阅读说明：本技术 面向无人机通信的智能信道测量装置及方法 (Intelligent channel measuring device and method for unmanned aerial vehicle communication ) 是由 王亚文 朱秋明 成能 陈小敏 仲伟志 宁本哲 谢文平 虞湘宾 于 2019-09-09 设计创作，主要内容包括：本发明公开了一种面向无人机通信的智能信道测量装置及方法,其中装置包括无人机单元,信道测量机载单元,信道测量地面接收单元和信道参数估计和建模单元,所述无人机单元中配有“小脑”模块；所述信道测量机载单元包含FPGA基带处理模块,数模转换模块,射频模块,自动增益控制(AGC)模块和天线模块,信道测量机载单元与无人机单元相连；所述信道测量地面接收单元包括天线模块,AGC模块,射频模块,模数转换模块和FPGA信号处理模块；所述信道参数估计和建模单元配备有“大脑”模块,“大脑”模块中安装有无人机数据链分析软件,包含信道参数估计和无人机空地信道建模两个子系统,信道测量地面接收单元与信道参数估计和建模单元相连。(The invention discloses an intelligent channel measuring device and method facing unmanned aerial vehicle communication, wherein the device comprises an unmanned aerial vehicle unit, a channel measuring machine-mounted unit, a channel measuring ground receiving unit and a channel parameter estimation and modeling unit, wherein a cerebellum module is arranged in the unmanned aerial vehicle unit; the channel measurement airborne unit comprises an FPGA baseband processing module, a digital-to-analog conversion module, a radio frequency module, an Automatic Gain Control (AGC) module and an antenna module, and is connected with the unmanned aerial vehicle unit; the channel measurement ground receiving unit comprises an antenna module, an AGC module, a radio frequency module, an analog-to-digital conversion module and an FPGA signal processing module; the channel parameter estimation and modeling unit is provided with a brain module, unmanned aerial vehicle data chain analysis software is installed in the brain module and comprises two subsystems of channel parameter estimation and unmanned aerial vehicle air-ground channel modeling, and the channel measurement ground receiving unit is connected with the channel parameter estimation and modeling unit.)

1. The utility model provides an intelligent channel measuring device towards unmanned aerial vehicle communication which characterized in that: the unmanned aerial vehicle system comprises an unmanned aerial vehicle unit (1-1), a channel measurement airborne unit (1-2), a channel measurement ground receiving unit (1-3) and a channel parameter estimation and modeling unit (1-4), wherein an unmanned aerial vehicle cerebellum module (1-5) is arranged in the unmanned aerial vehicle unit (1-1), the channel measurement airborne unit (1-2) is connected with the unmanned aerial vehicle unit (1-1), the unmanned aerial vehicle cerebellum module (1-5) is shared by the unmanned aerial vehicle and the channel measurement ground receiving unit (1-3), and the channel parameter estimation and modeling unit (1-4) is connected with the channel measurement ground receiving unit (1-3);

the channel measurement airborne unit (1-2) comprises an FPGA baseband processing module, a digital-to-analog conversion module, a radio frequency module and an automatic gain control module, namely an AGC module and an antenna module;

the channel measurement ground receiving unit (1-3) comprises an antenna module, an AGC module, a radio frequency module, an analog-to-digital conversion module and an FPGA signal processing module, wherein the FPGA signal processing module acquires and stores received data and transmits channel measurement data to a channel parameter estimation and modeling unit;

the channel parameter estimation and modeling unit (1-4) is provided with a brain module (1-10), wherein unmanned aerial vehicle data chain analysis software is installed in the brain module, and the brain module comprises two subsystems of channel parameter estimation and unmanned aerial vehicle air-ground channel modeling.

2. The smart channel measurement device for drone communication of claim 1, wherein: modeling unmanned aerial vehicle air-ground channel as

Wherein, P^LoS(t) and τ^LoS(t) is the power and delay in the line-of-sight path;andpower and delay in non-line-of-sight paths, k 2 pi f₀/c₀，f₀And c₀Representing the carrier frequency and the speed of light; phi is a^LoSAnd phi_n,mRandom initial phases are adopted and are uniformly distributed; v. of_tx(t) expressed as a velocity vector of the drone; calculating the spherical unit vector of the mth branch of the sight distance path and the nth non-sight distance pathAnd s_tx,n,m(t) the method is as follows:

wherein the content of the first and second substances,andazimuth and pitch, expressed as a line-of-sight path;anddenotes the nth barAzimuth and pitch angles of the mth branch of the non-line-of-sight path.

3. An intelligent channel measurement method for unmanned aerial vehicle communication is characterized in that: the method comprises the following steps:

firstly, checking system synchronous calibration equipment before measurement, checking whether airborne and ground terminal rubidium clock output time frequency signals are consistent, and checking whether cable connection among modules is normal;

secondly, the user can select the flight modes of the unmanned aerial vehicle, namely 'ground manual control' and 'intelligent air driving'; when the unmanned aerial vehicle is in a ground manual control mode, a user inputs and sets the flight track of the unmanned aerial vehicle, the flight speed and the type of a transmitting signal of the unmanned aerial vehicle, the channel bandwidth and the channel frequency parameter at a ground station module, the unmanned aerial vehicle is operated through the ground flight control module, and after the flight is stable, a channel measurement airborne unit carries out analog-to-digital conversion, up-conversion and AGC gain control on a baseband signal to transmit a signal meeting the user requirement; when the unmanned aerial vehicle is in an 'aerial intelligent driving' mode, a 'cerebellum' module (1-5) of the unmanned aerial vehicle integrates surrounding geographic environment information and receives feedback information of a ground station, the track, the speed and parameters related to a transmitting signal of the unmanned aerial vehicle are set autonomously, and when the channel state is severe, a channel measurement ground receiving unit sends a feedback instruction to the unmanned aerial vehicle, so that the unmanned aerial vehicle reduces the flight speed and even is in a hovering state, and the transmitting signal is ensured to be received smoothly;

thirdly, configuring parameters of a channel measurement ground receiving unit by a user, and issuing a command for acquiring and storing channel data to the channel measurement ground receiving unit (1-3) by a brain module (1-10); when a received signal passes through the AGC modules (1-14), when the signal power value is smaller than a threshold value set by the brain, the AGC modules (1-14) provide a gain value for the current signal power value; after the received signal passes through a down-conversion chip and an AD chip, a complex baseband signal is obtained; after the steps of filtering, capturing and the like of the FPGA data module (1-15), the stored data is transmitted back to the brain module (1-10) through the PCIE bus;

and fourthly, processing the original data by channel parameter estimation software by using a brain module (1-10), calculating power, path number, angle parameters and time delay parameters, and returning the parameter values to an unmanned aerial vehicle air-ground channel model to complete the parameter measurement and model establishment process of the unmanned aerial vehicle air-ground channel.

4. The smart channel measurement method for drone communication of claim 3, wherein: the estimation method of the power, the path number, the angle parameter and the time delay parameter comprises the following steps:

1. power of

In the first step, the channel impulse response h (t, tau) of the unmanned aerial vehicle is obtained from the data of the received signal and the data of the known transmitting signal_n) Calculating the time delay power spectrum P_n(t)；

P_n(t)＝||h(t,τ_n)||²,n＝0,1,2,...,N(t) (4)

Wherein | · | purple sweet²2-norm operation;

and step two, calculating to obtain initial power by using the power time delay spectrum obtained in the step (4), removing gains of an antenna, AGC (automatic gain control) and cable equipment, and calculating corrected power P'_n(t)；

P′_n(t)＝-10lgP(t,τ_n)+G_t+G_r+G_AGC+G_cable (5)

Thirdly, calculating the power P of the line-of-sight path and the non-line-of-sight path^LoS(t) and

P^LoS(t)＝P′₀(t,τ₀) (6)

2. number of paths

Step one, calculating a maximum value of a power time delay spectrum;

second, record aggregationThe number of non-line-of-sight paths is

3. Angle and time delay

First, parameters of channel estimationAndperforming hypothesis, processing the likelihood function according to complete data, estimating unknown missing data on the basis of the existing hypothesis data through maximum expectation estimation of equations (9) - (12), and obtaining new estimators theta 'and omega';

secondly, iterating the above-mentioned formula estimators theta 'and omega' to the first step operation, and further solving new estimators theta 'and omega';

thirdly, iterating the second step until the proposed channel parameters theta and omega do not change any more to obtain a channel estimation valueAnd

The technical field is as follows:

the invention relates to the unmanned aerial vehicle communication technology, in particular to an intelligent channel measuring device and method for unmanned aerial vehicle communication, and belongs to the field of wireless information transmission.

