CN109034088A - A kind of unmanned plane signal detection method and device - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及无线通信领域的信号检测与识别技术领域,特别是涉及一种无人机信号探测方法及装置。The invention relates to the technical field of signal detection and identification in the field of wireless communication, in particular to a method and device for detecting signals of an unmanned aerial vehicle.
背景技术Background technique
随着无人机技术的不断发展,无人机的应用也愈发广泛。无人机技术在航拍、农业植保、快递运输以及灾难救援等多方面领域为生产和生活带来了便利,与此同时,也伴随着出现了偷拍、运送违规物品、干扰民航起降信号等问题。因此,对于无线环境中无人机信号的探测逐渐成为一个重要研究方向。With the continuous development of drone technology, the application of drones is becoming more and more extensive. UAV technology has brought convenience to production and life in many fields such as aerial photography, agricultural plant protection, express transportation, and disaster relief. At the same time, it is also accompanied by problems such as candid photography, delivery of illegal items, and interference with civil aviation take-off and landing signals. . Therefore, the detection of UAV signals in wireless environments has gradually become an important research direction.
以往的无人机探测方法多是针对无人机信号和噪声信号的混合信号中对无人机信号进行信号检测与信号识别,但是随着无线通信环境日益复杂,例如,无人机可以工作在2.4GHz ISM(Industrial Scientific Medical,工业、科学、医疗)频段,但是该频段同时也存在Wi-Fi(Wireless Fidelity,无线保真)信号、蓝牙信号、无绳电话发出的信号、以及采用Zigbee(紫蜂)技术进行无线通信的信号等,因此,以往的探测方法已经不再适用于无人机信号的探测。Previous UAV detection methods are mostly for signal detection and signal identification of UAV signals in the mixed signal of UAV signals and noise signals, but as the wireless communication environment becomes increasingly complex, for example, UAVs can work in 2.4GHz ISM (Industrial Scientific Medical, industrial, scientific, medical) frequency band, but there are also Wi-Fi (Wireless Fidelity, wireless fidelity) signals, Bluetooth signals, signals from cordless phones, and Zigbee (Zigbee) signals in this frequency band. ) technology for wireless communication signals, etc. Therefore, the previous detection methods are no longer suitable for the detection of UAV signals.
关于信号探测,其主要包含信号检测与信号识别两个部分。信号检测是指将截获或接收到的信号进行特定的分析处理,判断目标频段是否有信号出现的过程。而信号识别是指对信号进行更深的分析处理,进一步识别出现的信号是否为目标信号的过程。Regarding signal detection, it mainly includes two parts: signal detection and signal identification. Signal detection refers to the process of performing specific analysis and processing on the intercepted or received signal to determine whether there is a signal in the target frequency band. Signal recognition refers to the process of performing deeper analysis and processing on the signal to further identify whether the signal that appears is the target signal.
在信号检测的过程中,一般先利用接收序列构造检验统计量,之后将统计量与门限或分类准则作比较。然而,信号识别由于无线环境存在多种信号混叠的现象,电磁环境较为复杂,目前还没有相对成熟的识别方法。一般而言,现有的信号检测识别算法主要包括以下四类:匹配滤波器检测、波形检测、相关矩阵检测和能量检测。其中,匹配滤波器检测与波形检测的实现需要足量的先验信息,而实际检测中通常很难满足此条件;相关矩阵检测虽能用于盲检测,但计算复杂,且算法性能在各种环境下的稳定性较差。能量检测相比以上几种方法,具有实现简单、算法复杂度低等优点,是实际检测中最常用的信号检测算法。In the process of signal detection, the received sequence is generally used to construct the test statistic first, and then the statistic is compared with the threshold or classification criterion. However, due to the phenomenon of multiple signal aliasing in the wireless environment and the complex electromagnetic environment for signal identification, there is no relatively mature identification method at present. Generally speaking, the existing signal detection and recognition algorithms mainly include the following four categories: matched filter detection, waveform detection, correlation matrix detection and energy detection. Among them, the implementation of matched filter detection and waveform detection requires sufficient prior information, but it is usually difficult to meet this condition in actual detection; although correlation matrix detection can be used for blind detection, the calculation is complicated, and the performance of the algorithm is in various Environment stability is poor. Compared with the above methods, energy detection has the advantages of simple implementation and low algorithm complexity, and is the most commonly used signal detection algorithm in actual detection.
现有技术中,根据无人机信号在频域上为类梯形波的特性,可以基于能量检测与识别方法,利用频域信号的在每一频率点处的信号梯度值的变化表示信号的能量,对无人机信号进行识别。但是在能量的检测及识别方法过程中,由于只关注能量特征,导致信号探测过程中对噪声信号敏感。也就是说,当无人机信号处于低信噪比的情况下,由于无线环境中的信号混杂,其中,含噪信号和纯噪信号之间的差异将变小,而无人机信号的探测也将出现偏差。同时,当无线环境中的干扰信号存在其他类梯形波的非无人机信号时,采用现有的能量检测与识别方法仅仅针对类梯形波或信号梯度进行识别,也将导致无人机信号的探测出现偏差。In the prior art, according to the characteristics of the UAV signal as a trapezoidal wave in the frequency domain, based on the energy detection and identification method, the change of the signal gradient value at each frequency point of the frequency domain signal can be used to represent the energy of the signal , to identify the UAV signal. However, in the process of energy detection and identification method, because we only focus on energy features, the signal detection process is sensitive to noise signals. That is to say, when the UAV signal is in the case of low signal-to-noise ratio, due to the signal clutter in the wireless environment, the difference between the noisy signal and the pure noise signal will become smaller, and the detection of the UAV signal There will also be deviations. At the same time, when there are other trapezoidal-like non-UAV signals in the interference signal in the wireless environment, using the existing energy detection and identification method to only identify trapezoidal-like waves or signal gradients will also lead to unmanned aerial vehicle signals. Probing has gone awry.
发明内容Contents of the invention
本发明实施例的目的在于提供一种无人机信号探测方法及装置,以实现对无线环境中的无人机信号的探测。具体技术方案如下:The purpose of the embodiments of the present invention is to provide a UAV signal detection method and device, so as to realize the detection of UAV signals in a wireless environment. The specific technical scheme is as follows:
本发明实施例提供了一种无人机信号探测方法,所述方法,包括:An embodiment of the present invention provides a UAV signal detection method, the method comprising:
获取待探测频段内的无线信号,作为待探测信号;Obtain the wireless signal in the frequency band to be detected as the signal to be detected;
按照预设频率间隔,划分所述待探测信号,得到多个频段内的无线子信号,作为待观测信号;Dividing the signal to be detected according to a preset frequency interval to obtain wireless sub-signals in multiple frequency bands as the signal to be observed;
针对每个所述待观测信号,基于预设能量特征公式,提取该待观测信号的能量特征;For each of the signals to be observed, based on a preset energy characteristic formula, extracting an energy feature of the signal to be observed;
针对每个所述待观测信号,基于预设累积量特征公式,提取该待观测信号的累积量特征;For each of the signals to be observed, based on a preset cumulant characteristic formula, extract the cumulant feature of the signal to be observed;
针对每个所述待观测信号,将该待观测信号的所述能量特征与所述累积量特征作为输入数据,输入预先训练的支持向量机SVM分类器,利用所述SVM分类器中的分类决策函数,确定所述待探测信号中是否包含有无人机信号,其中,所述SVM分类器是基于样本信号的特征数据训练得到的,所述样本信号的特征数据包括由所述样本信号中每个样本子信号的能量特征及累积量特征共同构成的样本支持向量,以及所述样本子信号对应的标记值,所述标记值表示所述样本子信号中是否存在无人机信号。For each of the signals to be observed, the energy feature and the cumulant feature of the signal to be observed are used as input data, input into a pre-trained support vector machine SVM classifier, and the classification decision in the SVM classifier is used function to determine whether the signal to be detected contains a UAV signal, wherein the SVM classifier is trained based on the characteristic data of the sample signal, and the characteristic data of the sample signal includes each A sample support vector composed of energy features and cumulant features of each sample sub-signal, and a flag value corresponding to the sample sub-signal, the flag value indicating whether there is a UAV signal in the sample sub-signal.
进一步的,针对每个所述待观测信号,基于预设能量特征公式,提取该待观测信号的能量特征,包括:Further, for each of the signals to be observed, based on a preset energy characteristic formula, the energy characteristics of the signal to be observed are extracted, including:
针对每个所述待观测信号,基于帕塞瓦尔定理,计算该待观测信号对应的能量值,作为能量特征,其中,针对第i个频段对应的所述待观测信号yi(t),其所述能量特征Ei表示为:For each signal to be observed, based on Parseval's theorem, calculate the energy value corresponding to the signal to be observed as an energy feature, wherein, for the signal to be observed y i (t) corresponding to the i-th frequency band, its The energy feature E i is expressed as:
Yi(f)表示该待观测信号yi(t)对应的频域信号,f1表示第i个频段对应的下边界频率,f2表示第i个频段对应的上边界频率,Δf表示在第i个频段中的频率采样间隔,t表示时间,f表示频率。Y i (f) represents the frequency domain signal corresponding to the signal to be observed y i (t), f 1 represents the lower boundary frequency corresponding to the i-th frequency band, f 2 represents the upper boundary frequency corresponding to the i-th frequency band, Δf represents the The frequency sampling interval in the i-th frequency band, t represents time, and f represents frequency.
进一步的,针对每个所述待观测信号,基于预设累积量特征公式,提取该待观测信号的累积量特征,包括:Further, for each of the signals to be observed, based on a preset cumulant characteristic formula, the cumulant feature of the signal to be observed is extracted, including:
针对每个所述待观测信号,计算该待观测信号的二阶累积量、四阶累积量和六阶累积量,其中,针对每个所述待观测信号,所述二阶累积量C21表示为:For each of the signals to be observed, calculate the second-order cumulant, the fourth-order cumulant, and the sixth-order cumulant of the signal to be observed, wherein, for each of the signals to be observed, the second-order cumulant C 21 represents for:
C21=E(|y(k)2|)-|Ey(k)2|C 21 =E(|y(k) 2 |)-|Ey(k) 2 |
y(k)表示第k个所述待观测信号,E(·)表示数学期望,|·|表示取模符号,C21中的1表示引入一个共轭复数;y(k) represents the kth described signal to be observed, E( ) represents mathematical expectation, |·| represents a modulus symbol, and 1 in C 21 represents the introduction of a conjugate complex number;
针对每个所述待观测信号,所述四阶累计量C42表示为:For each signal to be observed, the fourth-order cumulant C 42 is expressed as:
C42=E(|y(k)4|)-|Ey(k)2|2-2(E(|y(k)2|))2 C 42 =E(|y(k) 4 |)-|Ey(k) 2 | 2 -2(E(|y(k) 2 |)) 2
y(k)表示第k个所述待观测信号,E(·)表示数学期望,|·|表示取模符号,C42中的2表示引入两个共轭复数;y(k) represents the kth described signal to be observed, E( ) represents mathematical expectation, |·| represents a modulus symbol, and 2 in C 42 represents the introduction of two conjugated complex numbers;
针对每个所述待观测信号,所述六阶累积量C63表示为:For each signal to be observed, the sixth-order cumulant C 63 is expressed as:
C63=E(|y(k)6|)-9E(|y(k)|4)E(|y(k)|2)-3E(y*(k)3y(k))E(y(k)2)-3E(y*(k)y(k)3)E(y*(k)2)-18E(y*(k)2)E(y(k)2)E(|y(k)|2)-12(E(|y(k)|2))3 C 63 =E(|y(k) 6 |)-9E(|y(k)| 4 )E(|y(k)| 2 )-3E(y * (k) 3 y(k))E( y(k) 2 )-3E(y * (k)y(k) 3 )E(y * (k) 2 )-18E(y * (k) 2 )E(y(k) 2 )E(| y(k)| 2 )-12(E(|y(k)| 2 )) 3
y(k)表示第k个所述待观测信号,E(·)表示数学期望,|·|表示取模符号,*表示共轭符号,C63中的3表示引入三个共轭复数;y(k) represents the kth described signal to be observed, E(·) represents mathematical expectation, |·| represents a modulus symbol, * represents a conjugate symbol, and 3 in C 63 represents the introduction of three conjugate complex numbers;
针对每个所述待观测信号,根据该待观测信号的所述二阶累积量、四阶累积量和六阶累积量,按照预设累积量特征公式,确定该待观测信号的第一累计量特征和第二累积量特征,作为累积量特征,其中,针对每个所述待观测信号,所述累积量特征表示为:For each of the signals to be observed, according to the second-order cumulant, fourth-order cumulant, and sixth-order cumulant of the signal to be observed, according to the preset cumulant characteristic formula, determine the first cumulant of the signal to be observed feature and the second cumulant feature, as the cumulant feature, wherein, for each of the signals to be observed, the cumulant feature is expressed as:
γ1表示所述第一累积量特征,γ2表示所述第二累积量特征,y(k)表示第k个所述待观测信号,C21表示所述二阶累积量,C42表示所述四阶累积量,C63表示所述六阶累积量,C21中的1表示引入一个共轭复数,C42中的2表示引入两个共轭复数,C63中的3表示引入三个共轭复数。γ 1 represents the first cumulant feature, γ 2 represents the second cumulant feature, y(k) represents the kth signal to be observed, C 21 represents the second-order cumulant, and C 42 represents the The fourth-order cumulant is described, C 63 indicates the sixth-order cumulant, 1 in C 21 indicates the introduction of a conjugate complex number, 2 in C 42 indicates the introduction of two conjugate complex numbers, and 3 in C 63 indicates the introduction of three Conjugate plurals.
进一步的,针对每个所述待观测信号,将该待观测信号的所述能量特征与所述累积量特征作为输入数据,输入预先训练的支持向量机SVM分类器,利用所述SVM分类器中的分类决策函数,确定所述待探测信号中是否包含有无人机信号,包括:Further, for each of the signals to be observed, the energy feature and the cumulant feature of the signal to be observed are used as input data, and input into a pre-trained support vector machine SVM classifier, using the SVM classifier The classification decision function of determining whether the signal to be detected contains a UAV signal, including:
针对每个所述待观测信号,将该待观测信号的所述能量特征和所述累积量特征作为向量元素,生成一个对应的支持向量,其中,第n个所述待观测信号对应的支持向量表示为:For each of the signals to be observed, the energy feature and the cumulant feature of the signal to be observed are used as vector elements to generate a corresponding support vector, wherein the support vector corresponding to the nth signal to be observed Expressed as:
En表示该待观测信号的所述能量特征,γn1表示该待观测信号的所述第一累积量特征,γn2表示该观测信号的所述第二累积量特征; En represents the energy feature of the signal to be observed, γ n1 represents the first cumulant feature of the signal to be observed, and γ n2 represents the second cumulant feature of the observed signal;
将所述支持向量作为输入数据,输入预先训练的SVM分类器中;Using the support vectors as input data into a pre-trained SVM classifier;
根据所述SVM分类器中的分类决策函数,确定所述待观测信号对应的值,作为决策值,其中,所述分类决策函数f(mn)表示为:According to the classification decision function in the SVM classifier, determine the value corresponding to the signal to be observed as a decision value, wherein the classification decision function f(m n ) is expressed as:
分类决策函数f(mn)的值表示第n个所述待观测信号对应的决策值,sign(·)表示符号函数,N表示所述样本子信号的数量,表示第i个样本支持向量对应的拉格朗日乘子的对偶值,<·>表示內积运算,b*表示偏置量的值,mn T表示第n个所述待观测信号的能量特征和累积量特征对应的支持向量的转置,xi表示第i个样本子信号的能量特征和累积量特征对应的样本支持向量,yi表示第i个样本子信号对应的标记值;The value of the classification decision function f(m n ) represents the decision value corresponding to the nth signal to be observed, sign( ) represents a sign function, and N represents the number of sample sub-signals, Indicates the dual value of the Lagrangian multiplier corresponding to the i-th sample support vector, <·> indicates the inner product operation, b * indicates the value of the bias, m n T indicates the energy of the n-th signal to be observed The transposition of the support vector corresponding to the feature and the cumulant feature, x i represents the energy feature of the i-th sample sub-signal and the sample support vector corresponding to the cumulant feature, and y i represents the label value corresponding to the i-th sample sub-signal;
基于每个所述待观测信号的所述决策值,确定所述待探测信号中是否包含有无人机信号,当所述决策值中至少有一个值为1时,则表示该待探测信号中包含无人机信号,当所述决策值全部为-1时,则表示该待探测信号中没有包含无人机信号。Based on the decision value of each signal to be observed, it is determined whether the signal to be detected contains a drone signal, and when at least one of the decision values is 1, it means that the signal to be detected is UAV signals are included, and when the decision values are all -1, it means that the signals to be detected do not include UAV signals.
进一步的,SVM分类器的训练过程,包括:Further, the training process of the SVM classifier includes:
按照预设条件,在待探测频段中获取预设数量的无线信号,作为样本信号;Obtain a preset number of wireless signals in the frequency band to be detected according to preset conditions as sample signals;
按照预设频率间隔,划分所述样本信号,得到多个频段内的无线子信号,作为样本子信号;dividing the sample signal according to a preset frequency interval to obtain wireless sub-signals in multiple frequency bands as sample sub-signals;
针对每个所述样本子信号,提取该样本子信号的能量特征和累积量特征;For each of the sample sub-signals, extracting energy features and cumulant features of the sample sub-signals;
基于所述预设数量的样本信号,将由所述样本信号中样本子信号的能量特征和累积量特征构成的样本支持向量,以及所述样本子信号对应的标记值作为输入数据,输入采用当前分类器参数以及预设结构的SVM分类器,利用当前分类决策函数,对所述预设数量的样本信号进行分类,完成一轮训练,其中,在首次训练时,所述当前分类器参数为预设初始分类器参数,所述当前分类决策函数是根据所述预设初始分类器参数确定的;Based on the preset number of sample signals, the sample support vector composed of the energy feature and cumulant feature of the sample sub-signal in the sample signal, and the label value corresponding to the sample sub-signal are used as input data, and the input adopts the current classification The SVM classifier with parameters and a preset structure uses the current classification decision function to classify the preset number of sample signals to complete a round of training, wherein, during the first training, the parameters of the current classifier are preset initial classifier parameters, the current classification decision function is determined according to the preset initial classifier parameters;
针对每一轮训练,基于所述预设数量的样本信号,按照预设损失函数,确定所述SVM分类器的损失值;For each round of training, based on the preset number of sample signals, according to a preset loss function, determine the loss value of the SVM classifier;
当基于所述损失值确定所述SVM分类器达到预设标准时,完成训练,确定所述SVM分类器对应的分类决策函数;When it is determined based on the loss value that the SVM classifier reaches a preset standard, the training is completed, and the classification decision function corresponding to the SVM classifier is determined;
当基于所述损失值确定所述SVM分类器未达到预设标准时,按照预设调整方式,调整所述分类器参数,得到新的分类决策函数,并采用新的分类决策函数和所述预设数量的样本信号,完成新一轮训练。When it is determined based on the loss value that the SVM classifier does not meet the preset standard, adjust the classifier parameters according to the preset adjustment method to obtain a new classification decision function, and adopt the new classification decision function and the preset Number of sample signals to complete a new round of training.
进一步的,针对每一轮训练,基于所述预设数量的样本信号,按照预设损失函数,确定所述SVM分类器的损失值,包括:Further, for each round of training, based on the preset number of sample signals, according to a preset loss function, the loss value of the SVM classifier is determined, including:
在每一轮训练完成之后,基于铰链损失函数,确定所述SVM分类器损失值,其中,所述铰链损失函数P(x)表示为:After each round of training is completed, based on the hinge loss function, the SVM classifier loss value is determined, wherein the hinge loss function P(x) is expressed as:
所述铰链损失函数P(x)的值表示所述损失值,λ表示待调参数,w表示所述SVM分类器中分割面的法向量,||·||表示取范数,N表示所述样本子信号的数量,wT表示w的转置,xi表示由第i个样本子信号的能量特征和累积量特征对应的样本支持向量,yi表示第i个样本子信号对应的标记值,b表示截距。The value of the hinge loss function P(x) represents the loss value, λ represents the parameter to be adjusted, w represents the normal vector of the segmentation plane in the SVM classifier, ||·|| represents the norm, and N represents the The number of sample sub-signals described above, w T represents the transpose of w, x i represents the sample support vector corresponding to the energy feature and cumulant feature of the i-th sample sub-signal, y i represents the label corresponding to the i-th sample sub-signal value, and b represents the intercept.
进一步的,当基于所述损失值确定所述SVM分类器未达到预设标准时,按照预设调整方式,调整所述分类器参数,得到新的分类决策函数,并采用新的分类决策函数和所述预设数量的样本信号,完成新一轮训练,包括:Further, when it is determined based on the loss value that the SVM classifier does not meet the preset standard, the parameters of the classifier are adjusted according to the preset adjustment method to obtain a new classification decision function, and the new classification decision function and the set The preset number of sample signals is used to complete a new round of training, including:
当基于所述损失值确定所述SVM分类器未达到预设标准时,基于所述铰链损失函数,利用梯度下降法,确定所述铰链损失函数中的所述待调参数的值;When it is determined based on the loss value that the SVM classifier does not meet the preset standard, based on the hinge loss function, using a gradient descent method to determine the value of the parameter to be adjusted in the hinge loss function;
根据所述待调参数的值,重新确定所述SVM分类器对应的分类决策函数;Re-determine the classification decision function corresponding to the SVM classifier according to the value of the parameter to be adjusted;
根据重新确定的分类决策函数对所述预设数量的样本信号进行分类,完成新一轮训练。Classify the preset number of sample signals according to the re-determined classification decision function to complete a new round of training.
