CN111740796A - Method and device for predicting electromagnetic interference situation of UAV data link - Google Patents

Method and device for predicting electromagnetic interference situation of UAV data link Download PDF

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CN111740796A
CN111740796A CN202010506804.4A CN202010506804A CN111740796A CN 111740796 A CN111740796 A CN 111740796A CN 202010506804 A CN202010506804 A CN 202010506804A CN 111740796 A CN111740796 A CN 111740796A
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CN111740796B (en
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陈亚洲
张冬晓
赵敏
程二威
王玉明
马立云
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PLA University of Science and Technology
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Abstract

The invention is suitable for the technical field of unmanned aerial vehicle communication, and provides a method and a device for predicting the electromagnetic interference situation of an unmanned aerial vehicle data link, wherein the method comprises the following steps: acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle; inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold; and determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold value. This application establishes the relevant of developments with the unmanned aerial vehicle electromagnetic parameter that obtains electromagnetic interference effect threshold value and detection and environmental interference data through gaussian process regression prediction model, and prediction unmanned aerial vehicle that can be dynamic electromagnetic interference effect threshold value under different operating condition improves unmanned aerial vehicle electromagnetic interference situation and judges the accuracy to improve unmanned aerial vehicle's initiative interference killing feature.

Description

无人机数据链电磁干扰态势预测方法及装置Method and device for predicting electromagnetic interference situation of UAV data link

技术领域technical field

本发明属于无人机通信技术领域,尤其涉及一种无人机数据链电磁干扰态势预测方法及装置。The invention belongs to the technical field of unmanned aerial vehicle communication, and in particular relates to a method and a device for predicting the electromagnetic interference situation of an unmanned aerial vehicle data link.

背景技术Background technique

无人机是一种由无线电遥控设备或者自身预先设定程序控制的无人驾驶飞行器,无人机严重依赖信息链路,复杂电磁干扰环境下无人机装备的安全可靠性是无人机通信领域的一大难题。当前无人机装备抗电磁干扰能力较弱,突出体现在上行数据链路容易受到外界电磁辐射干扰,导致地空通信中断,严重威胁无人机的飞行安全。UAV is a kind of unmanned aerial vehicle controlled by radio remote control equipment or its own preset program. UAV relies heavily on information links. The safety and reliability of UAV equipment in complex electromagnetic interference environment is UAV communication. a major problem in the field. At present, the anti-electromagnetic interference capability of UAV equipment is weak, which is prominently reflected in the fact that the uplink data link is easily interfered by external electromagnetic radiation, resulting in interruption of ground-air communication and a serious threat to the flight safety of UAVs.

现有技术中,通常采用试验得到的电磁干扰效应阈值判断无人机信息链路当前的电磁干扰态势,然而,在无人机实际飞行过程中,数据链状态持续动态变化,机载天线接收的工作信号功率不断改变,且外界干扰信号频率随机出现。造成无人机电磁干扰态势判断准确性低下,无人机抗干扰能力弱的问题。In the prior art, the electromagnetic interference effect threshold obtained by experiment is usually used to judge the current electromagnetic interference situation of the UAV information link. The power of the working signal is constantly changing, and the frequency of the external interference signal appears randomly. This results in the problem of low accuracy of UAV electromagnetic interference situation judgment and weak UAV anti-jamming ability.

发明内容SUMMARY OF THE INVENTION

有鉴于此,本发明实施例提供了一种无人机数据链电磁干扰态势预测方法及装置,以解决现有技术中无人机电磁干扰态势判断准确性低下的问题。In view of this, the embodiments of the present invention provide a method and device for predicting the electromagnetic interference situation of the UAV data link, so as to solve the problem of low accuracy in judging the electromagnetic interference situation of the UAV in the prior art.

本发明实施例的第一方面提供了一种无人机数据链电磁干扰态势预测方法,包括:A first aspect of the embodiments of the present invention provides a method for predicting the electromagnetic interference situation of a UAV data link, including:

获取无人机的电磁参数和环境干扰数据;Obtain the electromagnetic parameters and environmental interference data of the UAV;

将所述无人机的所述电磁参数和所述环境干扰数据输入高斯过程回归预测模型,得到电磁干扰效应阈值;Inputting the electromagnetic parameters of the UAV and the environmental interference data into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold;

根据所述环境干扰数据和所述电磁干扰效应阈值,确定所述无人机数据链所处的电磁干扰态势。Determine the electromagnetic interference situation in which the UAV data link is located according to the environmental interference data and the electromagnetic interference effect threshold.

本发明实施例的第二方面提供了一种无人机数据链电磁干扰态势预测装置,包括:A second aspect of the embodiments of the present invention provides a device for predicting the electromagnetic interference situation of a UAV data link, including:

电磁数据获取模块,用于获取无人机的电磁参数和环境干扰数据;The electromagnetic data acquisition module is used to acquire the electromagnetic parameters and environmental interference data of the UAV;

阈值获取模块,用于将所述无人机的所述电磁参数和所述环境干扰数据输入高斯过程回归预测模型,得到电磁干扰效应阈值;a threshold value acquisition module, configured to input the electromagnetic parameters of the UAV and the environmental interference data into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold;

电磁干扰态势确定模块,用于根据所述环境干扰数据和所述电磁干扰效应阈值,确定所述无人机数据链所处的电磁干扰态势。An electromagnetic interference situation determination module, configured to determine the electromagnetic interference situation in which the UAV data link is located according to the environmental interference data and the electromagnetic interference effect threshold.

本发明实施例的第三方面提供了一种自适应预测终端,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序,所述处理器执行所述计算机程序时实现如上所述无人机数据链电磁干扰态势预测方法的步骤。A third aspect of the embodiments of the present invention provides an adaptive prediction terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer In the program, the steps of the above-mentioned method for predicting the electromagnetic interference situation of the UAV data link are realized.

本发明实施例的第四方面提供了一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,所述计算机程序被处理器执行时实现如上所述无人机数据链电磁干扰态势预测方法的步骤。A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, realizes the above-mentioned electromagnetic interference of the UAV data link The steps of the situation prediction method.

本发明实施例与现有技术相比存在的有益效果是:本申请首先获取无人机的电磁参数和环境干扰数据;然后将所述无人机的所述电磁参数和所述环境干扰数据输入高斯过程回归预测模型,得到电磁干扰效应阈值;最后根据所述环境干扰数据和所述电磁干扰效应阈值,确定所述无人机数据链所处的电磁干扰态势。本申请通过高斯过程回归预测模型将电磁干扰效应阈值和检测得到的无人机电磁参数与环境干扰数据建立动态的关联,能够动态的预测无人机在不同工作状态下的电磁干扰效应阈值,提高无人机电磁干扰态势判断准确性,从而提高无人机的主动抗干扰能力。Compared with the prior art, the embodiments of the present invention have the following beneficial effects: the present application first obtains the electromagnetic parameters and environmental interference data of the UAV; and then inputs the electromagnetic parameters and the environmental interference data of the UAV. The Gaussian process regression prediction model is used to obtain the electromagnetic interference effect threshold; finally, the electromagnetic interference situation in which the UAV data link is located is determined according to the environmental interference data and the electromagnetic interference effect threshold. The present application establishes a dynamic correlation between the electromagnetic interference effect threshold and the detected UAV electromagnetic parameters and environmental interference data through the Gaussian process regression prediction model, which can dynamically predict the electromagnetic interference effect threshold of the UAV under different working states, and improve the UAV electromagnetic interference situation judgment accuracy, so as to improve the UAV's active anti-jamming ability.

附图说明Description of drawings

为了更清楚地说明本发明实施例中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present invention. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

图1是本发明实施例提供的无人机数据链系统的结构示意图。FIG. 1 is a schematic structural diagram of a UAV data link system provided by an embodiment of the present invention.

图2是本发明实施例提供的无人机数据链电磁干扰态势预测方法的流程示意图;2 is a schematic flowchart of a method for predicting an electromagnetic interference situation of a UAV data link provided by an embodiment of the present invention;

图3是本发明实施例提供的无人机数据链电磁干扰态势预测方法的另一实现流程示意图;3 is a schematic flowchart of another implementation of the method for predicting the electromagnetic interference situation of the UAV data link provided by the embodiment of the present invention;

图4是本发明实施例提供的图2中S102的流程示意图;FIG. 4 is a schematic flowchart of S102 in FIG. 2 according to an embodiment of the present invention;

图5是本发明实施例提供的无人机数据链出现误码时输入数据与输出数据之间的关系曲线示意图;5 is a schematic diagram of a relationship curve between input data and output data when an error occurs in a UAV data link provided by an embodiment of the present invention;

图6是本发明实施例提供的无人机数据链出现失锁时输入数据与输出数据之间的关系曲线示意图;6 is a schematic diagram of a relationship curve between input data and output data when the UAV data link is out of lock according to an embodiment of the present invention;

图7是本发明实施例提供的数据链出现误码时电磁干扰效应阈值的高斯过程回归训练误差示意图;7 is a schematic diagram of a Gaussian process regression training error of an electromagnetic interference effect threshold when a bit error occurs in a data link provided by an embodiment of the present invention;

图8是本发明实施例提供的数据链出现失锁时电磁干扰效应阈值的高斯过程回归训练误差示意图;8 is a schematic diagram of a Gaussian process regression training error of an electromagnetic interference effect threshold when a data link loses lock provided by an embodiment of the present invention;

图9是本发明实施例提供的无人机数据链电磁干扰态势预测装置的结构示意图。FIG. 9 is a schematic structural diagram of a device for predicting an electromagnetic interference situation of a UAV data link provided by an embodiment of the present invention.

具体实施方式Detailed ways

以下描述中,为了说明而不是为了限定,提出了诸如特定系统结构、技术之类的具体细节,以便透彻理解本发明实施例。然而,本领域的技术人员应当清楚,在没有这些具体细节的其它实施例中也可以实现本发明。在其它情况中,省略对众所周知的系统、装置、电路以及方法的详细说明,以免不必要的细节妨碍本发明的描述。In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are set forth in order to provide a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

为了说明本发明所述的技术方案,下面通过具体实施例来进行说明。In order to illustrate the technical solutions of the present invention, the following specific embodiments are used for description.

