WO2023209925A1 - アンテナ制御装置、アンテナ制御方法及びアンテナ装置 - Google Patents

アンテナ制御装置、アンテナ制御方法及びアンテナ装置 Download PDF

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Publication number
WO2023209925A1
WO2023209925A1 PCT/JP2022/019215 JP2022019215W WO2023209925A1 WO 2023209925 A1 WO2023209925 A1 WO 2023209925A1 JP 2022019215 W JP2022019215 W JP 2022019215W WO 2023209925 A1 WO2023209925 A1 WO 2023209925A1
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Prior art keywords
excitation
amplitude
antenna
phase error
pattern
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PCT/JP2022/019215
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English (en)
French (fr)
Japanese (ja)
Inventor
一成 紀平
徹 高橋
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Mitsubishi Electric Corp
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Mitsubishi Electric Corp
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Priority to JP2022569002A priority Critical patent/JP7353515B1/ja
Priority to PCT/JP2022/019215 priority patent/WO2023209925A1/ja
Publication of WO2023209925A1 publication Critical patent/WO2023209925A1/ja
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01Q—ANTENNAS, i.e. RADIO AERIALS
    • H01Q3/00—Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system
    • H01Q3/26—Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system varying the relative phase or relative amplitude of energisation between two or more active radiating elements; varying the distribution of energy across a radiating aperture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station

Definitions

  • the present disclosure relates to an antenna control device, an antenna control method, and an antenna device.
  • an antenna device that includes a first array antenna that transmits radio waves toward a communication target device and a second array antenna that receives radio waves transmitted from the communication target device (for example, see Patent Document 1).
  • the antenna device calculates a weighting coefficient (( It is provided with a coefficient determining means for determining the excitation coefficient (hereinafter referred to as "excitation coefficient"). Further, the coefficient determining means determines an excitation coefficient by which a signal received by each of the plurality of antenna elements included in the second array antenna is multiplied according to the relative position.
  • the antenna device may deteriorate over time.
  • the environment in which the antenna device is used may change. Examples of changes in the usage environment of the antenna device include changes in external noise from outside the antenna device, changes in thermal noise generated from the receiver connected to the second array antenna, or changes in the first array antenna. There are changes in thermal noise resulting from the connected transmitter. For example, age-related deterioration of the antenna device or changes in the environment in which the antenna device is used may cause the excitation coefficient of the antenna element to deviate from the appropriate excitation coefficient.
  • the antenna device disclosed in Patent Document 1 has a problem in that the excitation coefficient of each antenna element determined by the coefficient determining means may deviate from an appropriate excitation coefficient.
  • the present disclosure has been made to solve the above-mentioned problems, and aims to provide an antenna control device and an antenna control method that can suppress deviations in excitation coefficients of respective antenna elements.
  • An antenna control device acquires an amplitude pattern of an array antenna having a plurality of antenna elements, and includes an excitation amplitude error of each antenna element and an excitation phase error of each antenna element based on the amplitude pattern.
  • the antenna includes an error estimation section that estimates an excitation amplitude phase error, and an excitation coefficient control section that controls an excitation coefficient of each antenna element based on the excitation amplitude phase error estimated by the error estimation section.
  • FIG. 1 is a configuration diagram showing an antenna device including an antenna control device 10 according to Embodiment 1.
  • FIG. 1 is a hardware configuration diagram showing hardware of an antenna control device 10 according to Embodiment 1.
  • FIG. 1 is a hardware configuration diagram of a computer when the antenna control device 10 is realized by software, firmware, or the like.
  • 3 is a flowchart showing an antenna control method that is a processing procedure of the antenna control device 10.
  • FIG. FIG. 2 is an explanatory diagram showing a neural network that realizes a learning model.
  • FIG. 6A is an explanatory diagram showing an example of the amplitude pattern P 1 of the array antenna 1 or the amplitude pattern P 2 of the array antenna 2.
  • FIG. 1 is a configuration diagram showing an antenna device including an antenna control device 10 according to Embodiment 1.
  • FIG. 1 is a hardware configuration diagram showing hardware of an antenna control device 10 according to Embodiment 1.
  • FIG. 1 is a hardware configuration diagram of a computer when the antenna control device 10 is realized by software
  • FIG. 3 is an explanatory diagram showing a simulation result of an amplitude pattern P 1 of the array antenna 1 or a simulation result of the amplitude pattern P 2 of the array antenna 2.
  • FIG. 2 is an explanatory diagram showing an amplitude pattern P 1 of the array antenna 1 included in the learning data D 1 or an amplitude pattern P 2 of the array antenna 2 included in the learning data D 2 .
  • FIG. 11A is an explanatory diagram showing the amplitude pattern of the horizontal cut surface among the two cut surfaces.
  • FIG. 11B is an explanatory diagram showing the amplitude pattern of the vertical cut surface among the two cut surfaces.
  • FIG. 1 is a configuration diagram showing an antenna device including an antenna control device 10 according to the first embodiment.
  • FIG. 2 is a hardware configuration diagram showing the hardware of the antenna control device 10 according to the first embodiment.
  • an array antenna 1 which is a first array antenna, has K antenna elements 1-1 to 1-K as a plurality of transmitting antenna elements. K is an integer of 2 or more.
  • the antenna device shown in FIG. 1 includes an array antenna 1 for transmission and an array antenna 2 for reception.
  • the antenna device may include only either the array antenna 1 for transmission or the array antenna 2 for reception.
  • the array antenna 1 or the array antenna 2 may be a transmitting/receiving antenna that serves both for transmitting and receiving.
  • the transmission signal generation section 3 generates a transmission signal Tx, and outputs the transmission signal Tx to the transmission beam forming section 4.
