344582B (en) Gravel-blind minimum variance distortionless response beam forming method
CN106249196A (en) Three-component acoustic vector sensors thinned array quaternary number ambiguity solution method
CN111366893B (en) Non-circular signal azimuth angle estimation method under uniform circular array unknown mutual coupling condition
CN111323743A (en) Signal direction of arrival estimation method and device based on sparse symmetric array
CN106680779B (en) Beam-forming method and device under impulsive noise
CN110231589B (en) Multipath signal direction-of-arrival estimation method with large diffusion angle
CN110736976A (en) sonar beam former performance estimation method of arbitrary array
CN110646765A (en) Riemann distance-based generalized sidelobe cancellation algorithm
CN112379334B (en) Adaptive beam forming method and device
CN111812607B (en) Meter wave MIMO radar low elevation angle estimation method based on beam space
JP2003270316A (en) Angle measuring instrument, angle measuring method, and program
CN113341371B (en) DOA estimation method based on L array and two-dimensional ESPRIT algorithm
CN115037352A (en) Noise reduction method for satellite beam alignment enhancement
CN111859272A (en) Rapid MUSIC spectrum decomposition method, device and equipment for large-scale antenna
CN110873866A (en) Monostatic MIMO radar target angle estimation method under cross-coupling condition
Wang et al. A two-dimensional direction finding method based on non-uniform array
CN109655801A (en) The angle-measuring method of radar seeker efficient spatial spectrum based on bicyclic round battle array
US11994603B1 (en) Method of sparse array oriented approach for DOA estimation of grating lobe target filtering
US11953584B2 (en) Three-dimensional location estimation using multiplicative processing of sensor measurements
Song et al. Application of Steering Vectors Rotation Invariance in DOA Estimation with Mutual Coupling

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant