基于时频分析的跳频通信侦察技术研究
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摘要
跳频技术是通信抗干扰技术中最有成效的技术之一,加之其优越的抗截获、抗衰落和较强的多址组网能力,已经在军事和民用通信中得到了广泛的应用。由此所引发的,对跳频通信的侦察和监测,也成为通信对抗和无线电监管领域近年来的研究重点。这一研究在技术上极富挑战性,这主要是由于：①跳频信号的载频按照伪随机方式发生跳变,使得跳频信号的检测和参数估计比一般的定频信号要困难得多；②随着人类信息化程度不断提高,电磁环境日益复杂。密集的定频信号、噪声信号、外界干扰信号以及各种突发信号相互交织在一起,使得跳频信号的检测变得十分困难；③在复杂的电磁环境下,同时工作的多个跳频网台,既有同步网,又有异步网；既有正交网,又有非正交网。它们的跳变速度相近或相同,工作的频率范围又可能有重合,就更增加了网台分选的难度。
由于跳频信号的频率随时间不断变化,属于典型的非平稳信号,单纯的时域或频域分析方法很难对其精确分析,而时频分析技术的出现则为处理这类非平稳信号提供了非常有效的方法。时频分析的基本思想是设计时间和频率的联合函数,用它同时描述信号在不同时间和频率的能量密度或强度。近年来,与此相关的研究工作不断发展与深入,其应用范围也不断扩大,成为当今信号处理领域的一个非常活跃而重要的研究课题。
本文根据跳频信号的时变特征,深入研究了适用于跳频信号的时频分析方法,并以时频分析为主线,系统地解决了复杂环境中跳频信号的网台盲分选、信号盲识别和参数盲估计问题。主要研究工作和成果包含以下几个方面：
●本文对基于核函数滤波的时频分析方法进行了深入的研究。对于跳频信号,Wigner-Ville分布存在严重的交叉项干扰,现有的Cohen类时频分布在交叉项抑制和时频聚集性方面存在一定的局限性。为了解决这些问题,本文提出了一种新的Cohen类时频分布核函数,相对于其它的核函数,新构造的核函数能够更好地匹配跳频信号的模糊函数,在参数选取适当的条件下,它能够彻底抑制跳频信号的交叉项干扰,并保持较高的时频聚集性。为了使核函数的参数达到最佳取值,提出了一种基于3阶Renyi熵的核参数优化方法,根据3阶Renyi熵对核参数进行优化选择,可以使核函数与信号达到最佳匹配,从而获得不含交叉项干扰,并具有较高时频聚集性的时频分布。通过对这种新的时频分布的噪声分析表明,它具有较好的抗噪声性能,新的核函数不仅抑制了多分量信号的交叉项,同时也起到了平滑白噪声的作用。
●除了通过核函数来抑制交叉项,根据跳频信号的频谱特点,本文提出了一种基于信号分解的组合时频分布,通过高斯滤波器组将多分量跳频信号分解成为多个单分量信号,再将每个信号分量的Wigner-Ville分布线性叠加来逼近跳频信号的能量分布。这种新的时频分布能够有效避免多个分量相互作用带来的交叉项干扰,而且保留了Wigner-Ville分布良好的时频分辨率,从而更加准确地刻画了跳频信号的时变频率特性。与同类方法相比,它的运算复杂度更低,因而具有更大的实用价值。通过对这种新的时频分布的噪声分析表明,它的抗噪声性能虽然没有达到最优,但是通过阈值滤波的方法对时频分布进行降噪处理,可以减小原时频分布中扰动信号分量的噪声能量,从而得到降噪后的时频分布。
●为了分离不同的跳频网台,本文提出了一种基于时频分布的跳频网台盲分选方法。这种方法利用不同源信号时频特征的差异,通过对混合信号的时频分布矩阵集合联合近似对角化来实现信号的盲分离。只要源信号之间互不相关且具有不同的时频特征,不论它们是跳频信号还是其它信号,非正交网台还是正交网台,恒跳速网台还是变跳速网台,都可以得到有效分离。由于时频分布本身具有良好的能量集中度和噪声扩散特性,基于时频分布的跳频网台盲分选方法的抗噪声性能优于其它的跳频网台盲分选方法。
●现有的跳频信号盲检测方法大多仅研究如何从噪声中检测跳频信号,对电磁环境的假设过于理想,而实际的电磁环境异常复杂。对于盲源分离后获得的单个信号,本文通过比较不同信号瞬时频率特征的差异,提出了一种基于瞬时频率的跳频信号盲识别方法。这种方法抓住了跳频信号区别于噪声和其它信号的独特特征,在复杂的电磁环境中,具有高的识别概率和低的虚警概率,因而具有很强的鲁棒性。
●为了在低信噪比下达到更好的参数估计性能,本文提出了一种基于瞬时频率的跳频参数盲估计方法,根据跳频信号频率的瞬变特性,通过对瞬时频率的小波变换,可以准确估计跳周期,进而估计其它跳频参数。该方法可以在低信噪比下实现高精度的参数估计,估计方差接近Cramer-Rao界,整体性能优于目前的主流方法。
本文的研究为跳频信号的侦察提供了新思路和新方法,这一研究对于跳频通信对抗的发展以及时频分析技术的拓宽应用无疑具有重大的理论意义和应用前景。
Frequency-hopping (FH) signals are widely used in both military and commercial communication for their good properties in term of:anti-jamming, low probability of interception, good capability against fading, code division multiple access. The reconnaissance and monitoring of FH signals is thereby the research emphasis of communication countermeasures and radio management.
FH signals are typical non-stationary signals for their frequency changing with time. A single time domain analysis or frequency domain analysis is helpless to them. The rise of time-frequency analysis provides an effective solution for this kind of signals. The idea of time-frequency analysis is to design a joint function of time and frequency, and use it to describe the power density on each time and frequency simultaneously. These years, the researches on time-frequency analysis are hot in signal processing.