进一步的,根据所述待调参数的值,重新确定所述SVM分类器对应的分类决策函数,包括:Further, according to the value of the parameter to be adjusted, re-determine the classification decision function corresponding to the SVM classifier, including:
根据所述待调参数的值,确定所述SVM分类器的惩罚参数,其中,所述惩罚参数表示调节优化方向中函数间隔以及分类准确度的权重,所述惩罚参数D与所述待调参数λ之间的关系表示为:According to the value of the parameter to be adjusted, determine the penalty parameter of the SVM classifier, wherein the penalty parameter represents the weight of adjusting the function interval and classification accuracy in the optimization direction, the penalty parameter D and the parameter to be adjusted The relationship between λ is expressed as:
基于所述惩罚参数,利用凸优化问题,确定所述SVM分类器对应分割面的划分方式,其中,所述分割面的划分方式表示利用超平面对所述预设数量的样本信号进行分类的方法,具体表示为:Based on the penalty parameter, using a convex optimization problem, determine the division method corresponding to the segmentation surface of the SVM classifier, wherein the division method of the segmentation surface represents a method for classifying the preset number of sample signals using a hyperplane , specifically expressed as:
s.t.yi(wTxi+b)≥1-ξi,i=1,2,...,Nsty i (w T x i +b)≥1-ξ i , i=1, 2,..., N
ξi≥0,i=1,2,...,Nξ i ≥ 0, i = 1, 2, ..., N
s.t.表示约束条件,w表示所述超平面的法向量,||·||表示取范数,D表示惩罚参数,N表示所述样本子信号的数量,ξi表示第i个样本支持向量对应的松弛变量,yi(wTxi+b)表示第i个样本支持向量到分割面的间隔距离,yi表示第i个样本子信号对应的标记值,wT表示w的转置,xi表示第i个样本支持向量,b表示截距;st represents the constraint condition, w represents the normal vector of the hyperplane, ||·|| represents the norm, D represents the penalty parameter, N represents the number of the sample sub-signals, ξ i represents the i-th sample support vector corresponding to The slack variable of , y i (w T x i +b) represents the interval distance from the i-th sample support vector to the segmentation plane, y i represents the label value corresponding to the i-th sample sub-signal, w T represents the transposition of w, x i represents the i-th sample support vector, b represents the intercept;
基于拉格朗日函数,以及函数对偶化方法,转换所述划分方式的求解问题,转换后所述划分方式表示为:Based on the Lagrangian function and the function dualization method, the solution problem of the division method is converted, and the division method after conversion is expressed as:
0≤αi≤D,i=1,2,...,N0≤α i ≤D, i=1, 2,...,N
s.t.表示约束条件,αi表示第i个样本支持向量对应的拉格朗日乘子的值,αj表示第j个样本支持向量对应的拉格朗日乘子的值,yi表示第i个样本子信号对应的标记值,yj表示第i个样本子信号对应的标记值,第i个样本子信号对应的样本支持向量的转置,xj表示第j个样本子信号对应的样本支持向量,<·>表示內积运算,D表示惩罚参数,N表示样本子信号的数量;st represents the constraint condition, α i represents the value of the Lagrangian multiplier corresponding to the i-th sample support vector, α j represents the value of the Lagrange multiplier corresponding to the j-th sample support vector, and y i represents the value of the i-th sample support vector The tag value corresponding to the sample sub-signal, y j represents the tag value corresponding to the i-th sample sub-signal, The transpose of the sample support vector corresponding to the i-th sample sub-signal, x j represents the sample support vector corresponding to the j-th sample sub-signal, <·> represents the inner product operation, D represents the penalty parameter, and N represents the number of sample sub-signals ;
基于SMO算法,确定拉格朗日乘子的值;Determine the value of the Lagrange multiplier based on the SMO algorithm;
利用样本支持向量的性质,确定偏置量的值,其中,所述所述偏置量的值b*表示为:Utilize the properties of the sample support vector to determine the value of the bias, wherein the value b * of the bias is expressed as:
N表示样本子信号的数量,yn表示第n个样本支持向量对应的标记值,yi表示第i个样本支持向量对应的标记值,表示第i个样本支持向量对应的拉格朗日乘子的对偶值,表示第i个样本支持向量的转置,xn表示第n个样本子信号对应的样本支持向量,<·>表示內积运算;N represents the number of sample sub-signals, y n represents the label value corresponding to the n-th sample support vector, y i represents the label value corresponding to the i-th sample support vector, Indicates the dual value of the Lagrangian multiplier corresponding to the i-th sample support vector, Indicates the transpose of the i-th sample support vector, x n indicates the sample support vector corresponding to the n-th sample sub-signal, <·> indicates the inner product operation;
基于所述拉格朗日乘子的值和所述偏置量的值,重新确定分类决策函数。Based on the value of the Lagrangian multiplier and the value of the bias, the classification decision function is re-determined.
本发明实施例还提供了一种用于无人机信号探测装置,所述装置,包括:The embodiment of the present invention also provides a signal detection device for unmanned aerial vehicles, the device, including:
待探测信号获取模块,用于获取待探测频段内的无线信号,作为待探测信号;The signal acquisition module to be detected is used to obtain the wireless signal in the frequency band to be detected as the signal to be detected;
待观测信号获取模块,用于按照预设频率间隔,划分所述待探测信号,得到多个频段内的无线子信号,作为待观测信号;The signal to be observed acquisition module is used to divide the signal to be detected according to the preset frequency interval, and obtain wireless sub-signals in multiple frequency bands as the signal to be observed;
能量特征提取模块,用于针对每个所述待观测信号,基于预设能量特征公式,提取该待观测信号的能量特征;An energy feature extraction module, configured to extract the energy feature of the signal to be observed based on a preset energy feature formula for each signal to be observed;
累积量特征提取模块,用于针对每个所述待观测信号,基于预设累积量特征公式,提取该待观测信号的累积量特征;A cumulant feature extraction module, configured to extract the cumulant feature of the signal to be observed based on a preset cumulant feature formula for each of the signals to be observed;
无人机信号确定模块,用于针对每个所述待观测信号,将该待观测信号的所述能量特征与所述累积量特征作为输入数据,输入预先训练的SVM分类器,利用所述SVM分类器中的分类决策函数,确定所述待探测信号中是否包含有无人机信号,其中,所述SVM分类器是基于样本信号的特征数据训练得到的,所述样本信号的特征数据包括由所述样本信号中每个样本子信号的能量特征及累积量特征共同构成的样本支持向量,以及所述样本子信号对应的标记值,所述标记值表示所述样本子信号中是否存在无人机信号。The unmanned aerial vehicle signal determination module is used for each of the signals to be observed, using the energy feature and the cumulative feature of the signal to be observed as input data, inputting a pre-trained SVM classifier, using the SVM The classification decision function in the classifier determines whether the unmanned aerial vehicle signal is included in the signal to be detected, wherein the SVM classifier is trained based on the feature data of the sample signal, and the feature data of the sample signal includes A sample support vector formed by the energy feature and cumulant feature of each sample sub-signal in the sample signal, and a flag value corresponding to the sample sub-signal, the flag value indicating whether there is no one in the sample sub-signal machine signal.
进一步的,所述装置,还包括:Further, the device also includes:
样本信号获取模块,用于按照预设条件,在待探测频段中获取预设数量的无线信号,作为样本信号;The sample signal acquisition module is used to acquire a preset number of wireless signals in the frequency band to be detected as sample signals according to preset conditions;
样本子信号获取模块,用于按照预设频率间隔,划分所述样本信号,得到多个频段内的无线子信号,作为样本子信号;A sample sub-signal acquisition module, configured to divide the sample signal according to a preset frequency interval, and obtain wireless sub-signals in multiple frequency bands as sample sub-signals;
特征数据获取模块,用于针对每个所述样本子信号,提取该样本子信号的能量特征和累积量特征;A feature data acquisition module, configured to extract energy features and cumulant features of the sample sub-signal for each of the sample sub-signals;
样本信号分类模块,用于基于所述预设数量的样本信号,将由所述样本信号中样本子信号的能量特征和累积量特征构成的样本支持向量,以及所述样本子信号对应的标记值作为输入数据,输入采用当前分类器参数以及预设结构的SVM分类器,利用当前分类决策函数,对所述预设数量的样本信号进行分类,完成一轮训练,其中,在首次训练时,所述当前分类器参数为预设初始分类器参数,所述当前分类决策函数是根据所述预设初始分类器参数确定的;A sample signal classification module, configured to use, based on the preset number of sample signals, a sample support vector composed of energy features and cumulant features of sample sub-signals in the sample signal, and a marker value corresponding to the sample sub-signal as Input data, input an SVM classifier using current classifier parameters and a preset structure, use the current classification decision function to classify the preset number of sample signals, and complete a round of training, wherein, during the first training, the said The current classifier parameters are preset initial classifier parameters, and the current classification decision function is determined according to the preset initial classifier parameters;
损失值确定模块,用于针对每一轮训练,基于所述预设数量的样本信号,按照预设损失函数,确定所述SVM分类器的损失值;A loss value determination module, configured to determine the loss value of the SVM classifier according to a preset loss function based on the preset number of sample signals for each round of training;
分类器生成模块,用于当基于所述损失值确定所述SVM分类器达到预设标准时,完成训练,确定所述SVM分类器对应的分类决策函数;A classifier generating module, configured to complete training when determining that the SVM classifier reaches a preset standard based on the loss value, and determine a classification decision function corresponding to the SVM classifier;
分类器参数调整模块,用于当基于所述损失值确定所述SVM分类器未达到预设标准时,按照预设调整方式,调整所述分类器参数,得到新的分类决策函数,并采用新的分类决策函数和所述预设数量的样本信号,完成新一轮训练。A classifier parameter adjustment module, configured to adjust the classifier parameters according to a preset adjustment method when it is determined based on the loss value that the SVM classifier does not reach a preset standard, to obtain a new classification decision function, and adopt a new Classify the decision function and the preset number of sample signals to complete a new round of training.
本发明实施例提供的一种无人机信号探测方法及装置,可以获取待探测频段内无线信号,作为待探测信号,按照预设频率间隔,划分待探测信号,得到多个频段内的无线子信号,作为待观测信号,针对每个待观测信号,基于预设能量特征公式,提取该待观测信号的能量特征,基于预设累积量特征公式,提取该待观测信号的累积量特征,针对每个观测信号,将该观测信号的能量特征与累积量特征作为输入数据,输入预先训练的SVM分类器,利用SVM分类器中的分类决策函数,确定待探测信号中是否包含有无人机信号。通过上述方法可以实现对无线环境中无人机信号的探测。A UAV signal detection method and device provided by the embodiments of the present invention can obtain wireless signals in frequency bands to be detected as signals to be detected, divide the signals to be detected according to preset frequency intervals, and obtain wireless sub-bands in multiple frequency bands. Signal, as the signal to be observed, for each signal to be observed, based on the preset energy characteristic formula, extract the energy feature of the signal to be observed, based on the preset cumulant characteristic formula, extract the cumulant feature of the signal to be observed, for each An observation signal, the energy feature and cumulant feature of the observation signal are used as input data, input into the pre-trained SVM classifier, and the classification decision function in the SVM classifier is used to determine whether the signal to be detected contains UAV signals. The detection of the UAV signal in the wireless environment can be realized through the above method.
当然,实施本发明的任一产品或方法并不一定需要同时达到以上所述的所有优点。Of course, implementing any product or method of the present invention does not necessarily need to achieve all the above-mentioned advantages at the same time.
附图说明Description of drawings
为了更清楚地说明本发明实施例或现有技术中的技术方案,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的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.