Background art:

the unmanned aerial vehicle has the characteristics of simple structure, dynamic deployment, low manufacturing cost and casualties reduction, and plays an extremely important role in various fields. The unmanned aerial vehicle needs to continuously transmit data with the ground control center through a communication link in the flight process. At present, the communication problem is an important bottleneck puzzling the development of unmanned aerial vehicles. According to statistics, in most of unmanned aerial vehicle crash events, the network communicated with the ground is interfered and interrupted as a main reason. Therefore, accurately researching the radio wave propagation characteristics of the unmanned aerial vehicle is important for designing a stable and reliable unmanned aerial vehicle communication system.

The unmanned aerial vehicle channel measurement is a direct way for knowing channel characteristics, can provide original data for describing the electromagnetic environment of the unmanned aerial vehicle, and describes the unmanned aerial vehicle signal change process under different propagation environments. In recent years, commercial channel detection systems widely used for channel measurement are mainly an Elektrobit prosbound CS system in finland, a measurement platform based on a vector network analyzer and an NTT DOCOMO channel detector, but these systems are heavy in equipment, are not suitable for being carried by an unmanned aerial vehicle, have the defect of small dynamic measurement range, cannot eliminate fuselage jitter, and bring a lot of obstacles to unmanned aerial vehicle channel measurement. It is worth noting that there is no case in the current industry to combine measured drone data with a channel theoretical model of drone. Therefore, it is necessary to design a channel measurement method and device for drone communication, which integrate the channel measurement and channel modeling functions.

The invention content is as follows:

the invention provides an intelligent channel measuring device and method for unmanned aerial vehicle communication, aiming at solving the problems in the prior art, the device and method can accurately measure the wireless communication link condition of an unmanned aerial vehicle in the flight process and model the time-varying fading caused by the propagation environment of the unmanned aerial vehicle, and are suitable for testing and verifying the equipment performance of an unmanned aerial vehicle communication system.

The invention adopts the following technical scheme: an intelligent channel measuring device facing unmanned aerial vehicle communication comprises an unmanned aerial vehicle unit, a channel measuring vehicle-mounted unit, a channel measuring ground receiving unit and a channel parameter estimation and modeling unit, wherein an unmanned aerial vehicle cerebellum module is arranged in the unmanned aerial vehicle unit, the channel measuring vehicle-mounted unit is connected with the unmanned aerial vehicle unit, the unmanned aerial vehicle cerebellum module and the channel measuring vehicle-mounted unit share one unmanned aerial vehicle cerebellum module, the channel measuring ground receiving unit is connected with the channel parameter estimation and modeling unit, and the unmanned aerial vehicle unit further comprises an aerial GPS module;

the channel measurement airborne unit comprises an FPGA baseband processing module, a digital-to-analog conversion module, a radio frequency module and an automatic gain control module, namely an AGC module and an antenna module;

the channel measurement ground receiving unit comprises an antenna module, an AGC module, a radio frequency module, an analog-to-digital conversion module and an FPGA signal processing module, wherein the FPGA signal processing module acquires and stores received data and transmits channel measurement data to a channel parameter estimation and modeling unit;

the channel parameter estimation and modeling unit is provided with a brain module, wherein unmanned aerial vehicle data chain analysis software is installed in the brain module and comprises two sub-modules of channel parameter estimation and unmanned aerial vehicle air-ground channel modeling.