  • the transmission beam forming section 4 distributes the transmission signal Tx generated by the transmission signal generation section 3 into K transmission signals Tx 1 to Tx K.
  • each transmitted signal Tx k ' after the excitation coefficient multiplication. is output to the transmitter 5.
  • the transmission beam forming unit 4 distributes the transmission signal Tx generated by the transmission signal generation unit 3 into K transmission signals Tx 1 to Tx K , and each of the distributed transmission signals Tx k is multiplied by an excitation coefficient EC 1,k .
  • the transmission signal generation unit 3 generates K transmission signals Tx 1 to Tx K
  • the transmission beam forming unit 4 generates each transmission signal generated by the transmission signal generation unit 3.
  • Tx k may be multiplied by an excitation coefficient EC 1,k .
  • the transmitter 5 converts the frequency of each transmission signal Tx k ' from a frequency in an IF (Intermediate Frequency) band to a frequency in an RF (Radio Frequency) band.
  • the receiving section 6 amplifies each frequency-converted received signal and outputs each amplified received signal Rx g ' to the receiving beam forming section 7.
  • the reception beam forming unit 7 multiplies each reception signal Rx g ' by an excitation coefficient EC 2,g .
  • the reception beam forming unit 7 combines the G reception signals Rx 1 to Rx G after excitation coefficient multiplication, and outputs a composite signal S of the G reception signals Rx 1 to Rx G to a reception device (not shown).
  • the antenna control device 10 includes a learning device 11, a learning model storage section 14, an error estimation section 15, and an excitation coefficient control section 16.
  • the learning device 11 includes a learning data acquisition section 12 and a learning processing section 13.
  • the learning device 11 generates a learning model GM 1 for the array antenna 1 and stores the learning model GM 1 in the learning model storage unit 14 .
  • the learning device 11 generates a learning model GM 2 for the array antenna 2, and stores the learning model GM 2 in the learning model storage unit 14.
  • a learning device 11 generates a learning model GM 1 for the array antenna 1 and a learning model GM 2 for the array antenna 2.
  • the learning data acquisition unit 12 is realized, for example, by the learning data acquisition circuit 21 shown in FIG.
  • the excitation amplitude phase error E 1,k includes the excitation amplitude error of the antenna element 1-k and the excitation phase error of the antenna element 1-k.
  • the excitation amplitude phase error E 2,g includes the excitation amplitude error of the antenna element 2-g and the excitation phase error of the antenna element 2-g.
  • the learning processing section 13 is realized, for example, by the learning processing circuit 22 shown in FIG.
  • the learning processing unit 13 provides the learning data D 1 acquired by the learning data acquisition unit 12 to the learning model GM 1 , and calculates the excitation amplitude phase error E 1,k corresponding to the amplitude pattern P 1 of the array antenna 1 into the learning model. Have GM 1 learn. Further, the learning processing unit 13 provides the learning data D 2 acquired by the learning data acquisition unit 12 to the learning model GM 2 and calculates the excitation amplitude phase error E 2,g corresponding to the amplitude pattern P 2 of the array antenna 2. Let learning model GM 2 learn.
  • the learning data D1 corresponds to the amplitude pattern P1 of the array antenna 1 when used in each area and the amplitude pattern P1 . It includes an excitation amplitude phase error E 1,k .
  • the learning data D 1 may include, for example, when the usage environment of the array antenna 1 changes, the amplitude pattern P 1 of the array antenna 1 when used in each of a plurality of mutually different usage environments, and the amplitude pattern P The excitation amplitude phase error E 1,k corresponding to 1 is included.
  • the learning data D2 corresponds to the amplitude pattern P2 of the array antenna 2 when used in each area and the amplitude pattern P2 .
  • the learning data D2 may include, for example, when the usage environment of the array antenna 2 changes, the amplitude pattern P2 of the array antenna 2 when used in each of a plurality of mutually different usage environments, and the amplitude pattern P2 of the array antenna 2 when used in each of a plurality of mutually different usage environments. 2 , an excitation amplitude phase error E2,g corresponding to E2,g .
  • the learning processing unit 13 causes the learning model storage unit 14 to store each of the learned learning model GM 1 and the learned learning model GM 2 .
  • the learning model storage unit 14 is realized, for example, by the learning model storage circuit 23 shown in FIG. 2.
  • the learning model storage unit 14 stores each of the learned learning model GM 1 and the learned learning model GM 2 .
  • the error estimation unit 15 is realized, for example, by the error estimation circuit 24 shown in FIG.
  • the error estimation unit 15 obtains each of the amplitude pattern P 1 of the array antenna 1 and the amplitude pattern P 2 of the array antenna 2.
  • the error estimation unit 15 provides the amplitude pattern P 1 of the array antenna 1 to the trained learning model GM 1 and obtains the excitation amplitude phase error E 1,k from the learning model GM 1 .
  • the error estimation unit 15 provides the amplitude pattern P 2 of the array antenna 2 to the trained learning model GM 2 and obtains the excitation amplitude phase error E 2,g from the learning model GM 2 .
  • the error estimation unit 15 outputs each of the excitation amplitude phase error E 1,k and the excitation amplitude phase error E 2,g to the excitation coefficient control unit 16.
  • the error estimation unit 15 gives the amplitude pattern P 1 to the learning model GM 1, obtains the excitation amplitude phase error E 1,k from the learning model GM 1, and obtains the excitation amplitude phase error E 1,k from the learning model GM 1. is given to the learning model GM 2 , and the excitation amplitude phase error E 1,k is obtained from the learning model GM 2 .