图1为本发明实施例提供的一种无人机信号探测方法的流程图之一;Fig. 1 is one of flow charts of a kind of unmanned aerial vehicle signal detection method provided by the embodiment of the present invention;
图2为本发明实施例提供的一种无人机信号探测的SVM分类器训练方法流程图;Fig. 2 is a flow chart of a SVM classifier training method for UAV signal detection provided by an embodiment of the present invention;
图3为本发明实施例提供的一种SVM分类器对样本信号分类的示意图;FIG. 3 is a schematic diagram of classifying sample signals by an SVM classifier provided by an embodiment of the present invention;
图4为本发明实施例提供的一种无人机信号探测方法的流程图以二;Fig. 4 is the flowchart of a kind of unmanned aerial vehicle signal detecting method provided by the embodiment of the present invention;
图5为本发明实施例提供的一种无人机信号探测装置的结构示意图之一;FIG. 5 is one of the structural schematic diagrams of a UAV signal detection device provided by an embodiment of the present invention;
图6为本发明实施例提供的一种无人机信号探测装置的结构示意图之二。Fig. 6 is the second structural schematic diagram of a UAV signal detection device provided by an embodiment of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
本发明实施例提供了一种无人机信号探测方法,如图1所示,可以包括以下步骤:The embodiment of the present invention provides a UAV signal detection method, as shown in Figure 1, may include the following steps:
步骤S101,获取待探测频段内的无线信号,作为待探测信号。Step S101, acquiring a wireless signal in a frequency band to be detected as a signal to be detected.
在本步骤中,利用天线、信号接收器等在所需探测的目标环境中,按照预设时间间隔,获取待探测频段内的无线信号,作为待探测信号。In this step, use antennas, signal receivers, etc. to obtain wireless signals in the frequency band to be detected in the target environment to be detected according to preset time intervals as signals to be detected.
具体的,根据目前已知的一种无人机使用频段的规定,可以供无人机工作的频段有840.5MHz至845MHz、1430MHz至1444MHz、以及2408MHz至2440MHz,假设现需要对2408MHz至2440MHz这一频段内的无人机信号进行探测,则待探测频段就是2408MHz至2440MHz,每隔2分钟,利用信号接收器对所需探测的目标环境中的无线信号进行采集,将采集到的无线信号作为待探测信号。Specifically, according to the currently known regulations on frequency bands used by drones, the frequency bands that can be used for drones are 840.5MHz to 845MHz, 1430MHz to 1444MHz, and 2408MHz to 2440MHz. The UAV signal in the frequency band is detected, and the frequency band to be detected is 2408MHz to 2440MHz. Every 2 minutes, the signal receiver is used to collect the wireless signal in the target environment to be detected, and the collected wireless signal is used as the target environment to be detected. probe signal.
步骤S102,按照预设频率间隔,划分待探测信号,得到多个频段内的无线子信号,作为待观测信号。Step S102, divide the signal to be detected according to the preset frequency interval, and obtain wireless sub-signals in multiple frequency bands as the signal to be observed.
在本步骤中,可以根据上述待探测信号的频谱图,对待探测信号采用等频率间隔划分,得到每个频段对应的无线子信号,作为待观测信号。In this step, according to the spectrum diagram of the signal to be detected, the signal to be detected can be divided into equal frequency intervals to obtain wireless sub-signals corresponding to each frequency band as the signal to be observed.
步骤S103,针对每个待观测信号,基于预设能量特征公式,提取该待观测信号的能量特征。Step S103, for each signal to be observed, extract the energy feature of the signal to be observed based on a preset energy feature formula.
在本步骤中,针对每个待观测信号,基于帕塞瓦尔定理,计算该待观测信号对应的能量值,作为能量特征。In this step, for each signal to be observed, based on Parseval's theorem, the energy value corresponding to the signal to be observed is calculated as an energy feature.
具体的,针对第i个待观测信号yi(t),利用帕塞瓦尔定理,可以计算得到其能量特征Ei,其中,Ei可以表示为:Specifically, for the i-th signal to be observed y i (t), using Parseval's theorem, its energy feature E i can be calculated, where E i can be expressed as:
Yi(f)表示该待观测信号yi(t)对应的频域信号,f1表示第i个频段对应的下边界频率,f2表示第i个频段对应的上边界频率,Δf表示在第i个频段中的频率采样间隔,t表示时间,f表示频率。Y i (f) represents the frequency domain signal corresponding to the signal to be observed y i (t), f 1 represents the lower boundary frequency corresponding to the i-th frequency band, f 2 represents the upper boundary frequency corresponding to the i-th frequency band, Δf represents the The frequency sampling interval in the i-th frequency band, t represents time, and f represents frequency.
步骤S104,针对每个待观测信号,基于预设累积量特征公式,提取该待观测信号的累积量特征。Step S104 , for each signal to be observed, based on a preset cumulant characteristic formula, extract the cumulant feature of the signal to be observed.
在本步骤中,针对每个待观测信号,基于该观测信号对应的二阶累积量、四阶累积量以及六阶累积量,按照预设累积量公式,提取该待观测信号的第一累积量特征以及第二累积量特征。In this step, for each signal to be observed, based on the second-order cumulant, fourth-order cumulant, and sixth-order cumulant corresponding to the observed signal, the first cumulant of the signal to be observed is extracted according to the preset cumulant formula feature and the second cumulant feature.
具体的,针对第k个待观测信号y(k),其二阶累积量C21可以表示为:Specifically, for the kth signal to be observed y(k), its second-order cumulant C 21 can be expressed as:
C21=E(|y(k)2|)-|Ey(k)2|C 21 =E(|y(k) 2 |)-|Ey(k) 2 |
其四阶累积量C42可以表示为:Its fourth-order cumulant C 42 can be expressed as:
C42=E(|y(k)4|)-|Ey(k)2|2-2(E(|y(k)2|))2 C 42 =E(|y(k) 4 |)-|Ey(k) 2 | 2 -2(E(|y(k) 2 |)) 2
其六阶累积量C63可以表示为:Its sixth-order cumulant C 63 can be expressed as:
C63=E(|y(k)6|)-9E(|y(k)|4)E(|y(k)|2)-3E(y*(k)3y(k))E(y(k)2)-3E(y*(k)y(k)3)E(y*(k)2)-18E(y*(k)2)E(y(k)2)E(|y(k)|2)-12(E(|y(k)|2))3 C 63 =E(|y(k) 6 |)-9E(|y(k)| 4 )E(|y(k)| 2 )-3E(y * (k) 3 y(k))E( y(k) 2 )-3E(y * (k)y(k) 3 )E(y * (k) 2 )-18E(y * (k) 2 )E(y(k) 2 )E(| y(k)| 2 )-12(E(|y(k)| 2 )) 3
在上述二阶累积量、四阶累积量以及六阶累积量的计算公式中,y(k)表示第k个待观测信号,E(·)表示数学期望,|·|表示取模符号,*表示共轭符号,C21中的1表示引入一个共轭复数,C42中的2表示引入两个共轭复数,C63中的3表示引入三个共轭复数。In the calculation formulas of the above-mentioned second-order cumulant, fourth-order cumulant and sixth-order cumulant, y(k) represents the kth signal to be observed, E( ) represents mathematical expectation, |·| represents the modulus symbol, * Indicates the conjugate symbol, 1 in C 21 means to introduce a conjugate complex number, 2 in C 42 means to introduce two conjugate complex numbers, and 3 in C 63 means to introduce three conjugate complex numbers.
根据上述二阶累积量、四阶累积量以及六阶累积量,可以按照预设累积量公式提取每个待观测信号的累积量特征。其中,预设累积量特征可以表示为:According to the above-mentioned second-order cumulant, fourth-order cumulant, and sixth-order cumulant, the cumulant feature of each signal to be observed can be extracted according to a preset cumulant formula. Among them, the preset cumulant feature can be expressed as:
γ1表示第一累积量特征,γ2表示第二累积量特征,y(k)表示第k个待观测信号,C21表示二阶累积量,C42表示四阶累积量,C63表示六阶累积量,C21中的1表示引入一个共轭复数,C42中的2表示引入两个共轭复数,C63中的3表示引入三个共轭复数。γ 1 represents the first cumulant feature, γ 2 represents the second cumulant feature, y(k) represents the kth signal to be observed, C 21 represents the second-order cumulant, C 42 represents the fourth-order cumulant, C 63 represents the sixth-order cumulant The 1 in C 21 means to introduce a conjugate complex number, the 2 in C 42 means to introduce two conjugate complex numbers, and the 3 in C 63 means to introduce three conjugate complex numbers.
由上述可知,由于高斯随机变量的一阶累积量是其随机变量的均值,二阶累积量是其随机变量的方差,而其三阶累积量或者三阶以上的累积量的等于零,因此,高阶累积量对可以对高斯噪声有很好的抑制效果。所有采用本发明实施例,可以抑制无线环境中的噪声信号。It can be known from the above that since the first-order cumulant of a Gaussian random variable is the mean value of its random variable, the second-order cumulant is the variance of its random variable, and its third-order cumulant or the cumulant above the third order is equal to zero, therefore, high The order cumulant pair can have a good suppression effect on Gaussian noise. All the noise signals in the wireless environment can be suppressed by adopting the embodiments of the present invention.
在本发明实施例中,针对每个待观测信号,该待观测信号的累积量特征主要是指根据其二阶累积量、四阶累积量以及六阶累积量得到的第一累积量特征以及第二累积量特征,因此,同理可以引入更高阶的累积量,按照预设累积量公式得到更多的累积量特征,例如,可以引入八阶累积量,计算得到第三累积量特征等。In the embodiment of the present invention, for each signal to be observed, the cumulant feature of the signal to be observed mainly refers to the first cumulant feature and the first cumulant feature obtained according to its second-order cumulant, fourth-order cumulant, and sixth-order cumulant Two cumulant features, therefore, similarly, a higher-order cumulant can be introduced, and more cumulant features can be obtained according to the preset cumulant formula, for example, an eighth-order cumulant can be introduced, and the third cumulant feature can be calculated.
步骤S105,针对每个待观测信号,将该待观测信号的能量特征与累积量特征作为输入数据,输入预先训练的SVM分类器,利用SVM分类器中的分类决策函数,确定待探测信号中是否包含有无人机信号。Step S105, for each signal to be observed, the energy feature and cumulant feature of the signal to be observed are used as input data, input into the pre-trained SVM classifier, and the classification decision function in the SVM classifier is used to determine whether the signal to be detected is Contains drone signals.
在本步骤中,针对每个待观测信号,将该待观测信号的能量特征与累积量特征作为向量元素,生成对应的支持向量,输入预先训练的SVM分类器中,利用分类器的分类决策函数确定,待探测信号中是否有无人机信号。In this step, for each signal to be observed, the energy feature and cumulant feature of the signal to be observed are used as vector elements to generate corresponding support vectors, which are input into the pre-trained SVM classifier, and the classification decision function of the classifier is used Determine whether there is a UAV signal in the signal to be detected.
具体的,针对每个待观测信号,将该待观测信号的能量特征和累积量特征作为向量元素,生成一个对应的支持向量,其中,第n个待观测信号对应的支持向量表示为:Specifically, for each signal to be observed, the energy feature and cumulant feature of the signal to be observed are used as vector elements to generate a corresponding support vector, where the support vector corresponding to the nth signal to be observed is Expressed as:
En表示该待观测信号的能量特征,γn1表示该待观测信号的第一累积量特征,γn2表示该观测信号的第二累积量特征。E n represents the energy feature of the signal to be observed, γ n1 represents the first cumulant feature of the signal to be observed, and γ n2 represents the second cumulant feature of the observed signal.
将支持向量作为输入数据,输入预先训练的SVM分类器中。The support vector is used as the input data and input into the pre-trained SVM classifier.