3. An intelligent channel measurement method facing unmanned aerial vehicle communication comprises the following steps:

firstly, checking system synchronous calibration equipment before measurement, checking whether airborne and ground terminal rubidium clock output time frequency signals are consistent, and checking whether cable connection among modules is normal;

secondly, the user can select the flight modes of the unmanned aerial vehicle, namely 'ground manual control' and 'intelligent air driving'; when the unmanned aerial vehicle is in a ground manual control mode, a user inputs and sets the flight track of the unmanned aerial vehicle, the flight speed and the type of a transmitting signal of the unmanned aerial vehicle, the channel bandwidth and the channel frequency parameter at a ground station module, the unmanned aerial vehicle is operated through the ground flight control module, and after the flight is stable, a channel measurement airborne unit carries out analog-to-digital conversion, up-conversion and AGC gain control on a baseband signal to transmit a signal meeting the user requirement; when the unmanned aerial vehicle is in an 'aerial intelligent driving' mode, the 'cerebellum' module of the unmanned aerial vehicle integrates surrounding geographic environment information and receives feedback information of a ground station, the track, the speed and parameters related to a transmitted signal of the unmanned aerial vehicle are set autonomously, and when the channel state is severe, the channel measurement ground receiving unit sends a feedback instruction to the unmanned aerial vehicle, so that the unmanned aerial vehicle reduces the flight speed and even is in a hovering state, and smooth reception of the transmitted signal is ensured;

thirdly, configuring parameters of a channel measurement ground receiving unit by a user, and issuing a command of acquiring and storing channel data to the channel measurement ground receiving unit by a brain module; when a received signal passes through the AGC module, when the signal power value is smaller than a threshold value set by the brain, the AGC module provides a gain value for the current signal power value; after the received signal passes through a down-conversion chip and an AD chip, a complex baseband signal is obtained; after the steps of filtering, capturing and the like of the FPGA data module, the stored data is transmitted back to the brain module through the PCIE bus;

and fourthly, processing the original data by channel parameter estimation software by using a brain module, calculating power, path number, angle parameters and time delay parameters, and returning the parameter values to the unmanned aerial vehicle air-ground channel model to complete the parameter measurement and model establishment process of the unmanned aerial vehicle air-ground channel.

The invention has the following beneficial effects:

1) the invention provides an intelligent channel measuring device and method for unmanned aerial vehicle communication, which can reduce jitter interference and increase the dynamic range of channel measurement, and are particularly suitable for air-ground channel measurement in a complex environment.

2) The invention provides an intelligent channel measuring device and method for unmanned aerial vehicle communication, which support the application of measured data to a theoretical model and the real-time establishment of an unmanned aerial vehicle air-ground channel model in a specific scene.

Description of the drawings:

fig. 1 is a schematic structural diagram of an unmanned aerial vehicle intelligent channel measuring device.

Fig. 2 is a flow chart of the unmanned aerial vehicle intelligent channel measuring device of the invention.

Fig. 3 is a schematic diagram of the internal structure of the airborne transmitting unit of the unmanned aerial vehicle intelligent channel measuring device.

Fig. 4 is a schematic diagram of the internal structure of the ground receiving unit of the unmanned aerial vehicle intelligent channel measuring device.

The specific implementation mode is as follows:

the invention will be further described with reference to the accompanying drawings.

As shown in figure 1, the intelligent channel measuring device facing unmanned aerial vehicle communication comprises an unmanned aerial vehicle unit (1-1), a channel measurement airborne unit (1-2), a channel measurement ground receiving unit (1-3) and a channel estimation and modeling unit (1-4). The unmanned aerial vehicle unit 1-1 is provided with a microcomputer, can be called as an unmanned aerial vehicle cerebellum (1-5) in the device, has the functions of recognizing the surrounding environment and deciding flight instructions, and is the key for realizing intelligent measurement. The cerebellum receives the flight control module (1-11) instruction to control the unmanned aerial vehicle to fly, and when the cerebellum receives the bad feedback of the received signal sent by the ground end, the cerebellum sends an instruction to reduce the flight speed of the unmanned aerial vehicle and increase the rotating speed of the propeller to keep the attitude of the unmanned aerial vehicle stable. In addition, the cerebellum management channel measures the parameters of the transmitting signals of the airborne unit, and the type, the channel bandwidth and the channel frequency of the transmitting signals are configured according to the requirements of users and the site environment. The unmanned aerial vehicle unit (1-1) also comprises an air GPS module (1-6) which can provide a time service signal for a rubidium clock in a channel synchronization system. The channel measurement airborne unit (1-2) is connected with the unmanned aerial vehicle unit (1-1), and the channel measurement airborne unit and the unmanned aerial vehicle unit share one unmanned aerial vehicle cerebellum module (1-5) for controlling the flight of the unmanned aerial vehicle and setting parameters of transmitted signals; the channel measurement ground receiving unit (1-3) is connected with the channel estimation and modeling unit (1-4), and the brain is responsible for managing the two parts and is used for receiving the unmanned aerial vehicle transmitting signal and estimating the unmanned aerial vehicle channel parameters and establishing an unmanned aerial vehicle air-ground channel model in the scene in real time.