  • the error estimation unit 15 estimates the excitation amplitude phase error E 1, k from the amplitude pattern P 1 according to the rule base, and estimates the excitation amplitude phase error E 1,k from the amplitude pattern P 2 according to the rule base.
  • the error E 2,g may be estimated.
  • the excitation coefficient control section 16 is realized, for example, by an excitation coefficient control circuit 25 shown in FIG. 2.
  • each of the learning data acquisition unit 12, learning processing unit 13, learning model storage unit 14, error estimation unit 15, and excitation coefficient control unit 16, which are the components of the antenna control device 10, is configured as shown in FIG. It is assumed that this will be realized using dedicated hardware. That is, it is assumed that the antenna control device 10 is realized by a learning data acquisition circuit 21, a learning processing circuit 22, a learning model storage circuit 23, an error estimation circuit 24, and an excitation coefficient control circuit 25.
  • the learning model storage circuit 23 includes, for example, RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electric Memory).
  • Non-volatile memory such as (Erasable, Programmable, Read Only Memory) This includes a flexible or volatile semiconductor memory, a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a DVD (Digital Versatile Disc). Further, each of the learning data acquisition circuit 21, the learning processing circuit 22, the error estimation circuit 24, and the excitation coefficient control circuit 25 is configured using, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or a combination thereof.
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • the components of the antenna control device 10 are not limited to those realized by dedicated hardware, but the antenna control device 10 may be realized by software, firmware, or a combination of software and firmware. Good too.
  • Software or firmware is stored in a computer's memory as a program.
  • a computer means hardware that executes a program, and includes, for example, a CPU (Central Processing Unit), a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a processor, or a DSP (Digital Signal Processor). do.
  • FIG. 3 is a hardware configuration diagram of a computer when the antenna control device 10 is realized by software, firmware, or the like.
  • the learning model storage unit 14 is configured on the memory 31 of the computer.
  • a program for causing a computer to execute each processing procedure in the learning data acquisition section 12, the learning processing section 13, the error estimation section 15, and the excitation coefficient control section 16 is stored in the memory 31.
  • the processor 32 of the computer executes the program stored in the memory 31.
  • FIG. 2 shows an example in which each of the components of the antenna control device 10 is realized by dedicated hardware
  • FIG. 3 shows an example in which the antenna control device 10 is realized by software, firmware, etc.
  • this is just an example, and some of the components in the antenna control device 10 may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, or the like.
  • FIG. 4 is a flowchart showing an antenna control method which is a processing procedure of the antenna control device 10.
  • the learning model GM 1 for the array antenna 1 and the learning model GM 2 for the array antenna 2, which are generated by the learning device 11, are realized by, for example, a neural network as shown in FIG. 5.
  • FIG. 5 is an explanatory diagram showing a neural network that realizes a learning model.
  • the neural network shown in FIG. 5 has an input layer, a middle layer, and an output layer.
  • input layers are X1, X2, and X3, intermediate layers are Y1 and Y2, and output layers are Z1, Z2, and Z3.
  • the neural network shown in FIG. 5 has one intermediate layer. However, this is just an example, and there may be two or more intermediate layers.
  • Learning algorithms used by the learning processing unit 13 of the learning device 11 include known algorithms such as supervised learning, semi-supervised learning, unsupervised learning, deep learning, and reinforcement learning.
  • Deep learning is a learning algorithm that learns to extract the feature values themselves.
  • Reinforcement learning is a learning algorithm that follows, for example, genetic programming, functional logic programming, or support vector machines.
  • the learning processing unit 13 uses supervised learning as the learning algorithm.
  • FIG. 6A is an explanatory diagram showing an example of the amplitude pattern P 1 of the array antenna 1 or the amplitude pattern P 2 of the array antenna 2.
  • the horizontal axis represents the angle [deg. ]
  • the vertical axis is the normalized amplitude [dB].
  • a plurality of amplitude patterns P 1 for array antenna 1 or a plurality of amplitude patterns P 2 for array antenna 2 are shown.
  • FIG. 6A a plurality of amplitude patterns P 1 for array antenna 1 or a plurality of amplitude patterns P 2 for array antenna 2 are shown.
  • the vertical axis represents the excitation amplitude error and excitation phase error included in the excitation amplitude phase error E 1,k , or the excitation amplitude error and excitation phase error included in the excitation amplitude phase error E 2,g. Each is shown.
  • the excitation amplitude error occurring in the antenna element 1-k (or 2-g) is determined by the excitation amplitude of the antenna element 1-k (or 2-g) corresponding to the reference amplitude pattern P std and the amplitude pattern P 1 (or P 2 ) is the error with the excitation amplitude of the antenna element 1-k (or 2-g) corresponding to the antenna element 1-k (or 2-g).
  • the reference amplitude pattern P std is an ideal amplitude pattern in which neither an excitation amplitude error nor an excitation phase error occurs.
  • the excitation phase error occurring in the antenna element 1-k (or 2-g) is the excitation phase error of the antenna element 1-k (or 2-g) corresponding to the reference amplitude pattern P std and the amplitude pattern P 1 ( or P 2 ) with the excitation phase of the antenna element 1-k (or 2-g).
  • the angle of the main beam is 0 [deg. ]
  • a plurality of amplitude patterns P1 are shown.
  • the angle of the main beam is 0 [deg. ]
  • the learning data D 1 may include a plurality of amplitude patterns P 1 having angles other than .
  • the learning data D1 acquired by the learning data acquisition unit 12 has a main beam angle of 0 [deg. ], and the main beam angle is 0[deg.]. ] may include an amplitude pattern P1 having an angle other than .
  • the learning data acquisition section 12 outputs one or more learning data D1 to the learning processing section 13.