根据SVM分类器中的分类决策函数,确定待观测信号对应的值,作为决策值,其中,分类决策函数f(mn)表示为:According to the classification decision function in the SVM classifier, determine the value corresponding to the signal to be observed as the decision value, where the classification decision function f(m n ) is expressed as:
分类决策函数f(mn)的值表示第n个待观测信号对应的决策值,sign(·)表示符号函数,N表示样本子信号的数量,表示第i个样本支持向量对应的拉格朗日乘子的对偶值,<·>表示內积运算,b*表示偏置量的值,mn T表示第n个待观测信号的能量特征和累积量特征对应的支持向量的转置,xi表示第i个样本子信号的能量特征和累积量特征对应的样本支持向量,yi表示第i个样本子信号对应的标记值。The value of the classification decision function f(m n ) represents the decision value corresponding to the nth signal to be observed, sign( ) represents the sign function, N represents the number of sample sub-signals, Indicates the dual value of the Lagrangian multiplier corresponding to the support vector of the i-th sample, <·> indicates the inner product operation, b * indicates the value of the bias value, m n T indicates the energy characteristic sum of the n-th signal to be observed The transpose of the support vector corresponding to the cumulant feature, x i represents the energy feature of the i-th sample sub-signal and the sample support vector corresponding to the cumulant feature, and y i represents the label value corresponding to the i-th sample sub-signal.
基于每个待观测信号的决策值,确定待探测信号中是否包含有无人机信号,当决策值中至少有一个值为1时,则表示该待探测信号中包含无人机信号,当决策值全部为-1时,则表示该待探测信号中没有包含无人机信号。Based on the decision value of each signal to be observed, it is determined whether the signal to be detected contains a UAV signal. When at least one of the decision values is 1, it means that the signal to be detected contains a UAV signal. When the decision When the values are all -1, it means that the signals to be detected do not contain UAV signals.
进一步的,由于每个待观测信号是由待探测信号分段后得到的,因此,通过判断每一个待观测信号中是否包含有无人机信号,可以确定待探测信号中是否包含有无人机信号。因此将每一个待观测信号的能量特征以及累积量特征作为向量元素,生成对应的支持向量。利用预先训练的SVM分类器分类决策函数中的分类决策函数对每个待观测信号的进行分类,由于分类决策函数属于符号函数,其输出值只用1和-1,因此在SVM分类器训练时可以用1表示有无人机信号,-1表示没有无人机信号。当待观测信号中有无人机信号时,SVM分类器的决策值应当为1,当待观测信号中没有无人机信号时,SVM分类器的决策值应当为-1。所以,针对待探测信号所包含的所有待观测信号,当其决策值中至少有一个值为1时,表示该待探测信号中包含无人机信号,当其决策值全部为-1时,则表示该待探测信号中没有包含无人机信号。Further, since each signal to be observed is obtained by segmenting the signal to be detected, by judging whether each signal to be observed contains a UAV signal, it can be determined whether the signal to be detected contains a UAV Signal. Therefore, the energy feature and cumulant feature of each signal to be observed are used as vector elements to generate the corresponding support vector. Use the classification decision function in the classification decision function of the pre-trained SVM classifier to classify each signal to be observed. Since the classification decision function is a symbolic function, its output value only uses 1 and -1, so when the SVM classifier is trained You can use 1 to indicate that there is a drone signal, and -1 to indicate that there is no drone signal. When there is a UAV signal in the signal to be observed, the decision value of the SVM classifier should be 1, and when there is no UAV signal in the signal to be observed, the decision value of the SVM classifier should be -1. Therefore, for all the signals to be observed included in the signal to be detected, when at least one of its decision values is 1, it means that the signal to be detected contains UAV signals; when all of its decision values are -1, then Indicates that the signals to be detected do not contain UAV signals.
在本发明实施例中,上述SVM分类器的结构可以采用现有的网络结构,例如,前馈型网络结构。通过将每个待观测信号与训练时的样本子信号进行全连接,按照上述分类决策函数中的参数计算顺序,实现对待观测信号中无人机信号的识别,进而实现对待探测信号中无人机信号的识别。In the embodiment of the present invention, the structure of the above SVM classifier may adopt an existing network structure, for example, a feed-forward network structure. By fully connecting each signal to be observed with the sample sub-signal during training, according to the parameter calculation sequence in the above classification decision function, the recognition of the UAV signal in the signal to be observed is realized, and then the UAV signal in the signal to be detected is realized. Signal recognition.
综上所述,在本发明实施例中,针对待观测信号,利用高阶累积量可以抑制噪声信号的特性,对待观测信号的中的噪声信号进行抑制,再利用SVM分类器识别待探测信号中的干扰信号和无人机信号,实现对无线环境中无人机信号的探测。To sum up, in the embodiment of the present invention, for the signal to be observed, the characteristics of the noise signal can be suppressed by using the high-order cumulant, and the noise signal in the signal to be observed is suppressed, and then the SVM classifier is used to identify the noise signal in the signal to be detected The jamming signal and UAV signal can realize the detection of UAV signal in the wireless environment.
在上述一种无人机信号探测方法中的一个实施例中,上述步骤S105中SVM分类器的训练过程,如图2所示,可以包括以下步骤:In one embodiment of above-mentioned a kind of unmanned aerial vehicle signal detection method, the training process of SVM classifier in the above-mentioned step S105, as shown in Figure 2, can comprise the following steps:
步骤S201,按照预设条件,在待探测频段中获取预设数量的无线信号,作为样本信号。Step S201, according to preset conditions, acquire a preset number of wireless signals in the frequency band to be detected as sample signals.
在本步骤中,可以在有无人机信号的情况下,在待探测频段中获取预设数量的无线信号,作为正样本信号;在没有无人机信号的情况下,在待探测频段中获取预设数量的样本信号,作为负样本信号。In this step, in the case of UAV signals, a preset number of wireless signals can be obtained in the frequency band to be detected as positive sample signals; A preset number of samples of the signal, as a negative sample signal.
具体的,可以在上述目标环境中,利用无人机模拟无人机信号的有无,并在待探测频段内获取预设数量的正样本信号和负样本信号。例如,假设待探测的频段是2408MHz至2440MHz,在目标环境中,利用无人机发送2410MHz的无人机信号,以30秒为时间间隔,在待探测频段中获取1000份的无线信号,作为正样本信号;同理,在相同条件下,没有无人机发送无人机信号,获取1000份的无线信号,作为负样本信号。Specifically, in the above-mentioned target environment, the UAV can be used to simulate the presence or absence of the UAV signal, and a preset number of positive sample signals and negative sample signals can be obtained in the frequency band to be detected. For example, assuming that the frequency band to be detected is 2408MHz to 2440MHz, in the target environment, use the UAV to send 2410MHz UAV signal, and take 30 seconds as the time interval to obtain 1000 copies of wireless signals in the frequency band to be detected as positive Sample signal; Similarly, under the same conditions, no drone sends a drone signal, and 1000 wireless signals are obtained as negative sample signals.
步骤S202,按照预设频率间隔,划分样本信号,得到多个频段内的无线子信号,作为样本子信号。Step S202, divide the sample signal according to preset frequency intervals to obtain wireless sub-signals in multiple frequency bands as sample sub-signals.
在本步骤中,针对每个样本信号,根据该样本信号的频谱图,对待样本信号采用等频率间隔划分,得到每个频段对应的无线子信号,作为样本子信号。In this step, for each sample signal, according to the spectrum diagram of the sample signal, the sample signal to be treated is divided into equal frequency intervals to obtain wireless sub-signals corresponding to each frequency band as sample sub-signals.
步骤S203,针对每个样本子信号,提取该样本子信号的能量特征和累积量特征。Step S203, for each sample sub-signal, extract the energy feature and cumulant feature of the sample sub-signal.
在本步骤中,可以根据上述步骤S103和步骤S104相同的方式,确定每个样本子信号的能量特征以及累积量特征。In this step, the energy feature and cumulant feature of each sample sub-signal can be determined in the same manner as the above step S103 and step S104.
具体的,针对每个样本子信号,基于预设能量特征公式,提取样本子信号的能量特征,其中,针对第i个频段内的样本子信号yi′(t)的能量特征Ei′表示为:Specifically, for each sample sub-signal, the energy feature of the sample sub-signal is extracted based on the preset energy feature formula, where the energy feature E i ′ of the sample sub-signal y i ′(t) in the i-th frequency band represents for:
Yi′(f)表示第i个频段内样本子信号yi′(t)对应的频域信号,f1表示第i个频段对应的下边界频率,f2表示第i个频段对应的上边界频率,Δf表示在第i个频段中的频率的采样间隔,t表示时间,f表示频率。Y i ′(f) represents the frequency domain signal corresponding to the sample sub-signal y i ′(t) in the i-th frequency band, f 1 represents the lower boundary frequency corresponding to the i-th frequency band, and f 2 represents the upper boundary frequency corresponding to the i-th frequency band Boundary frequency, Δf represents the sampling interval of the frequency in the i-th frequency band, t represents time, and f represents frequency.
针对每个样本子信号,按照预设累积量特征公式,确定该样本子信号的第一累计量特征和第二累积量特征,作为累积量特征,其中,第k个样本子信号y′(k),其第一累积量特征γ1′以及第二累积量特征γ2′表示为:For each sample sub-signal, according to the preset cumulant feature formula, determine the first cumulant feature and the second cumulant feature of the sample sub-signal as the cumulant feature, wherein the kth sample sub-signal y'(k ), the first cumulant feature γ 1 ′ and the second cumulant feature γ 2 ′ are expressed as:
C21′表示该样本子信号的二阶累积量,C42′表示样本子信号的四阶累积量,C63′表示该样本子信号的六阶累积量,C21′中的1表示引入一个共轭复数,C42′中的2表示引入两个共轭复数,C63′中的3表示引入三个共轭复数。C 21 ′ represents the second-order cumulant of the sample sub-signal, C 42 ′ represents the fourth-order cumulant of the sample sub-signal, C 63 ′ represents the sixth-order cumulant of the sample sub-signal, and 1 in C 21 ′ represents the introduction of a For conjugate complex numbers, 2 in C 42 ′ means introducing two conjugate complex numbers, and 3 in C 63 ′ means introducing three conjugate complex numbers.
步骤S204,基于预设数量的样本信号,将由样本信号中样本子信号的能量特征和累积量特征构成的样本支持向量,以及样本子信号对应的标记值作为输入数据,输入采用当前分类器参数以及预设结构的SVM分类器,利用当前分类决策函数,对预设数量的样本信号进行分类,完成一轮训练。Step S204, based on the preset number of sample signals, the sample support vector composed of the energy feature and cumulant feature of the sample sub-signal in the sample signal, and the label value corresponding to the sample sub-signal are used as input data, and the input adopts the current classifier parameters and The SVM classifier with a preset structure uses the current classification decision function to classify a preset number of sample signals to complete a round of training.
在步骤中,基于预设数量的样本信号,将由样本信号中的样本子信号的能量特征和累积量特征生成对应的样本支持向量,将每一个样本支持向量以及其对应的样本子信号的标记值,作为输入数据,输入预设结构的具有当前分类器参数的SVM分类器中,利用当前的分类决策函数,对预设数量的样本信号进行分类,完成一轮训练。In the step, based on the preset number of sample signals, the corresponding sample support vectors will be generated from the energy features and cumulant features of the sample sub-signals in the sample signals, and each sample support vector and the label value of its corresponding sample sub-signal , as the input data, input into the SVM classifier with the current classifier parameters of the preset structure, use the current classification decision function to classify the preset number of sample signals, and complete a round of training.