The channel measurement airborne unit (1-2) comprises an FPGA baseband processing module (1-12), a digital-to-analog conversion module, a radio frequency module, an Automatic Gain Control (AGC) module (1-13) and an antenna module. The AGC module can automatically adjust the gain of the received signal along with the signal strength, thereby further expanding the measurement range. An onboard channel measuring module (1-7) of a channel measuring onboard unit (1-2) is arranged in the unmanned aerial vehicle nacelle through a board card, and an antenna is arranged on a three-dimensional rotating cradle head and used for eliminating the influence caused by airplane shaking.

The channel measurement ground receiving unit (1-3) comprises an antenna module, an AGC module (1-14), a radio frequency module, an analog-to-digital conversion module and an FPGA signal processing module (1-15) and is used for receiving signals transmitted by the unmanned aerial vehicle signal channel transmitting unit which is propagated through the air and the ground. The channel measurement ground receiving module (1-9) board card of the channel measurement ground receiving unit (1-3) is connected with the channel parameter estimation and modeling unit to transmit channel measurement data. The channel measurement ground receiving unit (1-3) also comprises a ground GPS module (1-8).

The channel parameter estimation and modelling unit (1-4) is equipped with a PC, referred to in the present device as a "brain" module (1-10). The brain is the core of the measuring device and is responsible for coordinating and scheduling the autonomous flight state of the unmanned aerial vehicle, managing the channel measurement ground receiving unit and analyzing and processing the channel measurement original data. The brain is provided with unmanned aerial vehicle data chain analysis software which comprises two subsystems of channel parameter estimation and unmanned aerial vehicle air-ground channel modeling. The unmanned aerial vehicle air-ground channel model under the measurement scene can be locally established in real time by estimating the measurement data by the brain and iterating the obtained channel parameters to a theoretical channel model.

In the invention, the unmanned aerial vehicle air-ground channel is modeled as

Wherein, P^LoS(t) and τ^LoS(t) is the power and delay in the line-of-sight path;andpower and time delay in non-line-of-sight paths. k 2 pi f₀/c₀，f₀And c₀Representing the carrier frequency and the speed of light; phi is a^LoSAnd phi_n,mRandom initial phases are adopted and are uniformly distributed; v. of_tx(t) expressed as a velocity vector of the drone; calculating the spherical unit vector of the mth branch of the sight distance path and the nth non-sight distance pathAnd s_tx,n,m(t) the method is as follows:

wherein the content of the first and second substances,andazimuth and pitch, expressed as a line-of-sight path;andthe azimuth and elevation angles of the mth branch of the nth non-line-of-sight path are shown.

The invention relates to an intelligent channel measuring method for unmanned aerial vehicle communication, which comprises the following steps:

firstly, checking system synchronous calibration equipment before measurement, comparing whether rubidium clock output time frequency signals time-service by an air GPS module (1-6) and a ground GPS module (1-8) are consistent or not, and checking whether cable connection between the modules is normal or not;