In this dissertation, firstly, according to the time variant property of FH signals, a deep investigation is made on time-frequency distribution (TFD) suitable to FH signals in complicated electromagnetic environment. Then based on these TFD, a series of studies on blind separation, blind detection and blind parameter estimation of FH signals is made. Now list the main achievements as follows:
●The limitation of WVD and the existing time-frequency distributions of Cohen' class to FH signals is firstly be analyzed. To reduce the cross-terms in WVD, a new kernel function is put forward. Compared with other kernel functions, the new kernel function is rather matched to the ambiguity function of a FH signal. On the condition that kernel parameters are selected properly, the new kernel function can suppress the cross-terms of FH signals, and preserves a higher time-frequency resolution simultaneously. To optimize the kernel parameters, a method based on the second order Renyi entropy normalized by its volume is proposed. With this method, the kernel can obtain optimal match to the ambiguity function of a FH signal, and a TFD with high time-frequency concentration and without cross-terms is available.
●To reduce the cross-terms in WVD, according to the spectrum property of FH signals, a new time-frequency analysis method based on frequency decomposition is proposed. In this method, a multi-component FH signal is firstly decomposed into multiple single-component signals with a Gauss filter bank. Then the WVD of each component is sum up to form a new TFD. The theoretical analysis and simulation result shows that the new TFD can reduce the cross-term interference of FH signals, and preserves a higher time-frequency resolution simultaneously. So the new TFD can describe the time variant property of FH signals more accurately.
●To resist the noise in TFDs, the TFDs of noisy signals are deduced. A threshold filtering method is proposed to reduce the noise in TFDs. This method can reduce the power of noise and improve the readability of TFDs.
●To separate different FH signals, a new blind source separation (BSS) method based on TFD is proposed. This method exploits the difference in the time-frequency properties of the source signals to solve the blind source separation problem based on the joint diagonalization of a combined set of time-frequency distribution matrices. On the condition of source signals are non-correlative and have different time-frequency properties, the new method is effective on blind separation of FH signals. Due to the effect of spreading the noise power while localizing the source energy in the t-f domain, compared with those methods based on signal independence, the proposed BSS method has an increasing robustness with respect to noise.
●For the single signal achieved after blind source separation, via a comparison on instantaneous frequency (IF) of different signals, a blind detection method based on IF is proposed. Since the method exploits the particular property of FH signals different from other signals, it is robust for its high detection probability and low false alarm probability in complicated electromagnetic environment.
