无源探测系统DOA估计关键技术研究
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摘要
波达角(Direction of Arrival, DOA)估计是现代阵列信号处理领域中的重要研究内容,它在通信、雷达、声纳等诸多领域有着非常广泛的应用。为了使无源探测系统具有对同时到达雷达信号DOA进行估计的能力,本课题主要针对无源探测系统面临的复杂电磁环境,围绕阵列天线DOA估计在无源探测系统中应用所面临的关键问题展开研究。研究内容主要包括阵列DOA快速估计、复杂雷达信号DOA估计以及信源数估计与阵列误差校正问题,针对每个问题进行深入细致的探讨,并提出了相应的解决方法。
针对窄带雷达信号到达角快速估计引入传播算子方法(Propagator Method,PM)(?)弋替传统方法中的特征值分解步骤,快速获得信号和噪声子空间,解决了无源探测快速实现多目标DOA估计的问题。首先对PM算法引入对DOA估计带来的误差进行了详细分析。在此基础上,提出了基于PM算法的L型阵2维DOA参数估计快速方法并对算法进行了适当的改进。最后,针对PM方法的缺陷,提出了基于PM算法的信源数与DOA联合估计方法。通过仿真实验证明设计算法可在保证测向精度的同时,具有较高的DOA参数估计速度。
在复杂雷达信号DOA方面,主要研究了两方面内容。分别针对频率不同的窄带雷达信号和宽带信号的DOA估计进行了研究。对于分布于宽频带的窄带信号,由于入射信号频率不同使得传统算法在未知频率情况下,难以使用估计的噪声子空间或信号子空间获得正确的目标DOA。因此根据时空等效性,利用接收信号采样之间的时间延时与阵元之间接收信号程差带来的等效延时同时估计信号的频率与DOA参数。在对均匀线阵条件下频率与DOA联合估计进行分析后,重点研究了任意阵列结构的联合估计问题。在状态空间频率与DOA联合估计方法基础上,通过对延时自相关矩阵的结构变化,提出了基于二阶盲分离(Second-Ordor Blind Idnetificaion, SOBI)的DOA与频率联合估计方法,与状态空间的频率-DOA联合估计相比,其优势在于可以对信号数多于天线阵元数的情况进行信号的频率-DOA估计。最后提出了一种快速的频率-DOA方法,保证测向精度同时减小了计算负担。
对于多个宽带信号的DOA估计,本文主要从时频分析角度出发,通过对信号的时频分析将信号变化到时频域或模糊域,实现多个宽带信号的提取及其DOA估计。相对传统宽带信号DOA估计,通过宽带信号在时频平面的能量聚集性,可实现信噪比(Signl to Noise Ratio, SNR)的提高及信号的时频参数提取,从而改善了DOA估计性能。由于该类方法需要处理时空频3维矩阵,其计算量相对较大。所以提出了两种时频域DOA快速估计方法。在时频域DOA估计基础上,重点研究了时频模糊域信号DOA估计方法,主要针对线性调频体制这种确定信号,提出了在模糊域进行DOA估计的思想,设计了几种估计方法,发展了时频分析在DOA估计中的应用。相对于时频域DOA估计,其优势在于可实现宽窄与宽带信号自动分离；导向矢量结构不包含信号载频信息,使DOA估计突破了阵元间距的限制。
对于影响DOA估计性能的信源数估计和阵元通道幅相误差校正问题,文章将多变量分析的因子分解技术引入到阵列信号DOA处理当中。提出了使用因子分解模型近似阵列接收信号模型。通过统计信号处理实现了在色噪声情况下,对辐射源数的估计问题。计算机实验验证了算法的估计性能在一定的虚警概率条件下优于传统的盖尔圆盘法。并利用因子分解模型,提出了最小方差和列向量比值方法实现对各个阵元通道幅度和相位不一致性误差的估计,通过在系统前端增加辅助信号源的方法,获取通道幅度和相位不一致性误差的快速处理。实现了对通道不一致性的在线校正,且有运算时间少的优点。
Direction of arrival (DOA) estimation by array is an important research branch in the field of modern signal processing. It has a great variety of applications in many fields, such as communications, radar and sonar, etc. In order to make passive detection system have the ability of measuring multiple radiant signals arrived simultaneously and improve the angle resolution of DOA estimation, application of super-resolution DOA estimation algorithm by array to passive detection system is researched deeply in the dissertation. According to the complexity of electromagnetism condition faced to passive detection, the dissertation extend the research of key techniques for passive detection system, including fast DOA estimation, complex radar signal DOA estimation, source number estimation and array channel calibration. Each subject is studied in detail and corresponding algorithms are proposed. The contributions and fruits of the dissertation are listed as follow.
