超低频信道噪声统计特性及应用
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摘要
超低频(ELF)通信是指用频率为30～300Hz的电磁波作信号载体的通信。由于超低频电磁波在海水中具有传播衰减率较低的特性,因此超低频通信可以实现对全球范围、水下100米深潜艇的岸潜指挥通信,在军事领域具有重要的应用价值。
受天线及发射机工程实现的制约,超低频通信接收机输入端信号电平很小,噪声是影响超低频通信的主要因素。超低频信道的噪声主要是大气噪声,其统计特性严重非高斯。众所周知,传统的对高斯噪声最优的接收机,在严重非高斯噪声的条件下性能将恶化,甚至不能正常工作。另一方面,如果能够辨识信道噪声的统计特性并加以有效利用,则可以大幅度地提高接收机的性能。
本文围绕超低频信道噪声统计特性的辨识和工程应用问题,取得如下成果:
1)为了对GSM-06测得的超低频大气噪声进行幅度统计分析,采用频域法对超低频信道电磁噪声数据进行预处理,抑制50Hz工频干扰。接着对处理过的超低频信道大气噪声数据进行Lilliefors假设检验,证明超低频信道大气噪声的非正态属性。最后在估计Class A瞬时幅度概率分布参数值的基础上,根据概率分布图和Q-Q图结果综合分析做出结论:采用Class A瞬时幅度概率模型,可以很好地描述宽带超低频信道大气噪声数据的幅度统计特性,而对于窄带超低频噪声数据适用于Class B模型描述。
2)对Turbo码的译码算法在非高斯脉冲型噪声环境下的性能进行研究,提出一种新的基于噪声幅度统计特性服从Class A噪声模型的修正译码算法。该算法修正传统MAP算法的外部信息和信道信息计算公式,使之适应非高斯的Class A脉冲噪声环境从而改进了译码性能,计算机仿真证明新的译码算法在Class A噪声环境中比传统MAP算法具有更好的性能。
3)针对经典Class A噪声模型的参数估计问题。提出二种参数估计算法,一种基于特征函数谱,而另一种基于参数的贝叶斯估计。贝叶斯估计器采用马氏链蒙特卡罗法(MCMC)求解,适用于较小的样本数,具有低复杂度和快速收敛的优点。基于特征函数谱参数估计算法适用于较大的样本数,对应于信道噪声统计特性较为平稳的情况,它的优点是运算量小,参数估计速度快。研究简化Class B噪声模型的参数估计问题。Class B噪声模型的概率密度函数非常复杂,它表示无限项合流超几何函数之和,很大程度上限制了Class B噪声模型参数估计的研究。为克服概率密度函数复杂的难题,提出了一种基于特征函数谱,采用最小均方梯度法求非线性代价函数解的参数估计算法,解决了Class B噪声模型应用中的核心难题。
4)研究两维的M-Class A噪声模型的参数估计。为实现超低频信号的全向最佳接收必须研究多维噪声模型参数估计问题。提出一种二维M-Class A噪声模型的参数估计器,它基于参数最大后验概率的贝叶斯推理,采用马氏链蒙特卡罗法求非线性函数的解。该估计器对于较小的样本仍然具有快速收敛优点,尽管该估计器运算量很大,却是低复杂度的,其简洁的特征,可用于实时运算中。为未来具有全向接收能力的潜艇通信最佳接收确立理论和工程应用基础。
5)研究超低频接收机的工程实现问题。对于超低频接收机信号结构设计中的编译码单元、调制解调单元和扩频抗干扰单元,结合设计目标分别给出了设计的理论依据,关键指标优化以及工程实现因素的考虑。对于信号处理单元,采用基于Class A模型的参数估计算法,有效地利用信道噪声的统计特性,解决非高斯脉冲噪声下最佳信号接收问题,大幅度地提高了接收机性能。
The Extremely Low Frequency(ELF) communication system is designed to operate in the frequency between 30 Hz and 300 Hz.Because the low attenuation rate in seawater and in the earth-ionospheric waveguide,the ELF communication can provide nearly global coverage and shore-to-submarine communication to submerged submarines below 100M seawater.So the ELF communication has extremely important value in military areas.
Limited by the antennas and the transmitter of the ELF communication system,the available signal strength at the receiver is very small and the performance of the receiver system can have a significant rely on the ELF noise statistical characteristics.The ELF noise is known as atmospheric noise and its statistical characteristics is non-Gaussian. Conventional linear receivers which are effective and optimum for white Gaussian noise can drastically degrade the performance or even made ineffective when operating against the non-Gaussian noise.On the other hand,the performance of communications operating in such impulsive channels can be greatly enhanced if the true statistics of the channel are known and exploited.
This dissertation focuses on the model of the ELF noise,verified noise model by real data,the parameters estimation of noise model and engineering application which implement the ELF receiver.The research fellow the method of combining theory with engineering application.The main contributions are as follow:
1) For analyzing the Amplitude Probability Distribution(APD) of the noise from the device of GSM-06,the measured noise is processed by FFT filter which is used for suppressing the interfere of 50Hz.The Non-Gaussian property of ELF noise is testified by Lilliefors Hypothesis Testing.With considering the APD and the Q-Q plot(Quantile-Quantile Plot) of real ELF atmospheric noise based on the parameters estimation of Class A noise model,The conclusion is made by excellent agreement between the measured data and the theoretical curves which are provided by Class A.