●A blind parameters estimation method for FH signals based on IF is proposed. According to the transient frequencies of FH signals, via the wavelet transform of IF, the hop period can be estimated accurately, and the switching time instants and the hopping frequencies can be estimated thereby. The method can accurately estimate the parameters of FH signals under a low SNR. It has a superior performance compared with the accepted method these days.
The studies in this dissertation provide some new ideas and approaches for the reconnaissance of FH signals. Undoubtedly, it has important theoretical significance and practical value to the development of FH communication countermeasures and expanding the application of the time-frequency analysis.
由于跳频信号的频率随时间不断变化,属于典型的非平稳信号,单纯的时域或频域分析方法很难对其精确分析,而时频分析技术的出现则为处理这类非平稳信号提供了非常有效的方法。时频分析的基本思想是设计时间和频率的联合函数,用它同时描述信号在不同时间和频率的能量密度或强度。近年来,与此相关的研究工作不断发展与深入,其应用范围也不断扩大,成为当今信号处理领域的一个非常活跃而重要的研究课题。
本文根据跳频信号的时变特征,深入研究了适用于跳频信号的时频分析方法,并以时频分析为主线,系统地解决了复杂环境中跳频信号的网台盲分选、信号盲识别和参数盲估计问题。主要研究工作和成果包含以下几个方面：
●本文对基于核函数滤波的时频分析方法进行了深入的研究。对于跳频信号,Wigner-Ville分布存在严重的交叉项干扰,现有的Cohen类时频分布在交叉项抑制和时频聚集性方面存在一定的局限性。为了解决这些问题,本文提出了一种新的Cohen类时频分布核函数,相对于其它的核函数,新构造的核函数能够更好地匹配跳频信号的模糊函数,在参数选取适当的条件下,它能够彻底抑制跳频信号的交叉项干扰,并保持较高的时频聚集性。为了使核函数的参数达到最佳取值,提出了一种基于3阶Renyi熵的核参数优化方法,根据3阶Renyi熵对核参数进行优化选择,可以使核函数与信号达到最佳匹配,从而获得不含交叉项干扰,并具有较高时频聚集性的时频分布。通过对这种新的时频分布的噪声分析表明,它具有较好的抗噪声性能,新的核函数不仅抑制了多分量信号的交叉项,同时也起到了平滑白噪声的作用。
●除了通过核函数来抑制交叉项,根据跳频信号的频谱特点,本文提出了一种基于信号分解的组合时频分布,通过高斯滤波器组将多分量跳频信号分解成为多个单分量信号,再将每个信号分量的Wigner-Ville分布线性叠加来逼近跳频信号的能量分布。这种新的时频分布能够有效避免多个分量相互作用带来的交叉项干扰,而且保留了Wigner-Ville分布良好的时频分辨率,从而更加准确地刻画了跳频信号的时变频率特性。与同类方法相比,它的运算复杂度更低,因而具有更大的实用价值。通过对这种新的时频分布的噪声分析表明,它的抗噪声性能虽然没有达到最优,但是通过阈值滤波的方法对时频分布进行降噪处理,可以减小原时频分布中扰动信号分量的噪声能量,从而得到降噪后的时频分布。
●为了分离不同的跳频网台,本文提出了一种基于时频分布的跳频网台盲分选方法。这种方法利用不同源信号时频特征的差异,通过对混合信号的时频分布矩阵集合联合近似对角化来实现信号的盲分离。只要源信号之间互不相关且具有不同的时频特征,不论它们是跳频信号还是其它信号,非正交网台还是正交网台,恒跳速网台还是变跳速网台,都可以得到有效分离。由于时频分布本身具有良好的能量集中度和噪声扩散特性,基于时频分布的跳频网台盲分选方法的抗噪声性能优于其它的跳频网台盲分选方法。
●现有的跳频信号盲检测方法大多仅研究如何从噪声中检测跳频信号,对电磁环境的假设过于理想,而实际的电磁环境异常复杂。对于盲源分离后获得的单个信号,本文通过比较不同信号瞬时频率特征的差异,提出了一种基于瞬时频率的跳频信号盲识别方法。这种方法抓住了跳频信号区别于噪声和其它信号的独特特征,在复杂的电磁环境中,具有高的识别概率和低的虚警概率,因而具有很强的鲁棒性。
●为了在低信噪比下达到更好的参数估计性能,本文提出了一种基于瞬时频率的跳频参数盲估计方法,根据跳频信号频率的瞬变特性,通过对瞬时频率的小波变换,可以准确估计跳周期,进而估计其它跳频参数。该方法可以在低信噪比下实现高精度的参数估计,估计方差接近Cramer-Rao界,整体性能优于目前的主流方法。
本文的研究为跳频信号的侦察提供了新思路和新方法,这一研究对于跳频通信对抗的发展以及时频分析技术的拓宽应用无疑具有重大的理论意义和应用前景。
Frequency-hopping (FH) signals are widely used in both military and commercial communication for their good properties in term of:anti-jamming, low probability of interception, good capability against fading, code division multiple access. The reconnaissance and monitoring of FH signals is thereby the research emphasis of communication countermeasures and radio management.