如图1所示,图1示出了本实施例提供的无人机数据链系统的结构,包括地面子系统和机载子系统92;其中地面子系统包括地面终端911、第一双工器912和第一天线913,机载子系统92包括第二双工器921、机载数据链922、电磁干扰环境监测硬件923、自适应预测终端924和第二天线925。As shown in FIG. 1, FIG. 1 shows the structure of the UAV data link system provided in this embodiment, including a ground subsystem and an airborne subsystem 92; wherein the ground subsystem includes a ground terminal 911, a first duplexer 912 and a first antenna 913 , the airborne subsystem 92 includes a second duplexer 921 , an airborne data link 922 , electromagnetic interference environment monitoring hardware 923 , an adaptive prediction terminal 924 and a second antenna 925 .

在本实施例中,地面子系统用于无人机地面遥控、无人机电磁环境监测显示以及遥测显示,地面子系统通过第一双工器912和第二双工器921与机载子系统92通信。In this embodiment, the ground subsystem is used for drone ground remote control, drone electromagnetic environment monitoring and display, and telemetry display, and the ground subsystem communicates with the airborne subsystem through the first duplexer 912 and the second duplexer 921 92 Communications.

具体地,机载数据链922为现有的机载数据链,包括机载发射端、机载接收端和数据终端;本实施例提供的无人机数据链电磁干扰态势预测方法应用于自适应预测终端924中。自适应预测终端924与无人机飞控系统连接,用于将自身生成的抗干扰响应措施发送至无人机飞控系统。Specifically, the airborne data link 922 is an existing airborne data link, including an airborne transmitting end, an airborne receiving end and a data terminal; the method for predicting the electromagnetic interference situation of the UAV data link provided in this embodiment is applied to adaptive in the prediction terminal 924. The adaptive prediction terminal 924 is connected to the UAV flight control system, and is used for sending the anti-jamming response measures generated by itself to the UAV flight control system.

进一步地,电磁干扰环境监测硬件923用于获取无人机的电磁参数及环境干扰数据。电磁干扰环境监测硬件923包括功分器、补偿电路、监测平台和存储器,其中功分器与补偿电路连接,补偿电路与监测平台连接,监测平台与存储器连接,功分器接收双工器发送的信号,并通过补偿电路发送至监测平台及机载数据链922的机载接收端,监测平台将获取的信号存储至存储器中。Further, the electromagnetic interference environment monitoring hardware 923 is used to obtain electromagnetic parameters and environmental interference data of the UAV. The electromagnetic interference environment monitoring hardware 923 includes a power divider, a compensation circuit, a monitoring platform and a memory, wherein the power divider is connected with the compensation circuit, the compensation circuit is connected with the monitoring platform, the monitoring platform is connected with the memory, and the power divider receives the data sent by the duplexer. The signal is sent to the monitoring platform and the onboard receiving end of the onboard data link 922 through the compensation circuit, and the monitoring platform stores the acquired signal in the memory.

如图2所示,图2示出了本发明实施例提供的无人机数据链电磁干扰态势预测方法的流程,其过程详述如下:As shown in FIG. 2, FIG. 2 shows the process of the method for predicting the electromagnetic interference situation of the UAV data link provided by the embodiment of the present invention, and the process is described in detail as follows:

S101:获取无人机的电磁参数和环境干扰数据。S101: Obtain the electromagnetic parameters and environmental interference data of the UAV.

本实施例的流程主体为机载数据链的自适应预测终端924,自适应预测终端924首先获取电磁干扰环境监测硬件发送的无人机的电磁参数和环境干扰数据。无人机的电磁参数包括工作信号功率、工作信号频率和当前AGC电压。环境干扰数据包括电磁干扰功率和电磁干扰频率。The main body of the process in this embodiment is the adaptive prediction terminal 924 of the airborne data link. The adaptive prediction terminal 924 first obtains the electromagnetic parameters and environmental interference data of the drone sent by the electromagnetic interference environment monitoring hardware. The electromagnetic parameters of the UAV include working signal power, working signal frequency and current AGC voltage. Environmental interference data includes EMI power and EMI frequency.

具体地,AGC电压为接收机中频放大单元中自动增益控制电路内部电调衰减器控制的传导信号衰减幅值,该电路结构能够控制输出的中频信号功率稳定于固定值,电压值反映了接收信号的强度,即接收信号越强,需要的电调衰减量越大,AGC电压也越高。Specifically, the AGC voltage is the attenuation amplitude of the conducted signal controlled by the ESC in the automatic gain control circuit in the receiver intermediate frequency amplifier unit. This circuit structure can control the output intermediate frequency signal power to stabilize at a fixed value, and the voltage value reflects the received signal. , that is, the stronger the received signal, the greater the required ESC attenuation, and the higher the AGC voltage.

S102:将所述无人机的所述电磁参数和所述环境干扰数据输入高斯过程回归预测模型,得到电磁干扰效应阈值。S102: Input the electromagnetic parameters of the UAV and the environmental interference data into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold.

在本实施例中,无人机飞行过程中,数据链状态持续动态变化,机载天线接收的工作信号功率不断改变,此外,外界干扰信号频率随机出现,所以无法通过试验得到数据链所有工作状态下任意潜在干扰频率信号造成的电磁干扰效应阈值。因此,必须找出电磁干扰效应阈值与工作信号功率和干扰信号频率之间的关联性。In this embodiment, during the flight of the UAV, the state of the data link continues to change dynamically, and the power of the working signal received by the airborne antenna changes constantly. In addition, the frequency of the external interference signal appears randomly, so it is impossible to obtain all the working states of the data link through experiments. The threshold of electromagnetic interference effect caused by any potential interference frequency signal. Therefore, it is necessary to find the correlation between the threshold value of electromagnetic interference effect and the power of the working signal and the frequency of the interference signal.

本实施例采用高斯过程回归方法建模,建立电磁干扰效应阈值与工作信号功率和干扰信号频率之间的关联性,具有易实现,非参数推断、超参数自适应获取和预测输出具有概率意义等特点。This embodiment adopts the Gaussian process regression method for modeling to establish the correlation between the electromagnetic interference effect threshold and the power of the working signal and the frequency of the interference signal. Features.

S103:根据所述环境干扰数据和所述电磁干扰效应阈值,确定所述无人机数据链所处的电磁干扰态势。S103: Determine the electromagnetic interference situation in which the UAV data link is located according to the environmental interference data and the electromagnetic interference effect threshold.

从上述实施例可知,本申请首先获取无人机的电磁参数和环境干扰数据;然后将所述无人机的所述电磁参数和所述环境干扰数据输入高斯过程回归预测模型,得到电磁干扰效应阈值;最后根据所述环境干扰数据和所述电磁干扰效应阈值,确定所述无人机数据链所处的电磁干扰态势。本申请通过高斯过程回归预测模型将电磁干扰效应阈值和检测得到的无人机电磁参数与环境干扰数据建立动态的关联,能够动态的预测无人机在不同工作状态下的电磁干扰效应阈值,提高无人机电磁干扰态势判断准确性,从而提高无人机的主动抗干扰能力。It can be seen from the above embodiments that the present application first obtains the electromagnetic parameters and environmental interference data of the UAV; and then inputs the electromagnetic parameters and the environmental interference data of the UAV into the Gaussian process regression prediction model to obtain the electromagnetic interference effect. threshold; finally, according to the environmental interference data and the electromagnetic interference effect threshold, determine the electromagnetic interference situation in which the UAV data link is located. The present application establishes a dynamic correlation between the electromagnetic interference effect threshold and the detected UAV electromagnetic parameters and environmental interference data through the Gaussian process regression prediction model, which can dynamically predict the electromagnetic interference effect threshold of the UAV under different working states, and improve the UAV electromagnetic interference situation judgment accuracy, so as to improve the UAV's active anti-jamming ability.

在本发明的一个实施例中,如图3所示,图3示出了无人机数据链电磁干扰态势预测方法的另一实现流程,其过程详述如下:In an embodiment of the present invention, as shown in FIG. 3 , FIG. 3 shows another implementation process of the method for predicting the electromagnetic interference situation of the UAV data link. The process is described in detail as follows:

S201:获取训练样本,所述训练样本包括观测数据和输入数据;所述观测数据包括干信比和AGC电压变化量,所述输入数据包括电磁参数和环境干扰数据。S201: Acquire a training sample, where the training sample includes observation data and input data; the observation data includes an interference-to-signal ratio and an AGC voltage variation, and the input data includes electromagnetic parameters and environmental interference data.

在本实施例中,干信比为干扰信号功率与工作信号功率的比值。标准AGC电压为无人机在正常无干扰情况下监测到的AGC电压。AGC电压变化量包括失锁效应对应的AGC电压变化量和误码效应对应的AGC电压变化量,而失锁效应对应的AGC电压变化量为无人机数据链发生失锁效应时的AGC电压减去标准AGC电压的差值;误码效应对应的AGC电压变化量为无人机数据链发生误码效应时的AGC电压减去标准AGC电压的差值。In this embodiment, the signal-to-interference ratio is the ratio of the power of the interference signal to the power of the working signal. The standard AGC voltage is the AGC voltage monitored by the drone under normal interference-free conditions. The AGC voltage change includes the AGC voltage change corresponding to the loss of lock effect and the AGC voltage change corresponding to the bit error effect, and the AGC voltage change corresponding to the loss of lock effect is the AGC voltage when the UAV data link has a loss of lock effect. The difference of the standard AGC voltage; the AGC voltage change corresponding to the bit error effect is the difference between the AGC voltage when the bit error effect occurs in the UAV data link minus the standard AGC voltage.