  • the excitation amplitude phase error E 1,k corresponds to the amplitude pattern P 1 of the array antenna 1. That is, the excitation amplitude phase error E 1,k is the correct value of the excitation amplitude phase error with respect to the amplitude pattern P 1 of the array antenna 1.
  • the learning processing unit 13 acquires one or more learning data D 1 from the learning data acquisition unit 12 .
  • the learning processing unit 13 supplies each learning data D 1 to the input layer of the learning model GM 1 and calculates the excitation amplitude phase error E 1 ,k corresponding to the amplitude pattern P 1 included in each learning data D 1. is trained by learning model GM 1 . That is, when the amplitude pattern P 1 of the array antenna 1 is given to the input layer of the learning model GM 1 , the learning processing unit 13 generates a signal corresponding to the amplitude pattern P 1 of the array antenna 1 from the output layer of the learning model GM 1.
  • the learning data D2 acquired by the learning data acquisition unit 12 has a main beam angle of 0 [deg. ], and the main beam angle is 0[deg.]. ] may include an amplitude pattern P2 having an angle other than .
  • the learning data acquisition section 12 outputs one or more learning data D2 to the learning processing section 13.
  • the excitation amplitude phase error E 2,g corresponds to the amplitude pattern P 2 of the array antenna 2.
  • the excitation amplitude phase error E 2,g of the antenna element 2-g is the correct value of the excitation amplitude phase error with respect to the amplitude pattern P 2 of the array antenna 2.
  • the learning processing unit 13 acquires one or more learning data D 2 from the learning data acquisition unit 12 .
  • the learning processing unit 13 supplies the respective learning data D2 to the input layer of the learning model GM2 , and calculates the excitation amplitude phase error E2 ,g corresponding to the amplitude pattern P2 included in the respective learning data D2.
  • the learning model GM 2 is made to learn. That is, when the amplitude pattern P 2 of the array antenna 2 is given to the input layer of the learning model GM 2 , the learning processing unit 13 generates a signal corresponding to the amplitude pattern P 2 of the array antenna 2 from the output layer of the learning model GM 2.
  • the learning processing unit 13 causes the learning model storage unit 14 to store each of the learned learning model GM 1 and the learned learning model GM 2 .
  • error estimating unit 15 acquires each of the amplitude pattern P 1 of array antenna 1 and the amplitude pattern P 2 of array antenna 2 (steps in FIG. 4). ST1).
  • Each of the amplitude pattern P 1 of the array antenna 1 and the amplitude pattern P 2 of the array antenna 2 is an amplitude pattern measured by a measuring device (not shown), for example.
  • the error estimation unit 15 provides the amplitude pattern P 1 of the array antenna 1 to the input layer of the trained learning model GM 1 stored in the learning model storage unit 14 .
  • the error estimation unit 15 acquires the excitation amplitude phase error E 1,k from the output layer of the learning model GM 1 (step ST2 in FIG. 4).
  • the error estimation unit 15 provides the amplitude pattern P 2 of the array antenna 2 to the input layer of the trained learning model GM 2 stored in the learning model storage unit 14 .
  • the error estimation unit 15 acquires the excitation amplitude phase error E 2,g output from the output layer of the learning model GM 2 (step ST2 in FIG. 4).
  • the error estimation unit 15 outputs each of the excitation amplitude phase error E 1,k and the excitation amplitude phase error E 2,g to the excitation coefficient control unit 16.
  • the process of calculating the excitation coefficient EC 1,k such that the excitation amplitude phase error E 1, k becomes zero is a well-known technique, so a detailed explanation thereof will be omitted.
  • the excitation coefficient EC 1,k at which the excitation amplitude phase error E 1,k becomes zero is not limited to one at which the excitation amplitude phase error E 1,k becomes completely zero, but within a range that causes no practical problems. , the concept includes those in which the excitation amplitude phase error E 1,k is approximately zero.
  • the process of calculating the excitation coefficient EC 2,g such that the excitation amplitude phase error E 2,g becomes zero is a well-known technique, so a detailed explanation will be omitted.
  • the excitation coefficient EC 2,g at which the excitation amplitude phase error E 2,g becomes zero is not limited to one at which the excitation amplitude phase error E 2,g becomes completely zero, but within a range that causes no practical problems. , the excitation amplitude phase error E 2,g is approximately zero.
  • the transmission signal generation section 3 generates a transmission signal Tx, and outputs the transmission signal Tx to the transmission beam forming section 4.
  • the transmission beam forming unit 4 distributes the transmission signal Tx into K transmission signals Tx 1 to Tx K.
  • the transmitter 5 converts the frequency of each transmission signal Tx k ' from an IF band frequency to an RF band frequency.
  • the antenna element 1-k of the array antenna 1 radiates the transmission signal Tx k '' into space as a radio wave.
  • the receiving unit 6 converts the frequency of the received signal Rx g '' from the RF band frequency to the IF band frequency.
  • the receiving section 6 amplifies each frequency-converted received signal and outputs each amplified received signal Rx g ' to the receiving beam forming section 7.
  • the reception beam forming unit 7 multiplies each reception signal Rx g ' by an excitation coefficient EC 2,g .
  • the reception beam forming unit 7 combines the G reception signals Rx 1 to Rx G after excitation coefficient multiplication, and outputs a composite signal S of the G reception signals Rx 1 to Rx G to a reception device (not shown).
  • FIG. 7 is an explanatory diagram showing the simulation results of the amplitude pattern P 1 of the array antenna 1 or the simulation result of the amplitude pattern P 2 of the array antenna 2.
  • the horizontal axis represents the angle [deg. ]
  • the vertical axis is the normalized amplitude [dB].