In the aspect of DOA estimation for multiple narrow signals, the dissertation focuses on fast subspace calculation for the data received, because the implementation of subspace methods in passive detection application with real-time operation usually experiences a bottleneck in the calculation of the signal or noise subspace. Propagator Method (PM) for fast computation of signal or noise subspace has introduced. Superior to conventional DOA estimation, PM can obtain the noise subspace and signal subspace fastly in stead of Eigen-Value Decomposition (EVD). The performances of the Propagator Method in terms of the mean squared error on DOA estimation are investigated in detail. Based on Propagator Method, several 2D fast algorithms of DOA parameter estimation have designed on L shape of array structure. Furthermore, a joint estimation of source number and DOA is proposed in order to conquer the inherence of limitation in PM for application. Computer simulation confirms the proposed methods valid impressively.
Many methods of DOA estimation are essentially limited to processing narrow-band data. This is indeed a realistic assumption in many applications (e.g. active radar and communication). However, in the cases of passive detection system, the received signals may be broadband or narrowband distributed in broadband. In the environment of multiple narrow-band signals distributed in broadband, the subspace structure of data received by array can not be generally used for DOA estimation without the frequency information of each signal received. So the joint frequency-DOA methods of multiple narrowband signals are researched. Characteristic of time-space equivalence is properly used to joint frequency-DOA estimation. After analysis of joint frequency-DOA estimation techniques by uniform linear array (ULA), Joint frequency-DOA estimation by arbitrary array structure is researched deeply. Based on State-space joint estimation algorithm, a novel joint estimation algorithm, named SOBI (Second-Order Blind Identification) joint frequency-DOA estimation is shown by changing the structure of non-central auto-covariance matrix of data. Compared to the State-space algorithm, the new algorithm can realize the frequency-DOAs estimation in the condition that source number is more than the number of sensors in array. At last, a fast algorithm of joint frequency-DOA is explored for real-line application.
For DOA estimation of wideband signals, developing spatial time-frequency technology is much suited for non-stationary signals. Spatial time-frequency distribution (STFD) is illuminated and its structure is analyzed, simulations show that STFD act effective for DOA estimation than the common methods. For dwelling with the large computation load of this kind of methods, two fast algorithm are derived. As the further progress of STFD, spatial ambiguity distribution of the signals received by array are exploit for the scenarios of LFM signals instead of STFD. Ambiguity transformation converts the absolute time and frequency of LFM signal into the domain of relative time lag and frequency difference, which bring more attractive steering vector structure than that in time-frequency domain. Several algorithms for DOA estimation in ambiguity domain for coherent and incoherent LFM signal are proposed, which can be applied to arrays of any aperture size and arbitrary chirp rate signals. The frequency of the sources can be higher than the non-aliasing frequency of array designed for signals and the number of sources can be greater than the number of sensors. The simulations demonstrate the improvements in the DOA estimation of the wideband LFM signal.
At last but not the least, the factor analysis technique is introduced for sources number estimation and array channel calibration, which are two key problems to influence the performance of DOA estimation. Factor analysis model is shown to approximate the model of covariance matrix of data received. Then the statistic signal processing method for factor analysis is applied to estimate the sources number in colour noise condition. The simulation proves that the performance of the method for sources number estimation is better than that of GDE (Gerschgorin Disk Estimator) in certain false alarm rate. The two novel methods of array channel calibration, Least Square algorithm and Column Ration algorithm, are proposed by factor analysis technique. Based on an auxiliary testing source on microwave front-end, the two algorithm can extracting the amplitude-phase gain and noise parameters from the estimated covariance matrix in real-time.
针对窄带雷达信号到达角快速估计引入传播算子方法(Propagator Method,PM)(?)弋替传统方法中的特征值分解步骤,快速获得信号和噪声子空间,解决了无源探测快速实现多目标DOA估计的问题。首先对PM算法引入对DOA估计带来的误差进行了详细分析。在此基础上,提出了基于PM算法的L型阵2维DOA参数估计快速方法并对算法进行了适当的改进。最后,针对PM方法的缺陷,提出了基于PM算法的信源数与DOA联合估计方法。通过仿真实验证明设计算法可在保证测向精度的同时,具有较高的DOA参数估计速度。