S202:对所述训练样本中的观测数据和所述输入数据进行标准化处理,得到标准化后的训练样本;S202: Standardize the observed data in the training sample and the input data to obtain a standardized training sample;

S203:基于高斯过程回归方法创建初始预测模型;S203: Create an initial prediction model based on the Gaussian process regression method;

S204:将所述标准化后的训练样本输入所述初始预测模型,对所述初始预测模型进行训练,得到所述高斯过程回归预测模型。S204: Input the standardized training samples into the initial prediction model, and train the initial prediction model to obtain the Gaussian process regression prediction model.

本实施例以工作信号大功率-45dBm和小功率-80dBm两种数据链状态为例,通过无人机动态数据链电磁干扰注入效应试验分析得出:工作信号功率和干扰频率影响数据链电磁干扰效应阈值。In this example, the working signal high power -45dBm and low power -80dBm data link states are used as examples. Through the experimental analysis of the electromagnetic interference injection effect of the dynamic data link of the drone, it is concluded that the working signal power and the interference frequency affect the electromagnetic interference of the data link. effect threshold.

具体地,本申请以1频道为例,选择典型工作信号功率{-100,-95,-90,…,-45,-40,-35}dBm和干扰频偏{-5,-4,-3,-2,-1,0,1,2,3,4,5}MHz,通过开展注入试验得到不同数据链工作状态下的标准连续波干扰效应阈值,包括数据链开始出现误码和失锁两种效应时的干信比和AGC电压变化量。由于干扰信号功率和AGC电压不能直观地反映电磁干扰对无人机数据链的作用效果,所以将上述值分别转化为干信比和AGC电压变化量。确定输入数据(干扰信号频率、工作信号功率)与输出数据(干信比和AGC电压变化量)之间的关系,如图5所示和图6所示。Specifically, this application takes channel 1 as an example, selects typical working signal power {-100,-95,-90,...,-45,-40,-35}dBm and interference frequency offset {-5,-4,- 3,-2,-1,0,1,2,3,4,5}MHz, the standard CW interference effect thresholds under different data link working conditions were obtained by carrying out injection experiments, including the data link beginning to experience bit errors and loss. Interference-to-signal ratio and AGC voltage variation when the two effects are locked. Since the interference signal power and AGC voltage cannot directly reflect the effect of electromagnetic interference on the UAV data link, the above values are converted into the interference signal ratio and the AGC voltage variation respectively. Determine the relationship between input data (interference signal frequency, working signal power) and output data (interference signal ratio and AGC voltage variation), as shown in Figure 5 and Figure 6.

图5示出了无人机数据链出现误码时输入数据(干扰信号频率、工作信号功率)与输出数据(干信比和AGC电压变化量)之间的关系。其中,图5a示出了误码效应下不同工作信号功率对应的干信比与干扰频偏之间的关系曲线,图5b示出了误码效应下不同干扰频偏对应的干信比与干扰信号功率之间的关系曲线,图5c表示误码效应下不同工作信号功率对应的AGC电压变化量与干扰频偏之间的关系曲线,图5d表示误码效应下不同干扰频偏对应的AGC电压变化量与干扰信号功率之间的关系曲线。Figure 5 shows the relationship between input data (interference signal frequency, working signal power) and output data (interference-to-signal ratio and AGC voltage variation) when bit errors occur in the UAV data link. Among them, Figure 5a shows the relationship between the SIR and the interference frequency offset corresponding to different working signal powers under the bit error effect, and Figure 5b shows the SIR and the interference corresponding to different interference frequency offsets under the bit error effect. The relationship curve between signal powers, Figure 5c shows the relationship curve between the AGC voltage variation corresponding to different working signal powers and the interference frequency offset under the bit error effect, Figure 5d shows the AGC voltage corresponding to different interference frequency offsets under the bit error effect The relationship curve between the amount of change and the power of the interfering signal.

具体地,干扰频偏表示干扰信号频率减去工作信号频率得到的差值。Specifically, the interference frequency offset represents a difference obtained by subtracting the frequency of the interference signal from the frequency of the working signal.

图6示出了无人机数据链出现失锁时输入数据(干扰信号频率、工作信号功率)与输出数据(干信比和AGC电压变化量)之间的关系。其中,图6a示出了失锁效应下不同工作信号功率对应的干信比与干扰频偏之间的关系曲线,图6b示出了失锁效应下不同干扰频偏对应的干信比与干扰信号功率之间的关系曲线,图6c表示失锁效应下不同工作信号功率对应的AGC电压变化量与干扰频偏之间的关系曲线,图6d表示失锁效应下不同干扰频偏对应的AGC电压变化量与干扰信号功率之间的关系曲线。Figure 6 shows the relationship between input data (interference signal frequency, working signal power) and output data (interference signal ratio and AGC voltage variation) when the UAV data link loses lock. Among them, Figure 6a shows the relationship between the SIR and the interference frequency offset corresponding to different working signal powers under the loss of lock effect, and Figure 6b shows the SIR and the interference corresponding to different interference frequency offsets under the loss of lock effect. The relationship curve between signal powers, Figure 6c shows the relationship curve between the AGC voltage variation corresponding to different working signal powers and the interference frequency offset under the loss-of-lock effect, and Figure 6d shows the AGC voltage corresponding to different interference frequency offsets under the loss-of-lock effect The relationship curve between the amount of change and the power of the interfering signal.

由图5a和图6a可知,数据链在不同工作状态下出现误码和失锁效应时的干信比与干扰频偏都成非线性关系,同频干扰对应的干信比相对较小,即数据链抗同频干扰能力相对较弱,随着干扰频率偏离工作频率越远,干信比相对增大。图5b和6b中,对于不同干扰频率,数据链出现误码和失锁效应时的干信比与工作信号功率也都成非线性变化趋势,工作信号越强,干信比却相对减小,这是由于无人机的接收机内部有源电路逐渐趋于饱和状态引发的阻塞效应现象。类似地,由图5c、5d和6c、6d可知,数据链出现误码和失锁效应时AGC电压变化量与干扰频偏和工作信号功率都成非线性关系。因此,利用传统确定性建模方法难以从系统原理或者效应机理角度开展非线性系统电磁环境效应预测,但可以在基于上述试验数据从数学统计角度开展不确定性建模,创建高斯过程回归预测模型。It can be seen from Figure 5a and Figure 6a that the interference signal ratio and the interference frequency offset have a nonlinear relationship when the data link has bit error and lock loss effects in different working states, and the interference signal ratio corresponding to co-channel interference is relatively small, that is, The ability of the data link to resist co-channel interference is relatively weak. As the interference frequency deviates farther from the operating frequency, the interference signal ratio increases relatively. In Figures 5b and 6b, for different interference frequencies, the signal-to-interference ratio and the power of the working signal also show a nonlinear change trend when the data link has bit errors and lock-loss effects. This is due to the blocking effect phenomenon caused by the gradual saturation of the active circuit inside the receiver of the drone. Similarly, it can be seen from Figures 5c, 5d and 6c, 6d that when the data link has bit error and lock loss effects, the AGC voltage variation has a nonlinear relationship with the interference frequency offset and the working signal power. Therefore, it is difficult to predict the electromagnetic environmental effects of nonlinear systems from the perspective of system principle or effect mechanism using traditional deterministic modeling methods. .

具体地,由数据链电磁干扰注入效应试验可知,工作信号功率Ps和干扰频率fj影响数据链开始出现误码和失锁效应时的干扰信号功率{Pj1,Pj2}和AGC电压{Vj1,Vj2}。本实施例将干信比{ISR1,ISR2}以及AGC电压的变化量{V1,V2}作为训练样本的观测值,便于观测和衡量数据链状态的受扰变化程度。其中,ISR1表示误码时的干信比,ISR2表示失锁时的干信比。V1表示误码效应对应的施加干扰信号前后的AGC电压变化量,V2表示失锁效应对应的施加干扰信号前后AGC电压变化量。Specifically, it can be seen from the data link electromagnetic interference injection effect test that the working signal power P s and the interference frequency f j affect the interference signal power {P j1 , P j2 } and AGC voltage { P j1 , P j2 } and AGC voltage { V j1 , V j2 }. In this embodiment, the interference-to-signal ratio {ISR 1 , ISR 2 } and the variation of the AGC voltage {V 1 , V 2 } are taken as the observed values of the training samples, which is convenient to observe and measure the disturbed change degree of the data link state. Among them, ISR 1 represents the interference-to-signal ratio when the bit error occurs, and ISR 2 represents the interference-to-signal ratio when the lock is lost. V 1 represents the variation of the AGC voltage before and after the application of the interference signal corresponding to the bit error effect, and V 2 represents the variation of the AGC voltage before and after the application of the interference signal corresponding to the loss of lock effect.

此外,以电磁干扰频率fj和工作信号功率Ps作为训练样本的输入数据,开展高斯过程回归建模。In addition, using the electromagnetic interference frequency f j and the working signal power P s as the input data of the training samples, the Gaussian process regression modeling is carried out.

在本实施例中,基于高斯过程回归的动态数据链电磁干扰效应阈值预测方法可以概括为:In this embodiment, the method for predicting the electromagnetic interference effect threshold value of dynamic data link based on Gaussian process regression can be summarized as:

(1)确定样本数据。根据动态数据链电磁干扰注入效应试验结果确定训练样本;(1) Determine the sample data. Determine the training samples according to the test results of the electromagnetic interference injection effect of the dynamic data link;

(2)设置超参数初始值。以双指数协方差函数作为核函数,通过设置超参数初始值来确定先验分布函数。(2) Set the initial value of the hyperparameter. Using the double exponential covariance function as the kernel function, the prior distribution function is determined by setting the initial value of the hyperparameter.