2) This dissertation addresses the loss performance of Turbo decode in No-Gaussian impulsive noise by computer simulation and proposes a new modified decoding algorithm for Turbo code by using Middleton's Class A impulsive noise model based on statistical nature of the amplitude.The decoding algorithm improved performance by modified extrinsic and channel information of traditional MAP for suiting additive white Class A noise,In addition,The results of computer simulation have shown that the proposed algorithm has better performance than traditional MAP decoding algorithm in Class A noise.
3) Two estimations of parameters Class A noise model is provided,the one is based on Characteristic Function Method(CFM) and the other is based on Bayesian estimator, the Bayesian estimator of the Non-Gaussian model parameters is derived and calculated by the Markov Chain Monte Carlo(MCMC) procedure.The considered estimator provides a novel method for advantage of low-complexity,fast convergence with small sample sizes.The estimator of CFM is small computations,fast convergence with large sample sizes,especially suit for the case which the property of channel noise is stabilization.The estimation parameters of the simplified Class B noise model is derived. The simplified class B distribution is represented by a weighted sum of confluenthypergeometric function.The applicability of Class B noise model is limited because it's very complexity form.In this chapter,the efficient estimation of the Simplified Class B model parameters based on least square gradient method is derived.The considered estimator is fast converges and low-complexity with performance approaching theoretical optima for large data samples.The kernel difficulty of application for Class B model is solved.
4) The estimation parameters of two-dimensional Class A model is derived,for the optima reception of ELF signal is implemented in every direction.An efficient estimation of two-dimensional version Class A noise model parameters based on Markov Chain Monte Carlo(MCMC) is derived.The estimator can estimate five-parameter and hidden states for two-dimensional Class A noise model simultaneously.Simulation of this estimator indicates that this considered estimator is converges rapidly and low-complexity with performance approaching theoretical optima for small data samples,although it has large computations.The foundation of optima reception theory and engineering of application is provided for the future ELF communications in every direction.
5) This dissertation further addresses the engineering implementation of the ELF receiver.The block diagram of the principal components of the receiver,such as filter, MSK-demodulation,Antijamming,and decoding is given when considering the base of design theory,optimum performance and details of the design receiver.For the signal-processing component of the receiver,the estimation parameter of Class A noise model is adapted.The performance of the ELF receiver is improved mostly in the ELF non-Gaussian noise when the statistics of the channel noise are exploited.
受天线及发射机工程实现的制约,超低频通信接收机输入端信号电平很小,噪声是影响超低频通信的主要因素。超低频信道的噪声主要是大气噪声,其统计特性严重非高斯。众所周知,传统的对高斯噪声最优的接收机,在严重非高斯噪声的条件下性能将恶化,甚至不能正常工作。另一方面,如果能够辨识信道噪声的统计特性并加以有效利用,则可以大幅度地提高接收机的性能。
本文围绕超低频信道噪声统计特性的辨识和工程应用问题,取得如下成果:
1)为了对GSM-06测得的超低频大气噪声进行幅度统计分析,采用频域法对超低频信道电磁噪声数据进行预处理,抑制50Hz工频干扰。接着对处理过的超低频信道大气噪声数据进行Lilliefors假设检验,证明超低频信道大气噪声的非正态属性。最后在估计Class A瞬时幅度概率分布参数值的基础上,根据概率分布图和Q-Q图结果综合分析做出结论:采用Class A瞬时幅度概率模型,可以很好地描述宽带超低频信道大气噪声数据的幅度统计特性,而对于窄带超低频噪声数据适用于Class B模型描述。
2)对Turbo码的译码算法在非高斯脉冲型噪声环境下的性能进行研究,提出一种新的基于噪声幅度统计特性服从Class A噪声模型的修正译码算法。该算法修正传统MAP算法的外部信息和信道信息计算公式,使之适应非高斯的Class A脉冲噪声环境从而改进了译码性能,计算机仿真证明新的译码算法在Class A噪声环境中比传统MAP算法具有更好的性能。
3)针对经典Class A噪声模型的参数估计问题。提出二种参数估计算法,一种基于特征函数谱,而另一种基于参数的贝叶斯估计。贝叶斯估计器采用马氏链蒙特卡罗法(MCMC)求解,适用于较小的样本数,具有低复杂度和快速收敛的优点。基于特征函数谱参数估计算法适用于较大的样本数,对应于信道噪声统计特性较为平稳的情况,它的优点是运算量小,参数估计速度快。研究简化Class B噪声模型的参数估计问题。Class B噪声模型的概率密度函数非常复杂,它表示无限项合流超几何函数之和,很大程度上限制了Class B噪声模型参数估计的研究。为克服概率密度函数复杂的难题,提出了一种基于特征函数谱,采用最小均方梯度法求非线性代价函数解的参数估计算法,解决了Class B噪声模型应用中的核心难题。
4)研究两维的M-Class A噪声模型的参数估计。为实现超低频信号的全向最佳接收必须研究多维噪声模型参数估计问题。提出一种二维M-Class A噪声模型的参数估计器,它基于参数最大后验概率的贝叶斯推理,采用马氏链蒙特卡罗法求非线性函数的解。该估计器对于较小的样本仍然具有快速收敛优点,尽管该估计器运算量很大,却是低复杂度的,其简洁的特征,可用于实时运算中。为未来具有全向接收能力的潜艇通信最佳接收确立理论和工程应用基础。
5)研究超低频接收机的工程实现问题。对于超低频接收机信号结构设计中的编译码单元、调制解调单元和扩频抗干扰单元,结合设计目标分别给出了设计的理论依据,关键指标优化以及工程实现因素的考虑。对于信号处理单元,采用基于Class A模型的参数估计算法,有效地利用信道噪声的统计特性,解决非高斯脉冲噪声下最佳信号接收问题,大幅度地提高了接收机性能。
The Extremely Low Frequency(ELF) communication system is designed to operate in the frequency between 30 Hz and 300 Hz.Because the low attenuation rate in seawater and in the earth-ionospheric waveguide,the ELF communication can provide nearly global coverage and shore-to-submarine communication to submerged submarines below 100M seawater.So the ELF communication has extremely important value in military areas.