具体的,SVM分类器的一轮训练,需要利用SVM分类器对上述预设数量的样本信号对应的所有样本子信号进行分类。举例来说,假设上述正样本信号有1000个,负样本信号有1000个,也就是样本信号的数量是2000个。经过步骤S202处理后,每一个样本信号,对应生成10个样本子信号,就是有20000个样本子信号,现一轮训练,就是利用分类决策函数完成对这20000个样本子信号的分类。Specifically, for one round of training of the SVM classifier, the SVM classifier needs to be used to classify all sample sub-signals corresponding to the preset number of sample signals. For example, suppose there are 1000 positive sample signals and 1000 negative sample signals above, that is, the number of sample signals is 2000. After processing in step S202, each sample signal generates 10 sample sub-signals, that is, there are 20,000 sample sub-signals. The current round of training is to use the classification decision function to complete the classification of these 20,000 sample sub-signals.
在本发明实施例中,当首次训练时,上述SVM分类器的当前分类器参数是预先设置的,分类决策函数中的参数是根据当前分类器参数确定的。In the embodiment of the present invention, when training for the first time, the current classifier parameters of the SVM classifier are preset, and the parameters in the classification decision function are determined according to the current classifier parameters.
步骤S205,针对每一轮训练,基于预设数量的样本信号,按照预设损失函数,确定SVM分类器的损失值。Step S205, for each round of training, based on a preset number of sample signals, and according to a preset loss function, determine the loss value of the SVM classifier.
在本步骤中,在每一轮训练完成后,可以基于铰链损失函数,确定SVM分类器损失值,其中,铰链损失函数P(x)表示为:In this step, after each round of training is completed, the loss value of the SVM classifier can be determined based on the hinge loss function, where the hinge loss function P(x) is expressed as:
铰链损失函数P(x)的值表示损失值,λ表示待调参数,w表示SVM分类器中分割面的法向量,||·||表示取范数,N表示样本子信号的数量,wT表示w的转置,xi表示由第i个样本子信号的能量特征和累积量特征对应的样本支持向量,yi表示第i个样本子信号对应的标记值,b表示截距。The value of the hinge loss function P(x) represents the loss value, λ represents the parameter to be adjusted, w represents the normal vector of the segmentation surface in the SVM classifier, ||·|| represents the norm, N represents the number of sample sub-signals, w T represents the transpose of w, x i represents the sample support vector corresponding to the energy feature and cumulant feature of the i-th sample sub-signal, y i represents the label value corresponding to the i-th sample sub-signal, and b represents the intercept.
步骤S206,当基于损失值确定SVM分类器达到预设标准时,完成训练,确定SVM分类器对应的分类决策函数。Step S206, when it is determined based on the loss value that the SVM classifier meets the preset standard, the training is completed, and the classification decision function corresponding to the SVM classifier is determined.
在本步骤中,当损失值小于或等于预设阈值时,认为SVM分类器达到预设标准,可以结束训练,此时预设结构的SVM分类器的就是上述步骤S105中的SVM分类器,当前分类决策函数就是上述步骤S105中的分类决策函数。In this step, when the loss value is less than or equal to the preset threshold, it is considered that the SVM classifier reaches the preset standard, and the training can be ended. At this time, the SVM classifier with the preset structure is the SVM classifier in the above step S105. Currently The classification decision function is the classification decision function in the above step S105.
步骤S207,当基于损失值确定SVM分类器未达到预设标准时,按照预设调整方式,调整分类器参数,得到新的分类决策函数,并采用新的分类决策函数和预设数量的样本信号,完成新一轮训练。Step S207, when it is determined based on the loss value that the SVM classifier does not meet the preset standard, adjust the parameters of the classifier according to the preset adjustment method to obtain a new classification decision function, and use the new classification decision function and a preset number of sample signals, Complete a new round of training.
在本步骤中,当损失值大于预设阈值时,认为SVM分类器未达到预设标准,可以按照梯度下降法,通过计算上述铰链损失函数对λ的梯度,调整上述当前分类器参数,得到新的分类决策函数,并采用新的分类决策函数和预设数量的样本信号,完成新一轮训练。In this step, when the loss value is greater than the preset threshold, it is considered that the SVM classifier has not reached the preset standard, and the gradient of the above-mentioned hinge loss function to λ can be calculated according to the gradient descent method, and the above-mentioned current classifier parameters are adjusted to obtain a new The classification decision function, and use the new classification decision function and the preset number of sample signals to complete a new round of training.
具体的,上述SVM分类器的训练过程就是对样本信号进行分类,如图3所示,x1和x1表示信号的特征数据,例如,第一累积量特征和第二累积量特征,“+”表示正样本信号,“-”表示负样本信号,在高维空间中,利用超平面对正样本信号和负样本信号进行分类。Specifically, the training process of the above SVM classifier is to classify the sample signal, as shown in Figure 3 , x1 and x1 represent the feature data of the signal, for example, the first cumulant feature and the second cumulant feature, "+ "Represents a positive sample signal, "-" represents a negative sample signal. In a high-dimensional space, the positive sample signal and the negative sample signal are classified by using a hyperplane.
其中,超平面可以表示为:Among them, the hyperplane can be expressed as:
wTx+b=0w T x + b = 0
w表示超平面的法向量,wT表示w的转置,b表示截距。w represents the normal vector of the hyperplane, w T represents the transpose of w, and b represents the intercept.
那么,超平面的选取应该选取对正样本信号和负样本信号的中间位置,也就是距离超平面最远的正样本信号和负样本信号到超平面的相等,可以表示为:Then, the selection of the hyperplane should choose the middle position of the positive sample signal and the negative sample signal, that is, the positive sample signal and the negative sample signal farthest from the hyperplane are equal to the hyperplane, which can be expressed as:
s.t.yi(wTxi+b)≥1,i=1,2,...,Nsty i (w T x i +b)≥1, i=1, 2,..., N
为方便超平面问题的求解,可以将其转化为:In order to facilitate the solution of the hyperplane problem, it can be transformed into:
s.t.yi(wTxi+b)≥1,i=1,2,...,Nsty i (w T x i +b)≥1, i=1, 2,..., N
在SVM分类器对样本信号分类时,每一轮训练完成,需要根据上述待调参数的值,重新确定平面,因此,引入惩罚参数D和松弛变量ξ,其中,惩罚参数表示调节优化方向中函数间隔以及分类准确度的权重,松弛变量表示样本信号允许偏离超平面的边际的变化量。When the SVM classifier classifies the sample signal, after each round of training is completed, the plane needs to be re-determined according to the values of the above-mentioned parameters to be adjusted. Therefore, the penalty parameter D and the relaxation variable ξ are introduced, where the penalty parameter represents the function in the adjustment optimization direction The interval and the weight of the classification accuracy, the slack variable represents the amount of variation that the sample signal allows to deviate from the margin of the hyperplane.
其中,惩罚参数D和松弛变量ξ与上述铰链损失函数的关系可以表示为:Among them, the relationship between the penalty parameter D and the slack variable ξ and the above hinge loss function can be expressed as:
而超平面问题可以表示为:The hyperplane problem can be expressed as:
s.t.yi(wTxi+b)≥1-ξi,i=1,2,...,Nsty i (w T x i +b)≥1-ξ i , i=1, 2,..., N
ξi≥0,i=1,2,...,Nξ i ≥ 0, i = 1, 2, ..., N
s.t.表示约束条件,w表示超平面的法向量,||·||表示取范数,D表示惩罚参数,N表示样本子信号的数量,ξi表示第i个样本支持向量对应的松弛变量,yi(wTxi+b)表示第i个样本支持向量到分割面的间隔距离,yi表示第i个样本子信号对应的标记值,wT表示w的转置,xi表示第i个样本支持向量,b表示截距。st represents the constraint condition, w represents the normal vector of the hyperplane, ||·|| represents the norm, D represents the penalty parameter, N represents the number of sample sub-signals, ξ i represents the slack variable corresponding to the i-th sample support vector, y i (w T x i +b) represents the interval distance from the i-th sample support vector to the segmentation plane, y i represents the label value corresponding to the i-th sample sub-signal, w T represents the transpose of w, xi represents the i sample support vectors, b represents the intercept.
进一步的,可以构建拉格朗日函数L(w,b,ξ,α,μ),可以表示为:Further, the Lagrangian function L(w, b, ξ, α, μ) can be constructed, which can be expressed as:
其中,αi≥0,μi≥0,w表示超平面的法向量,||·||表示取范数,D表示惩罚参数,N表示样本子信号的数量,ξi表示第i个样本支持向量对应的松弛变量,αi和μi表示两个拉格朗日乘子的值,yi表示第i个样本子信号对应的标记值,wT表示w的转置,xi表示第i个样本子信号对应的样本支持向量,b表示截距,ξi表示第i个样本子信号对应的松弛变量。Among them, α i ≥ 0, μ i ≥ 0, w represents the normal vector of the hyperplane, ||·|| represents the norm, D represents the penalty parameter, N represents the number of sample sub-signals, ξ i represents the i-th sample The slack variable corresponding to the support vector, α i and μ i represent the values of two Lagrangian multipliers, y i represents the label value corresponding to the i-th sample sub-signal, w T represents the transpose of w, xi represents the The sample support vector corresponding to the i-th sample sub-signal, b represents the intercept, and ξ i represents the slack variable corresponding to the i-th sample sub-signal.
利用对偶方式,对超平面问题进行转换,具体可以表示为:Using the dual method to transform the hyperplane problem, it can be specifically expressed as:
0≤αi≤D,i=1,2,...,N0≤α i ≤D, i=1, 2,...,N
s.t.表示约束条件,αi表示第i个样本子信号对应的拉格朗日乘子的值,αj表示第j个样本子信号对应的拉格朗日乘子的值,yi表示第i个样本子信号对应的标记值,yj表示第i个样本子信号对应的标记值,第i个样本子信号对应的样本支持向量的转置,xj表示第j个样本子信号对应的样本支持向量,<·>表示內积运算,D表示惩罚参数,N表示样本子信号的数量。st represents the constraints, α i represents the value of the Lagrange multiplier corresponding to the i-th sample sub-signal, α j represents the value of the Lagrange multiplier corresponding to the j-th sample sub-signal, and y i represents the value of the i-th sample sub-signal The tag value corresponding to the sample sub-signal, y j represents the tag value corresponding to the i-th sample sub-signal, The transpose of the sample support vector corresponding to the i-th sample sub-signal, x j represents the sample support vector corresponding to the j-th sample sub-signal, <·> represents the inner product operation, D represents the penalty parameter, and N represents the number of sample sub-signals .
基于SMO算法,确定拉格朗日乘子的值。Based on the SMO algorithm, the value of the Lagrangian multiplier is determined.
利用样本支持向量的性质,确定偏置量的值,其中,偏置量的值b*表示为:Using the properties of the sample support vector, determine the value of the bias, where the value of the bias b * is expressed as:
N表示样本子信号的数量,yn表示第n个样本支持向量对应的标记值,yi表示第i个样本支持向量对应的标记值,表示第i个样本支持向量对应的拉格朗日乘子的对偶值,表示第i个样本支持向量的转置,xn表示第n个样本子信号对应的样本支持向量,<·>表示內积运算。N represents the number of sample sub-signals, y n represents the label value corresponding to the n-th sample support vector, y i represents the label value corresponding to the i-th sample support vector, Indicates the dual value of the Lagrangian multiplier corresponding to the i-th sample support vector, Indicates the transpose of the i-th sample support vector, x n indicates the sample support vector corresponding to the n-th sample sub-signal, <·> indicates the inner product operation.