具体地,设观测值为y=f′(x)=f(x)+ε,其中,ε为高斯噪声且服从高斯分布

Figure BDA0002526846060000081
观测值的协方差函数为
Figure BDA0002526846060000082
I表示单位矩阵。此外,n*个样本X*作为测试集,其输出f*同样服从高斯分布N(μ(x*),K(x*,x*)),此时观测值和测试样本的预测值服从联合高斯先验分布函数:Specifically, let the observed value be y=f'(x)=f(x)+ε, where ε is Gaussian noise and obeys Gaussian distribution
Figure BDA0002526846060000081
The covariance function of the observations is
Figure BDA0002526846060000082
I represents the identity matrix. In addition, n * samples X * are used as the test set, and the output f * also obeys the Gaussian distribution N(μ(x * ), K(x * , x * )), at this time, the predicted value of the observed value and the test sample obey the joint Gaussian prior distribution function:

Figure BDA0002526846060000083
Figure BDA0002526846060000083

式(1)中,协方差K(X,X)为n×n维矩阵,K(X,X*)为n×n*维矩阵,K(X*,X)为n*×n维矩阵,K(X*,X*)为n*×n*维矩阵In formula (1), the covariance K(X,X) is an n×n-dimensional matrix, K(X,X * ) is an n×n * -dimensional matrix, and K(X * ,X) is an n * ×n-dimensional matrix , K(X * ,X * ) is an n * ×n * dimensional matrix

(3)训练模型。输入训练样本数据,将先验分布函数转化为后验分布函数,优化核函数的超参数值;得到高斯过程回归预测模型。(3) Training the model. Input the training sample data, convert the prior distribution function into the posterior distribution function, optimize the hyperparameter value of the kernel function, and obtain the Gaussian process regression prediction model.

在本实施例中,由贝叶斯原理和联合高斯分布的先验分布函数,可以得到输出f*的条件后验分布函数如式(2)所示:In this embodiment, from the Bayesian principle and the prior distribution function of the joint Gaussian distribution, the conditional posterior distribution function of the output f * can be obtained as shown in formula (2):

Figure BDA0002526846060000091
Figure BDA0002526846060000091

本实施例根据后验分布的平均值和协方差可以得到测试样本的预测输出值。In this embodiment, the predicted output value of the test sample can be obtained according to the mean value and covariance of the posterior distribution.

进一步地,对超参数的优化过程具体包括:Further, the optimization process of hyperparameters specifically includes:

由于计算过程中通常采用零均值,预测误差一般受到协方差影响较大,所以必须确定协方差函数的最优超参数才能降低预测误差。选取平方指数函数作为高斯核函数的协方差函数,该函数表示如式(3)所示。Since zero mean is usually used in the calculation process, the prediction error is generally greatly affected by the covariance, so the optimal hyperparameter of the covariance function must be determined to reduce the prediction error. The square exponential function is selected as the covariance function of the Gaussian kernel function, and the function expression is shown in formula (3).

Figure BDA0002526846060000092
Figure BDA0002526846060000092

式(3)中,l表示特征长度尺度,δ表示样本标准差,θ={l,δ}={θ12,…,θn}是所有超参数集合,由贝叶斯理论可知p(A|B,C)p(B|C)=p(B|A,C)p(A|C)=P(A,B|C),可以得到超参数的后验分布函数:In formula (3), l represents the feature length scale, δ represents the sample standard deviation, θ={l,δ}={θ 12 ,...,θ n } is the set of all hyperparameters, which can be known from Bayesian theory p(A|B,C)p(B|C)=p(B|A,C)p(A|C)=P(A,B|C), the posterior distribution function of the hyperparameter can be obtained:

Figure BDA0002526846060000093
Figure BDA0002526846060000093

式(4)中,p(θ|X)表示超参数先验,由于其似然函数值与样本X无关,所以p(θ|X)=p(θ),该值设置为常数;p(y|X)表示样本观测值的似然函数,其函数值与θ无关,该值也可以设置为常数;p(y|θ,X)表示边缘似然函数,y服从高斯分布

Figure BDA0002526846060000094
样本方差
Figure BDA0002526846060000095
和协方差K(X,X)取决于超参数集θ,因此In formula (4), p(θ|X) represents the hyperparameter prior. Since its likelihood function value is independent of the sample X, p(θ|X)=p(θ), which is set as a constant; p( y|X) represents the likelihood function of the sample observation value, and its function value is independent of θ, and the value can also be set as a constant; p(y|θ, X) represents the edge likelihood function, and y obeys the Gaussian distribution
Figure BDA0002526846060000094
sample variance
Figure BDA0002526846060000095
and covariance K(X,X) depends on the hyperparameter set θ, so

Figure BDA0002526846060000096
Figure BDA0002526846060000096

最终,计算最优超参数转化成求解式(5)的最大值,为求解方便,对式(5)求对数,得到Finally, the optimal hyperparameters are calculated and converted into the maximum value of equation (5). For the convenience of solving, the logarithm of equation (5) is obtained,

Figure BDA0002526846060000097
Figure BDA0002526846060000097

式(6)中,该对数似然函数包含三项:第一项是复杂惩罚项,用于防止训练模型过拟合;第二项是数据拟合项,用于表征超参数对训练样本的拟合程度;第三项是常数项。在求解对数似然函数最大值过程中,通常采用共轭梯度法,即通过数次计算各参数的导数得到梯度负方向,然后经过多次迭代计算直至收敛,得到最优超参数。In formula (6), the log-likelihood function contains three terms: the first term is the complex penalty term, which is used to prevent the training model from overfitting; the second term is the data fitting term, which is used to characterize the hyperparameters to the training samples. the degree of fit; the third term is a constant term. In the process of solving the maximum value of the log-likelihood function, the conjugate gradient method is usually used, that is, the negative direction of the gradient is obtained by calculating the derivatives of each parameter several times, and then the optimal hyperparameters are obtained after many iterations until convergence.

在本发明的一个实施例中,针对本实施例中的观测数据和输入数据,由于高斯过程回归方法一般采用零均值假设,对非平稳训练样本数据进行预测时,会造成预测值迅速趋于零,导致模型预测误差偏大。为消除物理量纲影响,必须对输入样本数据{Ps,fj}和观测样本数据{ISR1,ISR2,V1,V2}进行标准化处理,再开展模型训练和预测。对于任意数组Z={z1,z2,…,zn},其标准化处理公式如式(7)所示。In an embodiment of the present invention, for the observation data and input data in this embodiment, since the Gaussian process regression method generally adopts the zero mean assumption, when predicting the non-stationary training sample data, the predicted value will quickly tend to zero. , resulting in a large prediction error of the model. In order to eliminate the influence of physical dimensions, the input sample data {P s , f j } and the observed sample data {ISR 1 , ISR 2 , V 1 , V 2 } must be standardized, and then model training and prediction are carried out. For any array Z={z 1 , z 2 , . . . , z n }, the normalization processing formula is shown in formula (7).

Figure BDA0002526846060000101
Figure BDA0002526846060000101

式(7)中,Z′表示标准化处理后的数组,

Figure BDA0002526846060000102
表示原始数据的平均值,stdZ表示原始数据的标准差,即式(8):In formula (7), Z′ represents the normalized array,
Figure BDA0002526846060000102
Represents the mean value of the original data, and stdZ represents the standard deviation of the original data, that is, formula (8):

Figure BDA0002526846060000103
Figure BDA0002526846060000103

原始数据经过标准化处理后,训练样本的平均值变为0,方差为1。相反地,利用高斯过程回归预测模型得到的预测值同样为标准化后的数据,需要根据式(7)逆向求解,恢复预测值量纲。After the original data is normalized, the mean of the training samples becomes 0 and the variance is 1. On the contrary, the predicted value obtained by using the Gaussian process regression prediction model is also the standardized data, which needs to be solved inversely according to equation (7) to restore the dimension of the predicted value.

在本实施例中,利用标准化处理后的输入样本数据{Ps,fj}和观测样本数据{ISR1,ISR2,V1,V2}开展高斯过程回归模型训练,设置平方指数核函数的超参数初始值θ={1,1},高斯噪声似然估计的标准差设置为0.37。通过试验可知,训练样本预测输出的干信比和AGC电压变化量能够拟合出实测数据的变化趋势,此外,高斯过程回归方法能够给出预测输出的概率性不确定度。95%置信区间范围越小,对应的预测输出值可信度越高;相反,该区间越大,对应的预测输出值可信度越低。In this embodiment, the Gaussian process regression model is trained by using the normalized input sample data {P s , f j } and the observed sample data {ISR 1 , ISR 2 , V 1 , V 2 }, and the square exponential kernel function is set. The initial value of the hyperparameter θ = {1, 1}, and the standard deviation of the Gaussian noise likelihood estimate is set to 0.37. Through experiments, it can be seen that the signal-to-interference ratio and AGC voltage variation of the predicted output of the training sample can fit the variation trend of the measured data. In addition, the Gaussian process regression method can give the probabilistic uncertainty of the predicted output. The smaller the 95% confidence interval, the higher the reliability of the corresponding predicted output value; on the contrary, the larger the interval, the lower the corresponding predicted output value.

在本实施例中,图7示出了数据链出现误码时电磁干扰效应阈值的高斯过程回归训练误差,其中图7a表示数据链出现误码时的干信比预测误差,图7b表示数据链出现误码时的AGC电压变化量预测误差。图8示出了数据链出现失锁时电磁干扰效应阈值的高斯过程回归训练误差,其中图8a表示数据链出现失锁时的干信比预测误差,图8b表示数据链出现失锁时的AGC电压变化量预测误差。In this embodiment, Fig. 7 shows the Gaussian process regression training error of the electromagnetic interference effect threshold when a bit error occurs in the data link, wherein Fig. 7a shows the interference signal ratio prediction error when a bit error occurs in the data link, and Fig. 7b shows the data link Prediction error of AGC voltage change when bit error occurs. Figure 8 shows the Gaussian process regression training error of the EMI effect threshold when the data link loses lock, wherein Figure 8a shows the interference signal ratio prediction error when the data link loses lock, and Figure 8b shows the AGC when the data link loses lock. Voltage variation prediction error.