Limited by the antennas and the transmitter of the ELF communication system,the available signal strength at the receiver is very small and the performance of the receiver system can have a significant rely on the ELF noise statistical characteristics.The ELF noise is known as atmospheric noise and its statistical characteristics is non-Gaussian. Conventional linear receivers which are effective and optimum for white Gaussian noise can drastically degrade the performance or even made ineffective when operating against the non-Gaussian noise.On the other hand,the performance of communications operating in such impulsive channels can be greatly enhanced if the true statistics of the channel are known and exploited.
This dissertation focuses on the model of the ELF noise,verified noise model by real data,the parameters estimation of noise model and engineering application which implement the ELF receiver.The research fellow the method of combining theory with engineering application.The main contributions are as follow:
1) For analyzing the Amplitude Probability Distribution(APD) of the noise from the device of GSM-06,the measured noise is processed by FFT filter which is used for suppressing the interfere of 50Hz.The Non-Gaussian property of ELF noise is testified by Lilliefors Hypothesis Testing.With considering the APD and the Q-Q plot(Quantile-Quantile Plot) of real ELF atmospheric noise based on the parameters estimation of Class A noise model,The conclusion is made by excellent agreement between the measured data and the theoretical curves which are provided by Class A.
2) This dissertation addresses the loss performance of Turbo decode in No-Gaussian impulsive noise by computer simulation and proposes a new modified decoding algorithm for Turbo code by using Middleton's Class A impulsive noise model based on statistical nature of the amplitude.The decoding algorithm improved performance by modified extrinsic and channel information of traditional MAP for suiting additive white Class A noise,In addition,The results of computer simulation have shown that the proposed algorithm has better performance than traditional MAP decoding algorithm in Class A noise.
3) Two estimations of parameters Class A noise model is provided,the one is based on Characteristic Function Method(CFM) and the other is based on Bayesian estimator, the Bayesian estimator of the Non-Gaussian model parameters is derived and calculated by the Markov Chain Monte Carlo(MCMC) procedure.The considered estimator provides a novel method for advantage of low-complexity,fast convergence with small sample sizes.The estimator of CFM is small computations,fast convergence with large sample sizes,especially suit for the case which the property of channel noise is stabilization.The estimation parameters of the simplified Class B noise model is derived. The simplified class B distribution is represented by a weighted sum of confluenthypergeometric function.The applicability of Class B noise model is limited because it's very complexity form.In this chapter,the efficient estimation of the Simplified Class B model parameters based on least square gradient method is derived.The considered estimator is fast converges and low-complexity with performance approaching theoretical optima for large data samples.The kernel difficulty of application for Class B model is solved.
4) The estimation parameters of two-dimensional Class A model is derived,for the optima reception of ELF signal is implemented in every direction.An efficient estimation of two-dimensional version Class A noise model parameters based on Markov Chain Monte Carlo(MCMC) is derived.The estimator can estimate five-parameter and hidden states for two-dimensional Class A noise model simultaneously.Simulation of this estimator indicates that this considered estimator is converges rapidly and low-complexity with performance approaching theoretical optima for small data samples,although it has large computations.The foundation of optima reception theory and engineering of application is provided for the future ELF communications in every direction.
5) This dissertation further addresses the engineering implementation of the ELF receiver.The block diagram of the principal components of the receiver,such as filter, MSK-demodulation,Antijamming,and decoding is given when considering the base of design theory,optimum performance and details of the design receiver.For the signal-processing component of the receiver,the estimation parameter of Class A noise model is adapted.The performance of the ELF receiver is improved mostly in the ELF non-Gaussian noise when the statistics of the channel noise are exploited.
引文
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