and secondly, the user starts the power supply of the unmanned aerial vehicle, and when the ground manual control mode is selected, the user controls the unmanned aerial vehicle to fly according to the position set by the user through the flight control modules 1-11. When the mode of 'intelligent driving in the air' is selected, the unmanned aerial vehicle recognizes the surrounding geographic environment and receives feedback information of the ground receiving end. The unmanned aerial vehicle cerebellum module (1-5) transmits parameters shown in a table 1, and meanwhile, the first GPS module (1-6) transmits three-dimensional geographic position information of the unmanned aerial vehicle to the unmanned aerial vehicle cerebellum module (1-5) in a data frame mode. Software control flow information reaches an airborne channel measuring module (1-7), an FPGA data module (1-12) generates a baseband signal of a pseudorandom sequence with a specified code length, the baseband signal is subjected to interpolation pulse shaping, an analog signal is converted into a digital signal through DA chip conversion, and an 800MHz radio frequency signal is formed after up-conversion. When the radio frequency signal passes through the AGC modules (1-13), the power value of the current signal is read, and whether the power value for starting the AGC unit is reached is judged. When the power of the transmitted signal is smaller than the threshold value set by the cerebellum, the AGC module (1-13) increases the power value of the transmitted signal, and finally the transmitted signal is transmitted through the antenna. Starting a flight working mode of an airborne three-dimensional holder of the unmanned aerial vehicle, improving the stability and reducing the influence caused by the self-shaking of the unmanned aerial vehicle;

table 1 transmit signal parameter settings

Parameter(s)	Numerical value
		Carrier frequency	800MHz
Bandwidth of	30MHz
		Type of transmitted signal	PN sequence
Length of transmitted signal	2μs
		Maximum sampling rate of transmitter	300MHz
Sampling interval	10ms

Thirdly, configuring parameters of a channel measurement ground receiving unit by a user, and issuing a command for acquiring and storing channel data to the channel measurement ground receiving unit (1-3) by a brain module (1-10); when a received signal passes through the AGC modules (1-14), when the signal power value is smaller than a threshold value set by the brain, the AGC modules (1-14) provide a gain value for the current signal power value; after the received signal passes through a down-conversion chip and an AD chip, a complex baseband signal is obtained; after the steps of filtering, capturing and the like of the FPGA data module (1-15), the stored data is transmitted back to the brain module (1-10) through the PCIE bus;

fourthly, processing unmanned aerial vehicle channel measurement data by using unmanned aerial vehicle data chain software in brain modules (1-10), combining original data such as GPS geographic position, attitude and the like, and obtaining power P from the preprocessed data through channel parameter estimation software^LoS(t) andtime delay tau^LoS(t) andangle of rotationAndsubstituting the channel parameters and the flight speed parameters of the unmanned aerial vehicle into a brain module to obtain an unmanned aerial vehicle air-ground channel model. The embodiment takes power, path number, time delay and angle spread as examples, and the parameter estimation method is as follows:

1. power of

In the first step, the channel impulse response h (t, tau) of the unmanned aerial vehicle is obtained from the data of the received signal and the data of the known transmitting signal_n) Calculating the time delay power spectrum P_n(t)；

P_n(t)＝||h(t,τ_n)||²,n＝0,1,2,...,N(t) (16)

Wherein | · | purple sweet²2-norm operation;

and step two, calculating to obtain initial power by using the power time delay spectrum obtained in the step (16), removing gains of equipment such as an antenna, AGC (automatic gain control) and a cable and calculating corrected power P'_n(t)；

P′_n(t)＝-10lgP(t,τ_n)+G_t+G_r+G_AGC+G_cable (17)

Thirdly, calculating the power P of the line-of-sight path and the non-line-of-sight path^LoS(t) and

P^LoS(t)＝P′₀(t,τ₀) (18)

2. number of paths

Step one, calculating a maximum value of a power time delay spectrum;

second, record aggregationThe number of non-line-of-sight paths is

3. Angle and time delay

First, parameters of channel estimationAndperforming hypothesis, processing the likelihood function according to complete data, estimating unknown missing data on the basis of the existing hypothesis data through maximum expectation estimation of equations (9) - (12), and obtaining new estimators theta 'and omega';

secondly, iterating the above-mentioned formula estimators theta 'and omega' to the first step operation, and further solving new estimators theta 'and omega';

thirdly, iterating the second step until the proposed channel parameters theta and omega do not change any more to obtain a channel estimation valueAnd

the foregoing is only a preferred embodiment of this invention and it should be noted that modifications can be made by those skilled in the art without departing from the principle of the invention and these modifications should also be considered as the protection scope of the invention.