如图7和8所示,与实际测试得到的观测样本值相比,同频干扰效应阈值的预测输出值偏差相对较大,这是由于同频带干扰情况下数据链状态不稳定以致于测量时机不准确造成的;干扰频偏越大,数据链出现电磁干扰效应时的状态也越稳定,测量时机相对准确,数据拟合效果也越好,总体预测误差小于1.5dB。通过上述样本学习和训练,得到高斯过程回归预测模型。As shown in Figures 7 and 8, compared with the observed sample values obtained from the actual test, the predicted output value deviation of the co-frequency interference effect threshold is relatively large. Caused by inaccuracy; the larger the interference frequency offset, the more stable the data link is when the electromagnetic interference effect occurs, the measurement timing is relatively accurate, the data fitting effect is better, and the overall prediction error is less than 1.5dB. Through the above sample learning and training, a Gaussian process regression prediction model is obtained.

在本发明的一个实施例中,所述无人机的电磁参数包括工作信号功率,所述环境干扰数据包括电磁干扰频率;所述电磁干扰效应阈值包括干扰信号功率阈值和AGC电压阈值;如图4所示,图4示出了图2中S102的具体时长流程,其过程详述如下:In an embodiment of the present invention, the electromagnetic parameters of the UAV include working signal power, the environmental interference data includes electromagnetic interference frequency; the electromagnetic interference effect threshold includes the interference signal power threshold and the AGC voltage threshold; as shown in the figure 4, FIG. 4 shows the specific duration process of S102 in FIG. 2, and the details of the process are as follows:

S301:将所述工作信号功率和所述电磁干扰频率输入所述高斯过程回归预测模型,输出所述无人机数据链出现不同效应时对应的干信比和AGC电压变化量;S301: Input the working signal power and the electromagnetic interference frequency into the Gaussian process regression prediction model, and output the corresponding interference signal ratio and AGC voltage variation when different effects occur in the UAV data link;

S302:根据所述无人机数据链出现不同效应时对应的干信比和所述工作信号功率,确定所述无人机数据链在不同工作状态下的干扰信号功率阈值;S302: Determine the interference signal power thresholds of the UAV data link under different working states according to the corresponding interference signal ratio and the working signal power when the UAV data link has different effects;

S303:根据所述无人机数据链出现不同效应时对应的AGC电压变化量和标准AGC电压,确定所述无人机数据链在不同工作状态下的AGC电压阈值。S303: Determine the AGC voltage thresholds of the drone data link in different working states according to the corresponding AGC voltage variation and the standard AGC voltage when the drone data link has different effects.

在本实施例中,将无人机当前环境的干扰信号功率与不同工作状态下的干扰信号功率阈值比较,确定无人机数据链所处的电磁干扰态势。In this embodiment, the interference signal power in the current environment of the drone is compared with the interference signal power threshold in different working states to determine the electromagnetic interference situation in which the data link of the drone is located.

在本实施例中,还可以将无人机的当前AGC电压与不同工作状态下的AGC电压阈值进行比较,确定无人机数据链所处的电磁干扰态势。In this embodiment, the current AGC voltage of the drone can also be compared with the AGC voltage thresholds in different working states to determine the electromagnetic interference situation in which the data link of the drone is located.

进一步地,通过综合干扰信号功率和当前AGC电压判断,确定无人机数据链所处的电磁干扰态势。Further, the electromagnetic interference situation in which the UAV data link is located is determined by judging the comprehensive interference signal power and the current AGC voltage.

在本发明的一个实施例中,所述数据链效应包括失锁和误码,所述干扰信号功率阈值包括第一干扰信号功率阈值、第二干扰信号功率阈值、第三干扰信号功率阈值和第四干扰信号功率阈值;图4中S302的具体实现流程还包括:In an embodiment of the present invention, the data link effect includes loss of lock and bit error, and the interference signal power threshold includes a first interference signal power threshold, a second interference signal power threshold, a third interference signal power threshold, and a first interference signal power threshold. Four interference signal power thresholds; the specific implementation process of S302 in Figure 4 also includes:

计算所述无人机数据链出现失锁效应时对应的干信比和所述工作信号功率的乘积,得到第一干扰信号功率阈值;所述第一干扰信号功率阈值为所述无人机数据链进入失锁状态的干扰信号功率阈值;Calculate the product of the corresponding interference signal ratio and the working signal power when the unmanned aerial vehicle data link has an out-of-lock effect, and obtain a first interference signal power threshold; the first interference signal power threshold is the unmanned aerial vehicle data. Interfering signal power threshold for the chain to enter the out-of-lock state;

计算所述无人机数据链出现误码效应时对应的干信比和所述工作信号功率的乘积,得到第二干扰信号功率阈值,所述第二干扰信号功率阈值为所述无人机数据链由不稳定状态进入临界失锁状态的干扰信号功率阈值;Calculate the product of the corresponding interference signal ratio and the working signal power when the UAV data link has a bit error effect to obtain a second interference signal power threshold, where the second interference signal power threshold is the UAV data The interfering signal power threshold for the chain to enter a critical loss-of-lock state from an unstable state;

将所述第二干扰信号功率阈值减去第一预设缓冲值,得到第三干扰信号功率阈值,所述第三干扰信号功率阈值为所述无人机数据链由相对稳定状态进入不稳定状态的干扰信号功率阈值;Subtract the first preset buffer value from the second interference signal power threshold to obtain a third interference signal power threshold, where the third interference signal power threshold is the UAV data link from a relatively stable state to an unstable state The interfering signal power threshold;

将所述第三干扰信号功率阈值减去第二预设缓冲值,得到第四干扰信号功率阈值,所述第四干扰信号功率阈值为所述无人机数据链由稳定状态进入相对稳定状态对应的干扰信号功率阈值。Subtract the second preset buffer value from the third interference signal power threshold to obtain a fourth interference signal power threshold, where the fourth interference signal power threshold corresponds to the UAV data link entering a relatively stable state from a stable state The interfering signal power threshold.

在本实施例中,将数据链的工作状态定义为失锁、临界失锁、不稳定、相对稳定和稳定。其中稳定和相对稳定状态为不受外界电磁干扰影响的状态;不稳定表示数据链受到外界电磁干扰影响后链路稳定性变差,飞机状态参量存在波动风险;临界失锁表示数据链受到外界电磁干扰影响后数据链不稳定,飞机状态参量波动较大;失锁表示数据链受到外界电磁干扰影响后通信中断,无人机失去控制。In this embodiment, the working states of the data link are defined as loss of lock, critical loss of lock, unstable, relatively stable and stable. Among them, stable and relatively stable states are states that are not affected by external electromagnetic interference; unstable means that the data link is affected by external electromagnetic interference, and the link stability becomes worse, and the aircraft state parameters are at risk of fluctuation; critical loss of lock means that the data link is affected by external electromagnetic interference. After the interference, the data link is unstable, and the aircraft state parameters fluctuate greatly; loss of lock means that the communication is interrupted after the data link is affected by external electromagnetic interference, and the drone is out of control.

在本实施例中,第一预设缓冲值和第二预设缓冲值均可以设置为6dB。In this embodiment, both the first preset buffer value and the second preset buffer value may be set to 6dB.

在本实施例中,若无人机当前的干扰信号功率大于第一干扰信号功率阈值,则预测无人机处于失锁状态;若无人机当前的干扰信号功率大于第二干扰信号功率阈值且小于或等于第一干扰信号功率阈值,则预测无人机处于临界失锁状态;若无人机当前的干扰信号功率大于第三干扰信号功率阈值且小于或等于第二干扰信号功率阈值,则预测无人机处于不稳定状态;若无人机当前的干扰信号功率大于第四干扰信号功率阈值且小于或等于第三干扰信号功率阈值,则预测无人机处于相对稳定状态;若无人机当前的干扰信号功率小于或等于第四干扰信号功率阈值,则预测无人机处于稳定状态。In this embodiment, if the current jamming signal power of the drone is greater than the first jamming signal power threshold, it is predicted that the drone is in an out-of-lock state; if the current jamming signal power of the drone is greater than the second jamming signal power threshold and If it is less than or equal to the first jamming signal power threshold, it is predicted that the UAV is in a critical loss-of-lock state; if the current jamming signal power of the UAV is greater than the third jamming signal power threshold and less than or equal to the second jamming signal power threshold, it is predicted that The drone is in an unstable state; if the current jamming signal power of the drone is greater than the fourth jamming signal power threshold and less than or equal to the third jamming signal power threshold, it is predicted that the drone is in a relatively stable state; if the drone is currently in a relatively stable state The jamming signal power is less than or equal to the fourth jamming signal power threshold, and the UAV is predicted to be in a stable state.

在本实施例中,第一干扰信号功率阈值大于第二干扰信号功率阈值,第二干扰信号功率阈值大于第三干扰信号功率阈值,第三干扰信号功率阈值大于第四干扰信号功率阈值。In this embodiment, the first interference signal power threshold is greater than the second interference signal power threshold, the second interference signal power threshold is greater than the third interference signal power threshold, and the third interference signal power threshold is greater than the fourth interference signal power threshold.

在本发明的一个实施例中,所述数据链效应包括失锁和误码,所述AGC电压阈值包括第一AGC电压阈值、第二AGC电压阈值、第三AGC电压阈值和第四AGC电压阈值;图4中的S303的具体实现流程包括:In one embodiment of the present invention, the data link effect includes loss of lock and bit error, and the AGC voltage threshold includes a first AGC voltage threshold, a second AGC voltage threshold, a third AGC voltage threshold and a fourth AGC voltage threshold ; The concrete realization flow of S303 in Fig. 4 includes:

将所述无人机数据链在失锁效应下对应的AGC电压变化量与标准AGC电压相加,得到第一AGC电压阈值,所述第一AGC电压阈值为所述无人机数据链进入失锁状态的AGC电压阈值;The AGC voltage change corresponding to the unmanned aerial vehicle data link under the loss of lock effect is added to the standard AGC voltage to obtain the first AGC voltage threshold, and the first AGC voltage threshold is the unmanned aerial vehicle data link. AGC voltage threshold in lock state;

将所述无人机数据链在误码效应下对应的AGC电压变化量与标准AGC电压相加,得到第二AGC电压阈值,所述第二AGC电压阈值为所述无人机数据链由不稳定状态进入临界失锁状态的AGC电压阈值;The AGC voltage variation corresponding to the UAV data link under the bit error effect is added to the standard AGC voltage to obtain a second AGC voltage threshold, and the second AGC voltage threshold is the UAV data link from which it is not determined. The AGC voltage threshold for the steady state to enter the critical loss-of-lock state;

将所述第二AGC电压阈值减去第三预设缓冲值,得到第三AGC电压阈值,所述第三AGC电压阈值为所述无人机数据链由相对稳定状态进入不稳定状态的AGC电压阈值;Subtract the third preset buffer value from the second AGC voltage threshold to obtain a third AGC voltage threshold, where the third AGC voltage threshold is the AGC voltage of the drone data link from a relatively stable state to an unstable state threshold;

将所述第三AGC电压阈值减去第四预设缓冲值,得到第四AGC电压阈值,所述第四AGC电压阈值为所述无人机数据链由稳定状态进入相对稳定状态的AGC电压阈值。Subtract the fourth preset buffer value from the third AGC voltage threshold to obtain a fourth AGC voltage threshold, where the fourth AGC voltage threshold is the AGC voltage threshold at which the drone data link enters a relatively stable state from a stable state .