基于拉格朗日乘子的值和偏置量的值,重新确定分类决策函数。Based on the value of the Lagrangian multiplier and the value of the bias, the classification decision function is redefined.
在本发明实施例中,上述样本支持向量的性质表示针对每个样本子信号,其样本支持向量的决策函数映射值与其标记值的积为1。In the embodiment of the present invention, the above property of the sample support vector means that for each sample sub-signal, the product of the decision function mapping value of the sample support vector and its label value is 1.
综上所述,由于样本信号只包括正样本信号和负样本信号,因此,利用样本信号对SVM分类器进行训练,可以更好的确定分类决策函数的准确性。所以采用本发明实施例中的SVM分类器可以更为准确的确定待探测信号中是否有无人机信号。To sum up, since the sample signal only includes the positive sample signal and the negative sample signal, the accuracy of the classification decision function can be better determined by using the sample signal to train the SVM classifier. Therefore, the use of the SVM classifier in the embodiment of the present invention can more accurately determine whether there is a UAV signal in the signal to be detected.
采用本发明实施例,可以实现对无线环境中的无人机信号的探测,如图4所示,在SVM分类器训练阶段,通过提取上述目标环境中的原始信号的能量特征与累积量特征,以及对无人机信号和原始信号提取能量特诊和累积量特征,利用提取得到的特征数据构造训练样本集,利用训练样本集对SVM分类器进行训练。而在识别过程中,根据待探测信号的能量特征和累积量特征,利用已经训练好的SVM分类器,可以确定待探测信号中是否存在无人机信号。By adopting the embodiment of the present invention, the detection of the UAV signal in the wireless environment can be realized. As shown in FIG. 4, in the SVM classifier training stage, by extracting the energy feature and cumulative feature of the original signal in the above-mentioned target environment, And extract the energy special diagnosis and cumulant features from the UAV signal and the original signal, use the extracted feature data to construct a training sample set, and use the training sample set to train the SVM classifier. In the identification process, according to the energy characteristics and cumulative characteristics of the signal to be detected, the trained SVM classifier can be used to determine whether there is a UAV signal in the signal to be detected.
基于同一种发明构思,根据上述本发明实施例提供的一种无人机信号探测方法,本发明实施例还提供了一种无人机信号探测的装置,如图5所示,可以包括:Based on the same inventive concept, according to a method for detecting a UAV signal provided by the above-mentioned embodiment of the present invention, an embodiment of the present invention also provides a device for detecting a UAV signal, as shown in FIG. 5 , which may include:
待探测信号获取模块501,用于获取待探测频段内的无线信号,作为待探测信号。The signal-to-be-detected acquisition module 501 is configured to acquire a wireless signal within a frequency band to be detected as a signal to be detected.
待观测信号获取模块502,用于按照预设频率间隔,划分待探测信号,得到多个频段内的无线子信号,作为待观测信号。The signal-to-be-observed acquisition module 502 is configured to divide the signal to be detected according to a preset frequency interval, and obtain wireless sub-signals in multiple frequency bands as the signal to be observed.
能量特征提取模块503,用于针对每个待观测信号,基于预设能量特征公式,提取该待观测信号的能量特征。The energy feature extraction module 503 is configured to, for each signal to be observed, extract the energy feature of the signal to be observed based on a preset energy feature formula.
累积量特征提取模块504,用于针对每个待观测信号,基于预设累积量特征公式,提取该待观测信号的累积量特征。The cumulant feature extraction module 504 is configured to, for each signal to be observed, extract the cumulant feature of the signal to be observed based on a preset cumulant feature formula.
无人机信号确定模块505,用于针对每个待观测信号,将该待观测信号的能量特征与累积量特征作为输入数据,输入预先训练的支持向量机SVM分类器,利用SVM分类器中的分类决策函数,确定待探测信号中是否包含有无人机信号。UAV signal determination module 505, for each signal to be observed, the energy feature and cumulative quantity feature of the signal to be observed are used as input data, input into the pre-trained support vector machine SVM classifier, using the The classification decision function determines whether the signal to be detected contains the UAV signal.
进一步的,能量特征提取模块503,具体用于针对每个待观测信号,基于帕塞瓦尔定理,计算该待观测信号对应的能量值,作为能量特征,其中,针对第i个频段对应的待观测信号yi(t),其能量特征Ei表示为:Further, the energy feature extraction module 503 is specifically configured to, for each signal to be observed, calculate the energy value corresponding to the signal to be observed based on Parseval's theorem as the energy feature, wherein, for the signal to be observed corresponding to the i-th frequency band Signal y i (t), its energy feature E i is expressed as:
Yi(f)表示该待观测信号yi(t)对应的频域信号,f1表示第i个频段对应的下边界频率,f2表示第i个频段对应的上边界频率,Δf表示在第i个频段中的频率采样间隔,t表示时间,f表示频率。Y i (f) represents the frequency domain signal corresponding to the signal to be observed y i (t), f 1 represents the lower boundary frequency corresponding to the i-th frequency band, f 2 represents the upper boundary frequency corresponding to the i-th frequency band, Δf represents the The frequency sampling interval in the i-th frequency band, t represents time, and f represents frequency.
进一步的,上述累积量特征提取模块504,包括:Further, the above-mentioned cumulant feature extraction module 504 includes:
累积量计算子模块,用于针对每个待观测信号,计算该待观测信号的二阶累积量、四阶累积量和六阶累积量,其中,针对每个待观测信号,二阶累积量C21可以表示为:The cumulant calculation sub-module is used for calculating the second-order cumulant, the fourth-order cumulant and the sixth-order cumulant of the signal to be observed for each signal to be observed, wherein, for each signal to be observed, the second-order cumulant C 21 can be expressed as:
C21=E(|y(k)2|)-|Ey(k)2|C 21 =E(|y(k) 2 |)-|Ey(k) 2 |
y(k)表示第k个待观测信号,E(·)表示数学期望,|·|表示取模符号,C21中的1表示引入一个共轭复数;y(k) represents the kth signal to be observed, E(·) represents the mathematical expectation, |·| represents the modulus symbol, and 1 in C 21 represents the introduction of a conjugate complex number;
针对每个待观测信号,四阶累计量C42可以表示为:For each signal to be observed, the fourth-order cumulant C 42 can be expressed as:
C42=E(|y(k)4|)-|Ey(k)2|2-2(E(|y(k)2|))2 C 42 =E(|y(k) 4 |)-|Ey(k) 2 | 2 -2(E(|y(k) 2 |)) 2
y(k)表示第k个待观测信号,E(·)表示数学期望,|·|表示取模符号,C42中的2表示引入两个共轭复数;y(k) represents the kth signal to be observed, E( ) represents mathematical expectation, |·| represents a modulus symbol, and 2 in C 42 represents the introduction of two conjugate complex numbers;
针对每个待观测信号,累积量C63可以表示为:For each signal to be observed, the cumulant C 63 can be expressed as:
C63=E(|y(k)6|)-9E(|y(k)|4)E(|y(k)|2)-3E(y*(k)3y(k))E(y(k)2)-3E(y*(k)y(k)3)E(y*(k)2)-18E(y*(k)2)E(y(k)2)E(|y(k)|2)-12(E(|y(k)|2))3 C 63 =E(|y(k) 6 |)-9E(|y(k)| 4 )E(|y(k)| 2 )-3E(y * (k) 3 y(k))E( y(k) 2 )-3E(y * (k)y(k) 3 )E(y * (k) 2 )-18E(y * (k) 2 )E(y(k) 2 )E(| y(k)| 2 )-12(E(|y(k)| 2 )) 3
y(k)表示第k个待观测信号,E(·)表示数学期望,|·|表示取模符号,*表示共轭符号,C63中的3表示引入三个共轭复数。y(k) represents the kth signal to be observed, E(·) represents the mathematical expectation, |·| represents the modulus symbol, * represents the conjugate symbol, and the 3 in C 63 represents the introduction of three conjugate complex numbers.
累积量特征计算子模块,用于针对每个待观测信号,根据该待观测信号的二阶累积量、四阶累积量和六阶累积量,按照预设累积量特征公式,确定该待观测信号的第一累计量特征和第二累积量特征,作为累积量特征,其中,针对每个待观测信号,累积量特征表示为:The cumulant feature calculation sub-module is used to determine the signal to be observed according to the second-order cumulant, fourth-order cumulant, and sixth-order cumulant of the signal to be observed according to the preset cumulant characteristic formula for each signal to be observed The first cumulant feature and the second cumulant feature of are used as the cumulant feature, where, for each signal to be observed, the cumulant feature is expressed as:
γ1表示第一累积量特征,γ2表示第二累积量特征,y(k)表示第k个待观测信号,C21表示二阶累积量,C42表示四阶累积量,C63表示六阶累积量,C21中的1表示引入一个共轭复数,C42中的2表示引入两个共轭复数,C63中的3表示引入三个共轭复数。γ 1 represents the first cumulant feature, γ 2 represents the second cumulant feature, y(k) represents the kth signal to be observed, C 21 represents the second-order cumulant, C 42 represents the fourth-order cumulant, C 63 represents the sixth-order cumulant The 1 in C 21 means to introduce a conjugate complex number, the 2 in C 42 means to introduce two conjugate complex numbers, and the 3 in C 63 means to introduce three conjugate complex numbers.
进一步的,上述无人机信号确定模块505,包括:Further, the above-mentioned UAV signal determination module 505 includes:
支持向量生成子模块,用于针对每个待观测信号,将该待观测信号的能量特征和累积量特征作为向量元素,生成一个对应的支持向量,其中,第n个待观测信号对应的支持向量表示为:The support vector generating submodule is used for each signal to be observed, using the energy feature and cumulant feature of the signal to be observed as vector elements to generate a corresponding support vector, wherein the support vector corresponding to the nth signal to be observed Expressed as:
En表示该待观测信号的能量特征,γn1表示该待观测信号的第一累积量特征,γn2表示该观测信号的第二累积量特征。E n represents the energy feature of the signal to be observed, γ n1 represents the first cumulant feature of the signal to be observed, and γ n2 represents the second cumulant feature of the observed signal.
支持向量输入子模块,用于将支持向量作为输入数据,输入预先训练的SVM分类器中。The support vector input submodule is used to input the support vector as input data into the pre-trained SVM classifier.
决策值确定子模块,用于根据SVM分类器中的分类决策函数,确定待观测信号对应的值,作为决策值,其中,分类决策函数f(mn)表示为:The decision value determination sub-module is used to determine the value corresponding to the signal to be observed according to the classification decision function in the SVM classifier as the decision value, wherein the classification decision function f(m n ) is expressed as:
分类决策函数f(mn)的值表示第n个待观测信号对应的决策值,sign(·)表示符号函数,N表示样本子信号的数量,表示第i个样本支持向量对应的拉格朗日乘子的对偶值,<·>表示內积运算,b*表示偏置量的值,mn T表示第n个待观测信号的能量特征和累积量特征对应的支持向量的转置,xi表示第i个样本子信号的能量特征和累积量特征对应的样本支持向量,yi表示第i个样本子信号对应的标记值。The value of the classification decision function f(m n ) represents the decision value corresponding to the nth signal to be observed, sign( ) represents the sign function, N represents the number of sample sub-signals, Indicates the dual value of the Lagrangian multiplier corresponding to the support vector of the i-th sample, <·> indicates the inner product operation, b * indicates the value of the bias value, m n T indicates the energy characteristic sum of the n-th signal to be observed The transpose of the support vector corresponding to the cumulant feature, x i represents the energy feature of the i-th sample sub-signal and the sample support vector corresponding to the cumulant feature, and y i represents the label value corresponding to the i-th sample sub-signal.