  • FIG. 7 shows simulation results of the amplitude pattern P1 of the array antenna 1 in which 16 antenna elements 1-1 to 1-16 are arranged in a straight line, or 16 antenna elements 2-1 to 2-16. shows the simulation result of the amplitude pattern P2 of the array antenna 2 arranged on a straight line.
  • the solid line indicates the amplitude pattern P 1 of the array antenna 1 when multiplied by the excitation coefficient EC 1,k by the transmitting beam forming unit 4, or the amplitude pattern P 1 when multiplied by the excitation coefficient EC 2,g by the receiving beam forming unit 7.
  • An amplitude pattern P2 of the array antenna 2 is shown. That is, the amplitude pattern P 1 of the array antenna 1 when the excitation coefficient EC 1 ,k is controlled based on the excitation amplitude phase error E 1 ,k output from the learned learning model GM 1 or the learned model GM 1 It shows the amplitude pattern P 2 of the array antenna 2 when the excitation coefficient EC 2,g is controlled based on the excitation amplitude phase error E 2,g output from the learning model GM 2 .
  • the dotted line is the correct amplitude pattern P 1 in which the excitation amplitude phase error E 1 ,k is considered, or the correct amplitude pattern P 2 in which the excitation amplitude phase error E 2,g is considered.
  • the broken line is the nominal of the amplitude pattern P1 without error or the nominal of the amplitude pattern P2 without error. It can be seen that the amplitude pattern P 1 or P 2 indicated by the solid line corresponds well to the amplitude pattern P 1 or P 2 indicated by the broken line.
  • the amplitude pattern of the array antenna 1 (or 2) having a plurality of antenna elements 1-1 to 1-K (or 2-1 to 2-G) is acquired, and based on the amplitude pattern, , an error estimation unit 15 that estimates an excitation amplitude phase error including an excitation amplitude error of each antenna element 1-k (or 2-g) and an excitation phase error of each antenna element 1-k (or 2-g). and an excitation coefficient control unit 16 that controls the excitation coefficient of each antenna element 1-k (or 2-g) based on the excitation amplitude phase error estimated by the error estimation unit 15.
  • the apparatus 10 was configured. Therefore, the antenna control device 10 can suppress deviations in the excitation coefficients of the respective antenna elements 1-k (or 2-g).
  • the array antenna 1 By suppressing the deviation in the excitation coefficient of each antenna element 1-k (or 2-g), for example, even if the antenna device deteriorates over time or the usage environment of the antenna device changes, the array antenna 1 ( Alternatively, deviation of the pointing direction (2) from the desired pointing direction can be suppressed.
  • learning data D1 including an excitation amplitude phase error E1 ,k corresponding to the amplitude pattern P1 of the array antenna 1 is provided to the input layer of the learning model GM1 .
  • learning data D 2 including an excitation amplitude phase error E 2 ,g corresponding to the amplitude pattern P 2 of the array antenna 2 is provided to the input layer of the learning model GM 2 .
  • the distribution of the excitation amplitude phase error E 1 ,k included in the learning data D 1 and the distribution of the excitation amplitude phase error E 2 ,g included in the learning data D 2 are normal distributions. It's okay.
  • each of the excitation amplitude phase error E 1,k and the excitation amplitude phase error E 2,g may be obtained from a function modeled based on measurement data.
  • a learning device 11 In the antenna control device 10 shown in FIG. 1, a learning device 11 generates a learning model GM 1 for the array antenna 1 and a learning model GM 2 for the array antenna 2. However, this is only an example, and the learning device 11 may generate a learning model GM shared by the array antenna 1 and the array antenna 2.
  • the learning device 11 When the learning device 11 generates a learning model GM shared by array antenna 1 and array antenna 2, the learning data given to the input layer of the learning model GM is the learning data of array antenna 1 when array antenna 1 is used. It includes an amplitude pattern and an excitation amplitude phase error corresponding to the amplitude pattern.
  • the learning data given to the input layer of the learning model GM includes an amplitude pattern of the array antenna 2 when the array antenna 2 is used and an excitation amplitude phase error corresponding to the amplitude pattern.
  • the area where array antenna 1 is used and the area where array antenna 2 is used may be the same area or may be different areas.
  • the learning data given to the input layer of the learning model GM includes the amplitude pattern of the array antenna when an array antenna other than array antennas 1 and 2 is used, and the excitation amplitude phase error corresponding to the amplitude pattern. It may include.
  • the antenna device shown in FIG. 1 includes an antenna control device 10.
  • the antenna device may not include the antenna control device 10 and the antenna control device 10 may exist on, for example, a cloud server.
  • the learning data acquisition unit 12 uses amplitude patterns P 1 and P 2 of the array antennas 1 and 2 included in the learning data D 1 and D 2 to correspond to the angles of the array antennas 1 and 2.
  • An antenna control device 10 that acquires an amplitude pattern whose amplitude includes amplitude errors ⁇ P 1 and ⁇ P 2 due to the influence of thermal noise will be described.
  • the configuration of the antenna device according to Embodiment 2 is similar to the configuration of the antenna device according to Embodiment 1, and the configuration diagram showing the antenna device according to Embodiment 2 is FIG. 1.
  • Each of the amplitude pattern P 1 of the array antenna 1 and the amplitude pattern P 2 of the array antenna 2 acquired by the error estimation unit 15 is, for example, an amplitude pattern measured by a measuring device (not shown).
  • the amplitude pattern may be affected by thermal noise of a receiver connected to the array antenna 2, for example.
  • the learning data acquisition unit 12 acquires learning data D 1 and D 2 as shown below, and sets the learning data D 1 to The learning data D2 is supplied to the input layer of the learning model GM1 , and the learning data D2 is supplied to the input layer of the learning model GM2 .