在本实施例中,第三预设缓冲值和第四预设缓冲值均可以设置为5V。第一AGC电压阈值大于第二AGC电压阈值,第二AGC电压阈值大于第三AGC电压阈值,第三AGC电压阈值大于第三AGC电压阈值。In this embodiment, both the third preset buffer value and the fourth preset buffer value may be set to 5V. The first AGC voltage threshold is greater than the second AGC voltage threshold, the second AGC voltage threshold is greater than the third AGC voltage threshold, and the third AGC voltage threshold is greater than the third AGC voltage threshold.

在本实施例中,若无人机的当前AGC电压大于第一AGC电压阈值,则预测无人机数据链处于失锁状态;若无人机的当前AGC电压大于第二AGC电压阈值且小于或等于第一AGC电压阈值,则预测所述无人机数据链处于临界失锁状态,若无人机的当前AGC电压大于第三AGC电压阈值且小于或等于第二AGC电压阈值,则预测所述无人机数据链处于不稳定状态,若无人机的当前AGC电压大于第四AGC电压阈值且小于或等于第三AGC电压阈值,则预测所述无人机数据链处于相对稳定状态,若无人机的当前AGC电压小于或等于第四AGC电压阈值,则预测所述无人机数据链处于稳定状态。In this embodiment, if the current AGC voltage of the drone is greater than the first AGC voltage threshold, it is predicted that the data link of the drone is in an out-of-lock state; if the current AGC voltage of the drone is greater than the second AGC voltage threshold and less than or is equal to the first AGC voltage threshold, it is predicted that the drone data link is in a critical loss-of-lock state, and if the current AGC voltage of the drone is greater than the third AGC voltage threshold and less than or equal to the second AGC voltage threshold, it is predicted that the The drone data link is in an unstable state. If the current AGC voltage of the drone is greater than the fourth AGC voltage threshold and less than or equal to the third AGC voltage threshold, it is predicted that the drone data link is in a relatively stable state. If the current AGC voltage of the man-machine is less than or equal to the fourth AGC voltage threshold, it is predicted that the UAV data link is in a stable state.

在本发明的一个实施例中,本实施例提供的无人机数据链电磁干扰态势预测方法还包括:In an embodiment of the present invention, the method for predicting the electromagnetic interference situation of the UAV data link provided by this embodiment further includes:

根据所述无人机数据链所处的电磁干扰态势确定对应的抗干扰响应措施。Corresponding anti-jamming response measures are determined according to the electromagnetic interference situation in which the UAV data link is located.

在本发明的一个实施例中,电磁干扰态势包括失锁状态、临界失锁状态、不稳定状态、相对稳定状态和稳定状态,上述确定抗干扰响应措施的具体过程包括:In an embodiment of the present invention, the electromagnetic interference situation includes a loss-of-lock state, a critical loss-of-lock state, an unstable state, a relatively stable state, and a stable state, and the above-mentioned specific process for determining the anti-interference response measures includes:

若所述无人机数据链处于所述相对稳定状态,则生成所述无人机数据链的异常报警信号;If the UAV data link is in the relatively stable state, an abnormal alarm signal of the UAV data link is generated;

若所述无人机数据链处于所述不稳定状态和所述临界失锁状态,则控制所述无人机执行电磁干扰自适应行为;所述电磁干扰自适应行为包括但不限于改变飞行航迹、调节机载天线方向、切换工作频道和控制地面发射功率。If the UAV data link is in the unstable state and the critical loss-of-lock state, the UAV is controlled to perform electromagnetic interference adaptive behavior; the electromagnetic interference adaptive behavior includes but is not limited to changing the flight flight track, adjust the direction of the airborne antenna, switch the working channel and control the ground transmit power.

在本实施例中,稳定状态不需要对无人机进行任何操作,根据电磁干扰等级划分,将相对稳定状态确定为初级警戒区间,此时生成报警信号,报警信号用于标记该无人机数据链。将不稳定和临界失锁状态确定为预警区间,当无人机数据链处于预警区间内时,执行电磁干扰自适应行为,电磁干扰自适应行为包括改变飞行航线、调节天线方向、切换工作频道和控制地面发射功率。其中,改变飞行航线用于远离干扰源,调节天线方向和切换工作频道均用于降低耦合效率,控制地面发射功率用于提高设备抗干扰能力。In this embodiment, the stable state does not require any operation on the UAV. According to the level of electromagnetic interference, the relatively stable state is determined as the primary warning interval. At this time, an alarm signal is generated, and the alarm signal is used to mark the data of the UAV. chain. The unstable and critical loss-of-lock state is determined as the early warning interval. When the UAV data link is in the early warning interval, the electromagnetic interference adaptive behavior is performed. The electromagnetic interference adaptive behavior includes changing the flight route, adjusting the antenna direction, switching the working channel and Controls the ground transmit power. Among them, changing the flight route is used to stay away from the interference source, adjusting the antenna direction and switching the working channel are used to reduce the coupling efficiency, and controlling the ground transmission power is used to improve the anti-jamming capability of the equipment.

在本实施例中,若无人机处于不稳定状态,则可以优先选择切换工作频道的抗干扰方式。若无人机处于临界失锁状态,空间频谱纯净度差,频道切换已经不能满足抗干扰需求,为了减轻干扰信号对无人机数据链的影响、降低地面控制站的发射损耗,适当增大工作信号能够使数据链达到抗干扰的目的。In this embodiment, if the drone is in an unstable state, the anti-jamming method of switching the working channel can be preferentially selected. If the UAV is in a critical loss-of-lock state, the purity of the space spectrum is poor, and the channel switching can no longer meet the anti-jamming requirements. The signal can make the data link achieve the purpose of anti-interference.

具体地,由于电磁干扰信号已经对数据链构成严重威胁,必须为数据链增加电磁干扰余量a,使无人机数据链的电磁干扰威胁程度降到不稳定状态,即第二干扰信号功率阈值Sj=Pj+a。Pj表示无人机当前监测的干扰信号功率。利用高斯过程回归预测模型逆向求出Sj条件下的工作信号功率PsSpecifically, since the electromagnetic interference signal has already posed a serious threat to the data link, it is necessary to increase the electromagnetic interference margin a for the data link, so that the electromagnetic interference threat level of the UAV data link is reduced to an unstable state, that is, the second interference signal power threshold S j =P j +a. P j represents the jamming signal power currently monitored by the UAV. Using the Gaussian process regression prediction model, the working signal power P s under the condition of S j is obtained reversely.

应理解,上述实施例中各步骤的序号的大小并不意味着执行顺序的先后,各过程的执行顺序应以其功能和内在逻辑确定,而不应对本发明实施例的实施过程构成任何限定。It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

在一个实施例中,如图9所示,图9示出了本实施例提供的无人机数据链电磁干扰态势预测装置100的结构示意图,其包括:In one embodiment, as shown in FIG. 9 , FIG. 9 shows a schematic structural diagram of the apparatus 100 for predicting the electromagnetic interference situation of the UAV data link provided in this embodiment, which includes:

电磁数据获取模块110,用于获取无人机的电磁参数和环境干扰数据;The electromagnetic data acquisition module 110 is used to acquire the electromagnetic parameters and environmental interference data of the UAV;

阈值获取模块120,用于将所述无人机的所述电磁参数和所述环境干扰数据输入高斯过程回归预测模型,得到电磁干扰效应阈值;a threshold value acquisition module 120, configured to input the electromagnetic parameters of the UAV and the environmental interference data into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold;

电磁干扰态势确定模块130,用于根据所述环境干扰数据和所述电磁干扰效应阈值,确定所述无人机数据链所处的电磁干扰态势。The electromagnetic interference situation determination module 130 is configured to determine the electromagnetic interference situation in which the UAV data link is located according to the environmental interference data and the electromagnetic interference effect threshold.

在一个实施例中,本实施例提供的无人机数据链电磁干扰态势预测装置100还包括:In one embodiment, the apparatus 100 for predicting the electromagnetic interference situation of the UAV data link provided in this embodiment further includes:

训练样本获取模块,用于获取训练样本,所述训练样本包括观测数据和输入数据;所述观测数据包括干信比和AGC电压变化量,所述输入数据包括电磁参数和环境干扰数据;a training sample acquisition module, configured to acquire a training sample, the training sample includes observation data and input data; the observation data includes interference signal ratio and AGC voltage variation, and the input data includes electromagnetic parameters and environmental interference data;

样本数据标准化模块,用于对所述训练样本中的观测数据和所述输入数据进行标准化处理,得到标准化后的训练样本;a sample data standardization module, which is used to standardize the observation data in the training sample and the input data to obtain a standardized training sample;

初始模型创建模块,用于基于高斯过程回归方法创建初始预测模型;The initial model creation module is used to create an initial prediction model based on the Gaussian process regression method;

模型训练模块,用于将所述标准化后的训练样本输入所述初始预测模型,对所述初始预测模型进行训练,得到所述高斯过程回归预测模型。A model training module, configured to input the standardized training samples into the initial prediction model, train the initial prediction model, and obtain the Gaussian process regression prediction model.