无人机信号确定子模块,用于基于每个待观测信号的决策值,确定待探测信号中是否包含有无人机信号,当决策值中至少有一个值为1时,则表示该待探测信号中包含无人机信号,当决策值全部为-1时,则表示该待探测信号中没有包含无人机信号。The UAV signal determination sub-module is used to determine whether the UAV signal is included in the signal to be detected based on the decision value of each signal to be observed. When at least one of the decision values has a value of 1, it means that the signal to be detected is The signal contains UAV signals, and when the decision values are all -1, it means that the signal to be detected does not contain UAV signals.
进一步的,上述本发明实施例提供的一种无人机信号探测装置,如图6所示,还可以包括:Further, the UAV signal detection device provided by the above-mentioned embodiment of the present invention, as shown in FIG. 6 , may also include:
样本信号获取模块601,用于按照预设条件,在待探测频段中获取预设数量的无线信号,作为样本信号。The sample signal acquisition module 601 is configured to acquire a preset number of wireless signals in the frequency band to be detected as sample signals according to preset conditions.
样本子信号获取模块602,用于按照预设频率间隔,划分样本信号,得到多个频段内的无线子信号,作为样本子信号。The sample sub-signal acquisition module 602 is configured to divide the sample signal according to preset frequency intervals to obtain wireless sub-signals in multiple frequency bands as sample sub-signals.
特征数据获取模块603,用于针对每个样本子信号,提取该样本子信号的能量特征和累积量特征。The feature data acquisition module 603 is configured to, for each sample sub-signal, extract the energy feature and cumulant feature of the sample sub-signal.
样本信号分类模块604,用于基于预设数量的样本信号,将由样本信号中样本子信号的能量特征和累积量特征构成的样本支持向量,以及样本子信号对应的标记值作为输入数据,输入采用当前分类器参数以及预设结构的SVM分类器,利用当前分类决策函数,对预设数量的样本信号进行分类,完成一轮训练,其中,在首次训练时,当前分类器参数为预设初始分类器参数,当前分类决策函数是根据预设初始分类器参数确定的。The sample signal classification module 604 is configured to use a sample support vector composed of the energy feature and cumulant feature of the sample sub-signal in the sample signal and the corresponding label value of the sample sub-signal as input data based on a preset number of sample signals. The current classifier parameters and the SVM classifier with the preset structure use the current classification decision function to classify the preset number of sample signals and complete a round of training. In the first training, the current classifier parameters are the preset initial classification The current classification decision function is determined according to the preset initial classifier parameters.
损失值确定模块605,用于针对每一轮训练,基于预设数量的样本信号,按照预设损失函数,确定SVM分类器的损失值。The loss value determination module 605 is configured to determine the loss value of the SVM classifier according to a preset loss function based on a preset number of sample signals for each round of training.
分类器生成模块606,用于当基于损失值确定SVM分类器达到预设标准时,完成训练,确定SVM分类器对应的分类决策函数。The classifier generating module 606 is configured to complete the training and determine the classification decision function corresponding to the SVM classifier when it is determined based on the loss value that the SVM classifier meets the preset standard.
分类器参数调整模块607,用于当基于损失值确定SVM分类器未达到预设标准时,按照预设调整方式,调整分类器参数,得到新的分类决策函数,并采用新的分类决策函数和预设数量的样本信号,完成新一轮训练。The classifier parameter adjustment module 607 is used to adjust the classifier parameters according to the preset adjustment method when it is determined based on the loss value that the SVM classifier does not meet the preset standard, to obtain a new classification decision function, and to adopt the new classification decision function and the preset Set the number of sample signals to complete a new round of training.
进一步的,损失值确定模块605,具体用于在每一轮训练完成之后,基于铰链损失函数,确定SVM分类器损失值,其中,铰链损失函数P(x)表示为:Further, the loss value determination module 605 is specifically configured to determine the loss value of the SVM classifier based on the hinge loss function after each round of training is completed, wherein the hinge loss function P(x) is expressed as:
铰链损失函数P(x)的值表示损失值,λ表示待调参数,w表示SVM分类器中分割面的法向量,||·||表示取范数,N表示样本子信号的数量,wT表示w的转置,xi表示由第i个样本子信号的能量特征和累积量特征对应的样本支持向量,yi表示第i个样本子信号对应的标记值,b表示截距。The value of the hinge loss function P(x) represents the loss value, λ represents the parameter to be adjusted, w represents the normal vector of the segmentation surface in the SVM classifier, ||·|| represents the norm, N represents the number of sample sub-signals, w T represents the transpose of w, x i represents the sample support vector corresponding to the energy feature and cumulant feature of the i-th sample sub-signal, y i represents the label value corresponding to the i-th sample sub-signal, and b represents the intercept.
进一步的,上述分类器参数调整模块607,包括:Further, the above-mentioned classifier parameter adjustment module 607 includes:
待调参数确定子模块,用于当基于损失值确定SVM分类器未达到预设标准时,基于铰链损失函数,利用梯度下降法,确定铰链损失函数中的待调参数的值。The sub-module for determining parameters to be adjusted is used to determine the value of the parameters to be adjusted in the hinge loss function based on the hinge loss function and using the gradient descent method when it is determined that the SVM classifier does not meet the preset standard based on the loss value.
分类决策函数确定子模块,用于根据待调参数的值,重新确定SVM分类器对应的分类决策函数。The classification decision function determination sub-module is used to re-determine the classification decision function corresponding to the SVM classifier according to the value of the parameter to be adjusted.
进一步的,上述分类决策函数确定子模块,还包括:Further, the above-mentioned classification decision function determination submodule also includes:
惩罚参数确定子模块,用于根据待调参数的值,确定SVM分类器的惩罚参数,其中,惩罚参数表示调节优化方向中函数间隔以及分类准确度的权重,惩罚参数D与待调参数λ之间的关系表示为:The penalty parameter determination submodule is used to determine the penalty parameter of the SVM classifier according to the value of the parameter to be adjusted, wherein the penalty parameter represents the weight of adjusting the function interval and classification accuracy in the optimization direction, and the difference between the penalty parameter D and the parameter λ to be adjusted The relationship between is expressed as:
超平面确定子模块,用于基于惩罚参数,利用凸优化问题,确定SVM分类器对应分割面的划分方式,其中,分割面的划分方式表示利用超平面对预设数量的样本信号进行分类的方法,具体表示为:The hyperplane determination submodule is used to determine the division method of the SVM classifier corresponding to the segmentation surface based on the penalty parameter and using the convex optimization problem, wherein the division method of the segmentation surface represents the method of classifying a preset number of sample signals using the hyperplane , specifically expressed as:
s.t.yi(wTxi+b)≥1-ξi,i=1,2,...,Nsty i (w T x i +b)≥1-ξ i , i=1, 2,..., N
ξi≥0,i=1,2,...,Nξ i ≥ 0, i = 1, 2, ..., N
s.t.表示约束条件,w表示超平面的法向量,||·||表示取范数,D表示惩罚参数,N表示样本子信号的数量,ξi表示第i个样本子信号对应的松弛变量,yi(wTxi+b)表示第i个样本支持向量到分割面的间隔距离,yi表示第i个样本子信号对应的标记值,wT表示w的转置,xi表示第i个样本支持向量,b表示截距。st represents the constraint condition, w represents the normal vector of the hyperplane, ||·|| represents the norm, D represents the penalty parameter, N represents the number of sample sub-signals, ξi represents the slack variable corresponding to the i-th sample sub-signal, y i (w T x i +b) represents the interval distance from the i-th sample support vector to the segmentation plane, y i represents the label value corresponding to the i-th sample sub-signal, w T represents the transpose of w, and xi represents the i-th sample support vectors, and b represents the intercept.
问题转换子模块,用于基于拉格朗日函数,以及函数对偶化方法,转换划分方式的求解问题,转换后划分方式表示为:The problem conversion sub-module is used to solve the problem based on the Lagrangian function and the function dualization method, and converts the partition method. The converted partition method is expressed as:
0≤αi≤D,i=1,2,...,N0≤α i ≤D, i=1, 2,...,N
s.t.表示约束条件,αi表示第i个样本子信号对应的拉格朗日乘子的值,αj表示第j个样本子信号对应的拉格朗日乘子的值,yi表示第i个样本子信号对应的标记值,yj表示第i个样本子信号对应的标记值,第i个样本子信号对应的样本支持向量的转置,xj表示第j个样本子信号对应的样本支持向量,<·>表示內积运算,D表示惩罚参数,N表示样本子信号的数量;st represents the constraints, α i represents the value of the Lagrange multiplier corresponding to the i-th sample sub-signal, α j represents the value of the Lagrange multiplier corresponding to the j-th sample sub-signal, and y i represents the value of the i-th sample sub-signal The tag value corresponding to the sample sub-signal, y j represents the tag value corresponding to the i-th sample sub-signal, The transpose of the sample support vector corresponding to the i-th sample sub-signal, x j represents the sample support vector corresponding to the j-th sample sub-signal, <·> represents the inner product operation, D represents the penalty parameter, and N represents the number of sample sub-signals ;
拉格朗日乘子确定子模块,用于基于SMO算法,确定拉格朗日乘子的值。The Lagrangian multiplier determining submodule is used to determine the value of the Lagrangian multiplier based on the SMO algorithm.
偏置量确定子模块,用于利用样本支持向量的性质,确定偏置量的值,其中,偏置量的值b*表示为:The offset determination sub-module is used to determine the value of the offset by using the properties of the sample support vector, wherein the value b * of the offset is expressed as:
N表示样本子信号的数量,yn表示第n个样本支持向量对应的标记值,yi表示第i个样本支持向量对应的标记值,表示第i个样本支持向量对应的拉格朗日乘子的对偶值,表示第i个样本支持向量的转置,xn表示第n个样本子信号对应的样本支持向量,<·>表示內积运算;N represents the number of sample sub-signals, y n represents the label value corresponding to the n-th sample support vector, y i represents the label value corresponding to the i-th sample support vector, Indicates the dual value of the Lagrangian multiplier corresponding to the i-th sample support vector, Indicates the transpose of the i-th sample support vector, x n indicates the sample support vector corresponding to the n-th sample sub-signal, <·> indicates the inner product operation;
基于拉格朗日乘子的值和偏置量的值,重新确定分类决策函数。Based on the value of the Lagrangian multiplier and the value of the bias, the classification decision function is redefined.
需要说明的是,在本文中,诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个......”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is a relationship between these entities or operations. any such actual relationship or order exists between them. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.
本说明书中的各个实施例均采用相关的方式描述,各个实施例之间相同相似的部分互相参见即可,每个实施例重点说明的都是与其他实施例的不同之处。尤其,对于装置实施例而言,由于其基本相似于方法实施例,所以描述的比较简单,相关之处参见方法实施例的部分说明即可。Each embodiment in this specification is described in a related manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for relevant parts, please refer to part of the description of the method embodiment.
以上所述仅为本发明的较佳实施例而已,并非用于限定本发明的保护范围。凡在本发明的精神和原则之内所作的任何修改、等同替换、改进等,均包含在本发明的保护范围内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention are included in the protection scope of the present invention.
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