  • FIG. 8 is an explanatory diagram showing the amplitude pattern P 1 of the array antenna 1 included in the learning data D 1 or the amplitude pattern P 2 of the array antenna 2 included in the learning data D 2 .
  • the horizontal axis represents the angle [deg. ]
  • the vertical axis is the normalized amplitude [dB].
  • the broken line indicates the amplitude pattern P 1 of the array antenna 1 under ideal conditions without thermal noise, or the amplitude pattern P 2 of the array antenna 2 under ideal conditions without thermal noise.
  • the solid line indicates an amplitude pattern P 1 ′ containing an amplitude error ⁇ P 1 at each angle obtained from the noise model, or an amplitude pattern P 2 ′ containing an amplitude error ⁇ P 2 at each angle obtained from the noise model.
  • the noise model is a model that outputs an amplitude error ⁇ P 1 due to the influence of thermal noise corresponding to the angle of the array antenna 1, or a model that outputs an amplitude error ⁇ P 2 due to the influence of thermal noise corresponding to the angle of the array antenna 2. This is the model to output. Since the noise model itself is a well-known model, detailed explanation will be omitted.
  • the distribution of thermal noise may be any distribution, and for example, a normal distribution may be considered.
  • the learning processing unit 13 acquires learning data D 1 from the learning data acquisition unit 12 .
  • the amplitude pattern P 1 of the array antenna 1 included in the learning data D 1 includes an amplitude error ⁇ P 1 due to the influence of thermal noise.
  • the learning processing unit 13 supplies the learning data D 1 to the input layer of the learning model GM 1 and causes the learning model GM 1 to learn the excitation amplitude phase error E 1,k corresponding to the amplitude pattern P 1 of the array antenna 1.
  • the learning processing unit 13 acquires learning data D 2 from the learning data acquisition unit 12 .
  • the amplitude pattern P 2 of the array antenna 2 included in the learning data D 2 includes an amplitude error ⁇ P 2 due to the influence of thermal noise.
  • the learning processing unit 13 supplies the learning data D 2 to the input layer of the learning model GM 2 and causes the learning model GM 2 to learn the excitation amplitude phase error E 2,g corresponding to the amplitude pattern P 2 of the array antenna 2.
  • the learning processing unit 13 causes the learning model storage unit 14 to store each of the learned learning model GM 1 and the learned learning model GM 2 .
  • the learning data acquisition unit 12 adds heat to the amplitude corresponding to the angle of the array antenna 1 (or 2) as the amplitude pattern of the array antenna 1 (or 2) included in the learning data.
  • the antenna control device 10 was configured to obtain an amplitude pattern that includes an amplitude error due to the influence of noise. Therefore, like the antenna control device 10 according to the first embodiment, the antenna control device 10 according to the second embodiment is capable of suppressing the deviation in the excitation coefficient of each antenna element 1-k (or 2-g). In addition, the influence of thermal noise can be further suppressed than the antenna control device 10 according to the first embodiment.
  • the learning processing unit 13 provides the learning model with an amplitude pattern in the angular range corresponding to the main beam and an excitation amplitude phase error corresponding to the amplitude pattern in the angular range corresponding to the main beam.
  • the control device 10 will be explained.
  • the configuration of the antenna device according to Embodiment 3 is similar to the configuration of the antenna device according to Embodiment 1, and the configuration diagram showing the antenna device according to Embodiment 3 is FIG. 1.
  • the learning processing unit 13 selects an angular range corresponding to the main beam from among the amplitude patterns P1 included in the learning data D1 acquired by the learning data acquisition unit 12. and the excitation amplitude phase error E 1,k corresponding to the amplitude pattern in the angular range corresponding to the main beam are given to the input layer of the learning model GM 1 . Then, the learning processing unit 13 causes the learning model GM 1 to learn the excitation amplitude phase error corresponding to the amplitude pattern given to the input layer.
  • the learning processing unit 13 selects an amplitude pattern in an angular range corresponding to the main beam and an amplitude pattern corresponding to the main beam among the amplitude patterns P2 included in the learning data D2 acquired by the learning data acquisition unit 12.
  • the excitation amplitude phase error E 2,g corresponding to the amplitude pattern in the angular range is given to the input layer of the learning model GM 2 .
  • the learning processing unit 13 causes the learning model GM 2 to learn the excitation amplitude phase error corresponding to the amplitude pattern given to the input layer.
  • FIG. 9 shows the amplitude pattern in the angle range corresponding to the main beam out of the amplitude pattern P1 included in the learning data D1 , or the main beam in the amplitude pattern P2 included in the learning data D2 .
  • FIG. 3 is an explanatory diagram showing an amplitude pattern in an angular range corresponding to a beam.
  • the horizontal axis represents the angle [deg. ]
  • the vertical axis is the normalized amplitude [dB].
  • the amplitude pattern in the angular range corresponding to the main beam is about ⁇ 8 to about +8 [deg. ] is the amplitude pattern of the angle in the range.
  • the amplitude patterns given to the respective input layers of the trained learning model GM 1 and the trained learning model GM 2 are limited to the amplitude patterns in the angular range corresponding to the main beam, thereby reducing the influence of thermal noise. Deterioration of communication performance of array antenna 1 (or 2) can be further suppressed.
  • Each of the amplitude pattern P 1 of the array antenna 1 and the amplitude pattern P 2 of the array antenna 2 may be a two-dimensional pattern in which the respective amplitude patterns on the two cut planes are combined.
  • FIG. 10 is an explanatory diagram showing a two-dimensional pattern in which respective amplitude patterns on two cut surfaces are combined.