在一个实施例中,所述无人机的电磁参数包括工作信号功率,所述环境干扰数据包括电磁干扰频率;所述电磁干扰效应阈值包括干扰信号功率阈值和AGC电压阈值;In one embodiment, the electromagnetic parameters of the drone include working signal power, and the environmental interference data includes electromagnetic interference frequency; the electromagnetic interference effect threshold includes an interference signal power threshold and an AGC voltage threshold;

所述阈值获取模块120包括:The threshold acquisition module 120 includes:

输出值获取单元,用于将所述工作信号功率和所述电磁干扰频率输入所述高斯过程回归预测模型,输出所述无人机数据链出现不同效应时对应的干信比和AGC电压变化量;An output value acquisition unit, configured to input the working signal power and the electromagnetic interference frequency into the Gaussian process regression prediction model, and output the corresponding interference signal ratio and AGC voltage variation when different effects occur in the UAV data link ;

干扰信号功率阈值获取单元,用于根据所述无人机数据链出现不同效应时对应的干信比和所述工作信号功率,确定所述无人机数据链在不同工作状态下的干扰信号功率阈值;The interference signal power threshold acquisition unit is used to determine the interference signal power of the drone data link in different working states according to the corresponding interference signal ratio and the working signal power when the drone data link has different effects threshold;

电压阈值获取单元,用于根据所述无人机数据链出现不同效应时对应的AGC电压变化量和标准AGC电压,确定所述无人机数据链在不同工作状态下的AGC电压阈值。The voltage threshold acquisition unit is configured to determine the AGC voltage threshold of the drone data link in different working states according to the corresponding AGC voltage variation and the standard AGC voltage when different effects occur on the drone data link.

在一个实施例中,所述数据链效应包括失锁和误码,所述干扰信号功率阈值包括第一干扰信号功率阈值、第二干扰信号功率阈值、第三干扰信号功率阈值和第四干扰信号功率阈值;In one embodiment, the data link effect includes loss of lock and bit error, and the interference signal power threshold includes a first interference signal power threshold, a second interference signal power threshold, a third interference signal power threshold, and a fourth interference signal power threshold power threshold;

所述干扰信号功率阈值获取单元包括:The interference signal power threshold obtaining unit includes:

第一干扰信号功率阈值计算子单元,用于计算所述无人机数据链出现失锁效应时对应的干信比和所述工作信号功率的乘积,得到第一干扰信号功率阈值;所述第一干扰信号功率阈值为所述无人机数据链进入失锁状态的干扰信号功率阈值;The first interference signal power threshold calculation subunit is used to calculate the product of the corresponding interference signal ratio and the working signal power when the unmanned aerial vehicle data link has an out-of-lock effect, so as to obtain the first interference signal power threshold; the first interference signal power threshold; A jamming signal power threshold is the jamming signal power threshold at which the UAV data link enters an out-of-lock state;

第二干扰信号功率阈值计算子单元,用于计算所述无人机数据链出现误码效应时对应的干信比和所述工作信号功率的乘积,得到第二干扰信号功率阈值,所述第二干扰信号功率阈值为所述无人机数据链由不稳定状态进入临界失锁状态的干扰信号功率阈值;The second interference signal power threshold calculation subunit is used to calculate the product of the corresponding interference signal ratio and the working signal power when the UAV data link has a bit error effect, to obtain the second interference signal power threshold, and the first The second jamming signal power threshold is the jamming signal power threshold at which the UAV data link enters a critical loss-of-lock state from an unstable state;

第三干扰信号功率阈值计算子单元,用于将所述第二干扰信号功率阈值减去第一预设缓冲值,得到第三干扰信号功率阈值,所述第三干扰信号功率阈值为所述无人机数据链由相对稳定状态进入不稳定状态的干扰信号功率阈值;A third interference signal power threshold calculation subunit, configured to subtract the first preset buffer value from the second interference signal power threshold to obtain a third interference signal power threshold, where the third interference signal power threshold is the no The threshold value of the interference signal power for the human-machine data link to enter an unstable state from a relatively stable state;

第四干扰信号功率阈值计算子单元,用于将所述第三干扰信号功率阈值减去第二预设缓冲值,得到第四干扰信号功率阈值,所述第四干扰信号功率阈值为所述无人机数据链由稳定状态进入相对稳定状态对应的干扰信号功率阈值。A fourth interference signal power threshold calculation subunit, configured to subtract the second preset buffer value from the third interference signal power threshold to obtain a fourth interference signal power threshold, where the fourth interference signal power threshold is the no The threshold value of the interference signal power corresponding to the HMI data link from a stable state to a relatively stable state.

在一个实施例中,所述数据链效应包括失锁和误码,所述AGC电压阈值包括第一AGC电压阈值、第二AGC电压阈值、第三AGC电压阈值和第四AGC电压阈值;电压阈值获取单元包括:In one embodiment, the data link effects include loss of lock and bit errors, and the AGC voltage thresholds include a first AGC voltage threshold, a second AGC voltage threshold, a third AGC voltage threshold, and a fourth AGC voltage threshold; voltage thresholds The acquisition unit includes:

第一AGC电压阈值获取子单元,用于将所述无人机数据链在失锁效应下对应的AGC电压变化量与标准AGC电压相加,得到第一AGC电压阈值,所述第一AGC电压阈值为所述无人机数据链进入失锁状态的AGC电压阈值;The first AGC voltage threshold acquisition sub-unit is used to add the corresponding AGC voltage variation of the UAV data link under the loss-of-lock effect to the standard AGC voltage to obtain a first AGC voltage threshold, the first AGC voltage The threshold is the AGC voltage threshold at which the UAV data link enters an out-of-lock state;

第二AGC电压阈值获取子单元,用于将所述无人机数据链在误码效应下对应的AGC电压变化量与标准AGC电压相加,得到第二AGC电压阈值,所述第二AGC电压阈值为所述无人机数据链由不稳定状态进入临界失锁状态的AGC电压阈值;The second AGC voltage threshold acquisition subunit is used to add the corresponding AGC voltage variation of the UAV data link under the bit error effect to the standard AGC voltage to obtain a second AGC voltage threshold, the second AGC voltage The threshold is the AGC voltage threshold at which the UAV data link enters a critical unlocked state from an unstable state;

第三AGC电压阈值获取子单元,用于将所述第二AGC电压阈值减去第三预设缓冲值,得到第三AGC电压阈值,所述第三AGC电压阈值为所述无人机数据链由相对稳定状态进入不稳定状态的AGC电压阈值;A third AGC voltage threshold obtaining subunit, configured to subtract a third preset buffer value from the second AGC voltage threshold to obtain a third AGC voltage threshold, where the third AGC voltage threshold is the UAV data link AGC voltage threshold from a relatively stable state to an unstable state;

第四AGC电压阈值获取子单元,用于将所述第三AGC电压阈值减去第四预设缓冲值,得到第四AGC电压阈值,所述第四AGC电压阈值为所述无人机数据链由稳定状态进入相对稳定状态的AGC电压阈值。The fourth AGC voltage threshold acquisition subunit is used to subtract the fourth preset buffer value from the third AGC voltage threshold to obtain a fourth AGC voltage threshold, where the fourth AGC voltage threshold is the UAV data link AGC voltage threshold from steady state to relatively steady state.

在一个实施例中,本实施例提供的无人机数据链电磁干扰态势预测装置100还包括:In one embodiment, the apparatus 100 for predicting the electromagnetic interference situation of the UAV data link provided in this embodiment further includes:

干扰措施确定模块,用于根据所述无人机数据链所处的电磁干扰态势确定对应的抗干扰响应措施。The interference measure determination module is used for determining corresponding anti-interference response measures according to the electromagnetic interference situation in which the UAV data link is located.

在一个实施例中,干扰措施确定模块包括:In one embodiment, the interference measure determination module includes:

异常报警单元,用于若所述无人机数据链处于所述相对稳定状态,则生成所述无人机数据链的异常报警信号;an abnormal alarm unit, configured to generate an abnormal alarm signal of the UAV data link if the UAV data link is in the relatively stable state;

干扰自适应行为执行单元,用于若所述无人机数据链处于所述不稳定状态和所述临界失锁状态,则控制所述无人机执行电磁干扰自适应行为;所述电磁干扰自适应行为包括但不限于改变飞行航迹、调节机载天线方向、切换工作频道和控制地面发射功率。The interference adaptive behavior execution unit is configured to control the UAV to perform the electromagnetic interference adaptive behavior if the UAV data link is in the unstable state and the critical loss-of-lock state; the electromagnetic interference automatic Adaptive behaviors include, but are not limited to, changing flight paths, adjusting the direction of onboard antennas, switching operating channels, and controlling ground transmit power.

本发明一实施例提供了一种自适应预测终端924,包括:处理器、存储器以及存储在所述存储器中并可在所述处理器上运行的计算机程序。所述处理器执行所述计算机程序时实现上述各个无人机数据链电磁干扰态势预测方法实施例中的步骤,例如图2所示的步骤101至103。或者,所述处理器执行所述计算机程序时实现上述各装置实施例中各模块/单元的功能,例如图9所示模块110至130的功能。An embodiment of the present invention provides an adaptive prediction terminal 924, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in each of the above embodiments of the method for predicting the electromagnetic interference situation of the UAV data link are implemented, for example, steps 101 to 103 shown in FIG. 2 . Alternatively, when the processor executes the computer program, the functions of the modules/units in the foregoing device embodiments, for example, the functions of the modules 110 to 130 shown in FIG. 9 , are implemented.

所述计算机程序可以被分割成一个或多个模块/单元,所述一个或者多个模块/单元被存储在所述存储器中,并由所述处理器执行,以完成本发明。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段,该指令段用于描述所述计算机程序在所述自适应预测终端924中的执行过程。The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the adaptive prediction terminal 924 .