FH signals are typical non-stationary signals for their frequency changing with time. A single time domain analysis or frequency domain analysis is helpless to them. The rise of time-frequency analysis provides an effective solution for this kind of signals. The idea of time-frequency analysis is to design a joint function of time and frequency, and use it to describe the power density on each time and frequency simultaneously. These years, the researches on time-frequency analysis are hot in signal processing.
In this dissertation, firstly, according to the time variant property of FH signals, a deep investigation is made on time-frequency distribution (TFD) suitable to FH signals in complicated electromagnetic environment. Then based on these TFD, a series of studies on blind separation, blind detection and blind parameter estimation of FH signals is made. Now list the main achievements as follows:
●The limitation of WVD and the existing time-frequency distributions of Cohen' class to FH signals is firstly be analyzed. To reduce the cross-terms in WVD, a new kernel function is put forward. Compared with other kernel functions, the new kernel function is rather matched to the ambiguity function of a FH signal. On the condition that kernel parameters are selected properly, the new kernel function can suppress the cross-terms of FH signals, and preserves a higher time-frequency resolution simultaneously. To optimize the kernel parameters, a method based on the second order Renyi entropy normalized by its volume is proposed. With this method, the kernel can obtain optimal match to the ambiguity function of a FH signal, and a TFD with high time-frequency concentration and without cross-terms is available.
●To reduce the cross-terms in WVD, according to the spectrum property of FH signals, a new time-frequency analysis method based on frequency decomposition is proposed. In this method, a multi-component FH signal is firstly decomposed into multiple single-component signals with a Gauss filter bank. Then the WVD of each component is sum up to form a new TFD. The theoretical analysis and simulation result shows that the new TFD can reduce the cross-term interference of FH signals, and preserves a higher time-frequency resolution simultaneously. So the new TFD can describe the time variant property of FH signals more accurately.
●To resist the noise in TFDs, the TFDs of noisy signals are deduced. A threshold filtering method is proposed to reduce the noise in TFDs. This method can reduce the power of noise and improve the readability of TFDs.
●To separate different FH signals, a new blind source separation (BSS) method based on TFD is proposed. This method exploits the difference in the time-frequency properties of the source signals to solve the blind source separation problem based on the joint diagonalization of a combined set of time-frequency distribution matrices. On the condition of source signals are non-correlative and have different time-frequency properties, the new method is effective on blind separation of FH signals. Due to the effect of spreading the noise power while localizing the source energy in the t-f domain, compared with those methods based on signal independence, the proposed BSS method has an increasing robustness with respect to noise.
●For the single signal achieved after blind source separation, via a comparison on instantaneous frequency (IF) of different signals, a blind detection method based on IF is proposed. Since the method exploits the particular property of FH signals different from other signals, it is robust for its high detection probability and low false alarm probability in complicated electromagnetic environment.
●A blind parameters estimation method for FH signals based on IF is proposed. According to the transient frequencies of FH signals, via the wavelet transform of IF, the hop period can be estimated accurately, and the switching time instants and the hopping frequencies can be estimated thereby. The method can accurately estimate the parameters of FH signals under a low SNR. It has a superior performance compared with the accepted method these days.
The studies in this dissertation provide some new ideas and approaches for the reconnaissance of FH signals. Undoubtedly, it has important theoretical significance and practical value to the development of FH communication countermeasures and expanding the application of the time-frequency analysis.
引文
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