  • the horizontal axis represents the angle [deg. ]
  • the vertical axis represents the angle in the elevation direction [deg. ].
  • FIG. 11A is an explanatory diagram showing the amplitude pattern of the horizontal cut surface among the two cut surfaces.
  • the amplitude pattern of the horizontal cut plane is a one-dimensional pattern.
  • FIG. 11B is an explanatory diagram showing the amplitude pattern of the vertical cut surface among the two cut surfaces.
  • the amplitude pattern of the vertical cut plane is a one-dimensional pattern.
  • the example of FIG. 11A shows the amplitude pattern of the horizontal cut surface of the two cut surfaces
  • the example of FIG. 11B shows the amplitude pattern of the vertical cut surface of the two cut surfaces.
  • this is just an example; for example, among two cut surfaces, one cut surface is an oblique cut surface that is inclined from the horizontal cut surface, and the other cut surface is an oblique cut surface that is inclined from the vertical cut surface. It may be a cut surface.
  • the configuration of the antenna device according to Embodiment 4 is similar to the configuration of the antenna device according to Embodiment 1, and the configuration diagram showing the antenna device according to Embodiment 4 is FIG. 1.
  • the learning data acquisition unit 12 acquires, as learning data D1 , a one-dimensional amplitude pattern of one cut surface , a one-dimensional amplitude pattern of the other cut surface, and one of the amplitude patterns P1 of the array antenna 1.
  • Learning data D1 including an excitation amplitude phase error corresponding to the one-dimensional amplitude pattern of the cut surface and an excitation amplitude phase error corresponding to the one-dimensional amplitude pattern of the other cut surface is acquired.
  • the learning data acquisition unit 12 acquires, as learning data D 2 , a one-dimensional amplitude pattern of one cut surface and a one-dimensional amplitude pattern of the other cut surface among the amplitude patterns P 2 of the array antenna 2 .
  • the learning data acquisition unit 12 obtains, as the learning data D1 , a one-dimensional amplitude pattern on the horizontal cut plane and a one-dimensional amplitude pattern on the vertical cut plane among the amplitude patterns P1 of the array antenna 1.
  • Learning data D1 is obtained, which includes an excitation amplitude phase error corresponding to the amplitude pattern of the horizontal cut plane, and an excitation amplitude phase error corresponding to the amplitude pattern of the vertical cut plane.
  • the learning data acquisition unit 12 acquires, as learning data D2 , a one-dimensional amplitude pattern on the horizontal cut plane, a one-dimensional amplitude pattern on the vertical cut plane, and a horizontal cut out of the amplitude pattern P2 of the array antenna 2.
  • Learning data D2 including the excitation amplitude phase error of the antenna element 2-g corresponding to the amplitude pattern of the plane and the excitation amplitude phase error of the antenna element 2-g corresponding to the amplitude pattern of the vertical cut plane. get.
  • the learning processing unit 13 acquires, for example, one or more pieces of learning data D1 as shown in FIG. 11 from the learning data acquisition unit 12.
  • the learning processing unit 13 supplies each learning data D 1 to the input layer of the learning model GM 1 and causes the learning model GM 1 to learn an excitation amplitude phase error corresponding to a one-dimensional amplitude pattern on the horizontal cut plane. Further, the learning processing unit 13 causes the learning model GM 1 to learn the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the vertical cut plane.
  • the learning processing unit 13 when the one-dimensional amplitude pattern on the horizontal cut plane is given to the input layer of the learning model GM 1 , the learning processing unit 13 generates a signal corresponding to the one-dimensional amplitude pattern on the horizontal cut plane from the output layer of the learning model GM 1 .
  • the weights w11-w16 and w21-w26 of the neural network are adjusted so that the excitation amplitude phase error is output.
  • the learning processing unit 13 corresponds to the one-dimensional amplitude pattern on the vertical cut plane from the output layer of the learning model GM 1 .
  • the weights w11-w16 and w21-w26 of the neural network are adjusted so that the excitation amplitude phase error is output.
  • the learning processing unit 13 acquires, for example, one or more learning data D2 as shown in FIG. 11 from the learning data acquisition unit 12.
  • the learning processing unit 13 supplies each learning data D 2 to the input layer of the learning model GM 2 and causes the learning model GM 2 to learn the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the horizontal cut plane. Further, the learning processing unit 13 causes the learning model GM 2 to learn the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the vertical cut plane.
  • the learning processing unit 13 when the one-dimensional amplitude pattern on the horizontal cut plane is given to the input layer of the learning model GM 2 , the learning processing unit 13 generates a signal corresponding to the one-dimensional amplitude pattern on the horizontal cut plane from the output layer of the learning model GM 2 .
  • the weights w11-w16 and w21-w26 of the neural network are adjusted so that the excitation amplitude phase error is output.
  • the learning processing unit 13 corresponds to the one-dimensional amplitude pattern on the vertical cut plane from the output layer of the learning model GM 2 .
  • the weights w11-w16 and w21-w26 of the neural network are adjusted so that the excitation amplitude phase error is output.
  • error estimating unit 15 calculates, for example, a one-dimensional amplitude pattern on the horizontal cut plane and a one-dimensional amplitude pattern on the vertical cut plane as the amplitude pattern P 1 of array antenna 1. Obtain the dimensional amplitude pattern. Further, the error estimation unit 15 obtains, for example, a one-dimensional amplitude pattern on the horizontal cut plane and a one-dimensional amplitude pattern on the vertical cut plane as the amplitude pattern P2 of the array antenna 2.