所述自适应预测终端924可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述无人机可包括,但不仅限于,处理器、存储器。本领域技术人员可以理解,上述给出的自适应预测终端924的部件并不构成对自适应预测终端924的限定,可以包括比上述结构更多或更少的部件,或者组合某些部件,或者不同的部件,例如所述无人机还可以包括输入输出设备、网络接入设备、总线等。The adaptive prediction terminal 924 may be a computing device such as a desktop computer, a notebook computer, a palmtop computer, and a cloud server. The drone may include, but is not limited to, a processor, memory. Those skilled in the art can understand that the components of the adaptive prediction terminal 924 given above do not constitute a limitation on the adaptive prediction terminal 924, and may include more or less components than the above structure, or combine some components, or Different components, such as the UAV, may also include input and output devices, network access devices, buses, and the like.

所称处理器可以是中央处理单元(Central Processing Unit,CPU),还可以是其他通用处理器、数字信号处理器(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现成可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The processor may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf processors Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

所述存储器可以是所述自适应预测终端924的内部存储单元,例如自适应预测终端924的硬盘或内存。所述存储器也可以是所述自适应预测终端924的外部存储设备,例如所述自适应预测终端924上配备的插接式硬盘,智能存储卡(Smart Media Card,SMC),安全数字(Secure Digital,SD)卡,闪存卡(Flash Card)等。进一步地,所述存储器还可以既包括所述自适应预测终端924的内部存储单元也包括外部存储设备。所述存储器用于存储所述计算机程序以及所述无人机所需的其他程序和数据。所述存储器还可以用于暂时地存储已经输出或者将要输出的数据。The memory may be an internal storage unit of the adaptive prediction terminal 924 , such as a hard disk or a memory of the adaptive prediction terminal 924 . The memory may also be an external storage device of the adaptive prediction terminal 924, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital) equipped on the adaptive prediction terminal 924. , SD) card, flash memory card (Flash Card) and so on. Further, the memory may also include both an internal storage unit of the adaptive prediction terminal 924 and an external storage device. The memory is used to store the computer program and other programs and data required by the drone. The memory may also be used to temporarily store data that has been output or is to be output.

所属领域的技术人员可以清楚地了解到,为了描述的方便和简洁,仅以上述各功能单元、模块的划分进行举例说明,实际应用中,可以根据需要而将上述功能分配由不同的功能单元、模块完成,即将所述装置的内部结构划分成不同的功能单元或模块,以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中,上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。另外,各功能单元、模块的具体名称也只是为了便于相互区分,并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

在上述实施例中,对各个实施例的描述都各有侧重,某个实施例中没有详述或记载的部分,可以参见其它实施例的相关描述。In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本发明的范围。Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

在本发明所提供的实施例中,应该理解到,所揭露的装置/无人机和方法,可以通过其它的方式实现。例如,以上所描述的装置/无人机实施例仅仅是示意性的,例如,所述模块或单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。另一点,所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口,装置或单元的间接耦合或通讯连接,可以是电性,机械或其它的形式。In the embodiments provided by the present invention, it should be understood that the disclosed apparatus/unmanned aerial vehicle and method may be implemented in other manners. For example, the device/unmanned aerial vehicle embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple divisions. Units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

另外,在本发明各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本发明实现上述实施例方法中的全部或部分流程,也可以通过计算机程序来指令相关的硬件来完成,所述的计算机程序可存储于一计算机可读存储介质中,该计算机程序在被处理器执行时,可实现上述各个方法实施例的步骤。。其中,所述计算机程序包括计算机程序代码,所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括:能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是,所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减,例如在某些司法管辖区,根据立法和专利实践,计算机可读介质不包括电载波信号和电信信号。The integrated modules/units, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. . Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable media Electric carrier signals and telecommunication signals are not included.

以上所述实施例仅用以说明本发明的技术方案,而非对其限制;尽管参照前述实施例对本发明进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围,均应包含在本发明的保护范围之内。The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it is still possible to implement the foregoing implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the within the protection scope of the present invention.

Claims (10)

1. The method for predicting the electromagnetic interference situation of the data link of the unmanned aerial vehicle is characterized by comprising the following steps of:
acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle;
inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold;
and determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold value.
2. The drone data link electromagnetic interference situation prediction method of claim 1, wherein prior to the obtaining the drone's electromagnetic parameters and environmental interference data, the method further comprises:
acquiring a training sample, wherein the training sample comprises observation data and input data; the observation data comprises an interference-signal ratio and AGC voltage variation, and the input data comprises electromagnetic parameters and environmental interference data;
standardizing the observation data and the input data in the training sample to obtain a standardized training sample;
establishing an initial prediction model based on a Gaussian process regression method;
and inputting the normalized training samples into the initial prediction model, and training the initial prediction model to obtain the Gaussian process regression prediction model.
3. The method of predicting drone data link electromagnetic interference posture of claim 1, wherein the electromagnetic parameters of the drone include operating signal power, the environmental interference data includes electromagnetic interference frequency; the electromagnetic interference effect threshold comprises an interference signal power threshold and an AGC voltage threshold;
inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold, wherein the electromagnetic interference effect threshold comprises the following steps:
inputting the working signal power and the electromagnetic interference frequency into the Gaussian process regression prediction model, and outputting corresponding interference-signal ratio and AGC voltage variation when different effects occur in the unmanned aerial vehicle data chain;
determining interference signal power thresholds of the unmanned aerial vehicle data chain in different working states according to corresponding interference-signal ratios and the working signal power when different effects occur in the unmanned aerial vehicle data chain;
and determining AGC voltage threshold values of the unmanned aerial vehicle data chain in different working states according to the AGC voltage variation and the standard AGC voltage corresponding to the unmanned aerial vehicle data chain with different effects.
4. The method of claim 3, wherein the datalink effects include loss of lock and bit errors, and wherein the jamming signal power thresholds include a first jamming signal power threshold, a second jamming signal power threshold, a third jamming signal power threshold, and a fourth jamming signal power threshold;
the determining the interference signal power threshold of the data link of the unmanned aerial vehicle in different working states according to the corresponding interference-to-signal ratio and the working signal power when different effects occur in the data link of the unmanned aerial vehicle comprises:
calculating the product of the corresponding interference-signal ratio and the working signal power when the unmanned aerial vehicle data link has the out-of-lock effect to obtain a first interference signal power threshold; the first interference signal power threshold is an interference signal power threshold when the unmanned aerial vehicle data link enters an out-of-lock state;
calculating the product of the corresponding interference-signal ratio and the working signal power when the error code effect occurs to the data chain of the unmanned aerial vehicle to obtain a second interference signal power threshold value, wherein the second interference signal power threshold value is the interference signal power threshold value when the data chain of the unmanned aerial vehicle enters a critical out-of-lock state from an unstable state;
subtracting a first preset buffer value from the second interference signal power threshold value to obtain a third interference signal power threshold value, wherein the third interference signal power threshold value is an interference signal power threshold value when the unmanned aerial vehicle data chain enters an unstable state from a relatively stable state;
and subtracting a second preset buffer value from the third interference signal power threshold value to obtain a fourth interference signal power threshold value, wherein the fourth interference signal power threshold value is an interference signal power threshold value corresponding to the fact that the unmanned aerial vehicle data chain enters a relatively stable state from a stable state.
5. The method of claim 3, wherein the datalink effects include loss of lock and bit errors, and wherein the AGC voltage thresholds include a first AGC voltage threshold, a second AGC voltage threshold, a third AGC voltage threshold, and a fourth AGC voltage threshold;
according to AGC voltage variation and standard AGC voltage that correspond when different effects appear in the unmanned aerial vehicle data link, confirm the AGC voltage threshold value of unmanned aerial vehicle data link under different operating condition, include:
adding the AGC voltage variation corresponding to the unmanned aerial vehicle data link under the unlocking effect to a standard AGC voltage to obtain a first AGC voltage threshold value, wherein the first AGC voltage threshold value is the AGC voltage threshold value when the unmanned aerial vehicle data link enters the unlocking state;
adding the AGC voltage variation corresponding to the data chain of the unmanned aerial vehicle under the error code effect to a standard AGC voltage to obtain a second AGC voltage threshold value, wherein the second AGC voltage threshold value is the AGC voltage threshold value when the data chain of the unmanned aerial vehicle enters a critical unlocking state from an unstable state;
subtracting a third preset buffer value from the second AGC voltage threshold value to obtain a third AGC voltage threshold value, wherein the third AGC voltage threshold value is an AGC voltage threshold value when the unmanned aerial vehicle data chain enters an unstable state from a relatively stable state;
and subtracting a fourth preset buffer value from the third AGC voltage threshold value to obtain a fourth AGC voltage threshold value, wherein the fourth AGC voltage threshold value is the AGC voltage threshold value when the unmanned aerial vehicle data chain enters a relatively stable state from the stable state.
6. The unmanned aerial vehicle data link electromagnetic interference situation prediction method of any one of claims 1 to 5, the method further comprising:
and determining corresponding anti-interference response measures according to the electromagnetic interference situation of the unmanned aerial vehicle data chain.
7. The unmanned aerial vehicle data link electromagnetic interference situation prediction method of claim 6, wherein the electromagnetic interference situation includes an out-of-lock state, a critical out-of-lock state, an unstable state, a relatively stable state, and a stable state;
the determining of the corresponding anti-interference response measures according to the electromagnetic interference situation where the unmanned aerial vehicle data chain is located includes:
if the unmanned aerial vehicle data chain is in the relatively stable state, generating an abnormal alarm signal of the unmanned aerial vehicle data chain;
if the data link of the unmanned aerial vehicle is in the unstable state and the critical unlocking state, controlling the unmanned aerial vehicle to execute an electromagnetic interference adaptive behavior; the electromagnetic interference adaptive behavior includes, but is not limited to, changing flight path, adjusting airborne antenna direction, switching operating channels, and controlling ground transmit power.
8. The utility model provides an unmanned aerial vehicle data link electromagnetic interference situation prediction device which characterized in that includes:
the electromagnetic data acquisition module is used for acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle;
the threshold value obtaining module is used for inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold value;
and the electromagnetic interference situation determination module is used for determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold.
9. An adaptive predictive terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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