  • the error estimation unit 15 supplies, as the amplitude pattern P 1 of the array antenna 1, for example, a one-dimensional amplitude pattern on a horizontal cut plane to the input layer of the learned learning model GM 1 , and from the output layer of the learning model GM 1 , An excitation amplitude phase error corresponding to a one-dimensional amplitude pattern on the horizontal cut plane is obtained.
  • the error estimating unit 15 supplies, as the amplitude pattern P 1 of the array antenna 1, a one-dimensional amplitude pattern in the vertical cut plane to the input layer of the learned model GM 1, and outputs the amplitude pattern P 1 of the array antenna 1 to the input layer of the learned model GM 1 From this, the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the vertical cut plane is obtained.
  • the error estimation unit 15 outputs each of the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the horizontal cut plane and the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the vertical cut plane to the excitation coefficient control unit 16. .
  • the error estimation unit 15 supplies, for example, a one-dimensional amplitude pattern on a horizontal cut plane to the input layer of the learned learning model GM 2 as the amplitude pattern P 2 of the array antenna 2, and from the output layer of the learning model GM 2 , An excitation amplitude phase error corresponding to a one-dimensional amplitude pattern on the horizontal cut plane is obtained. Further, the error estimating unit 15 supplies, for example, a one-dimensional amplitude pattern in a vertical cut plane to the input layer of the learned learning model GM 2 as the amplitude pattern P 2 of the array antenna 2, and provides the output layer of the learning model GM 2 with From this, the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the vertical cut plane is obtained.
  • the error estimation unit 15 outputs each of the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the horizontal cut plane and the excitation amplitude phase error corresponding to the one-dimensional amplitude pattern on the vertical cut plane to the excitation coefficient control unit 16. .
  • the excitation coefficient control unit 16 acquires, for example, each of the excitation amplitude phase error related to the horizontal cut plane and the excitation amplitude phase error related to the vertical cut plane with respect to the array antenna 1 from the error estimation unit 15.
  • the excitation coefficients EC 1, H, k at which the excitation amplitude phase error becomes zero are not limited to those at which the excitation amplitude phase error becomes completely zero, but are those where the excitation amplitude phase error is within a range that does not cause any practical problems. This is a concept that includes things that are approximately zero.
  • the excitation coefficient EC 1, V, k at which the excitation amplitude phase error becomes zero is not limited to the one at which the excitation amplitude phase error becomes completely zero, but the excitation amplitude phase error within a range that does not cause any practical problems. This is a concept that includes things that are approximately zero.
  • the excitation coefficient control unit 16 calculates the excitation coefficient EC 1,k by multiplying the excitation distribution indicated by the excitation coefficient EC 1,H,k and the excitation distribution indicated by the excitation coefficient EC 1 ,V ,k .
  • the process of calculating the product of the excitation distribution indicated by the excitation coefficient EC 1, H, k and the excitation distribution indicated by the excitation coefficient EC 1, V, k is a well-known technique, so detailed explanation will be omitted.
  • the excitation coefficient control section 16 outputs the excitation coefficient EC 1,k to the transmission beam forming section 4 .
  • the excitation coefficient control unit 16 acquires, for example, each of the excitation amplitude phase error related to the horizontal cut plane and the excitation amplitude phase error related to the vertical cut plane for the array antenna 2 from the error estimation unit 15.
  • the excitation coefficients EC 2, H, k at which the excitation amplitude phase error becomes zero are not limited to those at which the excitation amplitude phase error becomes completely zero, but are those where the excitation amplitude phase error is within a range that does not cause any practical problems. This is a concept that includes things that are approximately zero.
  • the excitation coefficient EC 2, V, k at which the excitation amplitude phase error becomes zero is not limited to one at which the excitation amplitude phase error becomes completely zero, but is one in which the excitation amplitude phase error is within a range that does not cause any practical problems. This is a concept that includes things that are approximately zero.
  • the excitation coefficient control unit 16 calculates the excitation coefficient EC 2,g by multiplying the excitation distribution indicated by the excitation coefficient EC 2,H,g and the excitation distribution indicated by the excitation coefficient EC 2 ,V ,g .
  • the excitation coefficient control section 16 outputs the excitation coefficient EC 2,g to the reception beam forming section 7 .
  • the amplitude pattern of the array antenna 1 is a two-dimensional pattern in which the respective amplitude patterns on the two cut planes are combined, and the learning data acquisition unit 12 uses the amplitude pattern as the learning data.
  • the antenna control device 10 is configured to acquire learning data including the amplitude pattern of each cut surface and the excitation amplitude phase error corresponding to the amplitude pattern of each cut surface. Therefore, like the antenna control device 10 according to the first embodiment, the antenna control device 10 according to the fourth embodiment is capable of suppressing the deviation in the excitation coefficient of each antenna element 1-k (or 2-g). can. Furthermore, the antenna control device 10 according to the fourth embodiment can reduce the processing load of the learning process in the learning processing section 13 more than the antenna control device 10 according to the first embodiment.
  • the present disclosure is suitable for an antenna control device, an antenna control method, and an antenna device.
  • 1 Array antenna (first array antenna), 1-1 to 1-K antenna elements, 2 Array antenna (second array antenna), 2-1 to 2-G antenna elements, 3 Transmission signal generation section, 4 Transmission Beam forming unit, 5 transmitting unit, 6 receiving unit, 7 receiving beam forming unit, 10 antenna control device, 11 learning device, 12 learning data acquisition unit, 13 learning processing unit, 14 learning model storage unit, 15 error estimation unit, 16 Excitation coefficient control unit, 21 Learning data acquisition circuit, 22 Learning processing circuit, 23 Learning model storage circuit, 24 Error estimation circuit, 25 Excitation coefficient control circuit, 31 Memory, 32 Processor.

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