在复杂雷达信号DOA方面,主要研究了两方面内容。分别针对频率不同的窄带雷达信号和宽带信号的DOA估计进行了研究。对于分布于宽频带的窄带信号,由于入射信号频率不同使得传统算法在未知频率情况下,难以使用估计的噪声子空间或信号子空间获得正确的目标DOA。因此根据时空等效性,利用接收信号采样之间的时间延时与阵元之间接收信号程差带来的等效延时同时估计信号的频率与DOA参数。在对均匀线阵条件下频率与DOA联合估计进行分析后,重点研究了任意阵列结构的联合估计问题。在状态空间频率与DOA联合估计方法基础上,通过对延时自相关矩阵的结构变化,提出了基于二阶盲分离(Second-Ordor Blind Idnetificaion, SOBI)的DOA与频率联合估计方法,与状态空间的频率-DOA联合估计相比,其优势在于可以对信号数多于天线阵元数的情况进行信号的频率-DOA估计。最后提出了一种快速的频率-DOA方法,保证测向精度同时减小了计算负担。
对于多个宽带信号的DOA估计,本文主要从时频分析角度出发,通过对信号的时频分析将信号变化到时频域或模糊域,实现多个宽带信号的提取及其DOA估计。相对传统宽带信号DOA估计,通过宽带信号在时频平面的能量聚集性,可实现信噪比(Signl to Noise Ratio, SNR)的提高及信号的时频参数提取,从而改善了DOA估计性能。由于该类方法需要处理时空频3维矩阵,其计算量相对较大。所以提出了两种时频域DOA快速估计方法。在时频域DOA估计基础上,重点研究了时频模糊域信号DOA估计方法,主要针对线性调频体制这种确定信号,提出了在模糊域进行DOA估计的思想,设计了几种估计方法,发展了时频分析在DOA估计中的应用。相对于时频域DOA估计,其优势在于可实现宽窄与宽带信号自动分离；导向矢量结构不包含信号载频信息,使DOA估计突破了阵元间距的限制。
对于影响DOA估计性能的信源数估计和阵元通道幅相误差校正问题,文章将多变量分析的因子分解技术引入到阵列信号DOA处理当中。提出了使用因子分解模型近似阵列接收信号模型。通过统计信号处理实现了在色噪声情况下,对辐射源数的估计问题。计算机实验验证了算法的估计性能在一定的虚警概率条件下优于传统的盖尔圆盘法。并利用因子分解模型,提出了最小方差和列向量比值方法实现对各个阵元通道幅度和相位不一致性误差的估计,通过在系统前端增加辅助信号源的方法,获取通道幅度和相位不一致性误差的快速处理。实现了对通道不一致性的在线校正,且有运算时间少的优点。
Direction of arrival (DOA) estimation by array is an important research branch in the field of modern signal processing. It has a great variety of applications in many fields, such as communications, radar and sonar, etc. In order to make passive detection system have the ability of measuring multiple radiant signals arrived simultaneously and improve the angle resolution of DOA estimation, application of super-resolution DOA estimation algorithm by array to passive detection system is researched deeply in the dissertation. According to the complexity of electromagnetism condition faced to passive detection, the dissertation extend the research of key techniques for passive detection system, including fast DOA estimation, complex radar signal DOA estimation, source number estimation and array channel calibration. Each subject is studied in detail and corresponding algorithms are proposed. The contributions and fruits of the dissertation are listed as follow.
In the aspect of DOA estimation for multiple narrow signals, the dissertation focuses on fast subspace calculation for the data received, because the implementation of subspace methods in passive detection application with real-time operation usually experiences a bottleneck in the calculation of the signal or noise subspace. Propagator Method (PM) for fast computation of signal or noise subspace has introduced. Superior to conventional DOA estimation, PM can obtain the noise subspace and signal subspace fastly in stead of Eigen-Value Decomposition (EVD). The performances of the Propagator Method in terms of the mean squared error on DOA estimation are investigated in detail. Based on Propagator Method, several 2D fast algorithms of DOA parameter estimation have designed on L shape of array structure. Furthermore, a joint estimation of source number and DOA is proposed in order to conquer the inherence of limitation in PM for application. Computer simulation confirms the proposed methods valid impressively.
Many methods of DOA estimation are essentially limited to processing narrow-band data. This is indeed a realistic assumption in many applications (e.g. active radar and communication). However, in the cases of passive detection system, the received signals may be broadband or narrowband distributed in broadband. In the environment of multiple narrow-band signals distributed in broadband, the subspace structure of data received by array can not be generally used for DOA estimation without the frequency information of each signal received. So the joint frequency-DOA methods of multiple narrowband signals are researched. Characteristic of time-space equivalence is properly used to joint frequency-DOA estimation. After analysis of joint frequency-DOA estimation techniques by uniform linear array (ULA), Joint frequency-DOA estimation by arbitrary array structure is researched deeply. Based on State-space joint estimation algorithm, a novel joint estimation algorithm, named SOBI (Second-Order Blind Identification) joint frequency-DOA estimation is shown by changing the structure of non-central auto-covariance matrix of data. Compared to the State-space algorithm, the new algorithm can realize the frequency-DOAs estimation in the condition that source number is more than the number of sensors in array. At last, a fast algorithm of joint frequency-DOA is explored for real-line application.
For DOA estimation of wideband signals, developing spatial time-frequency technology is much suited for non-stationary signals. Spatial time-frequency distribution (STFD) is illuminated and its structure is analyzed, simulations show that STFD act effective for DOA estimation than the common methods. For dwelling with the large computation load of this kind of methods, two fast algorithm are derived. As the further progress of STFD, spatial ambiguity distribution of the signals received by array are exploit for the scenarios of LFM signals instead of STFD. Ambiguity transformation converts the absolute time and frequency of LFM signal into the domain of relative time lag and frequency difference, which bring more attractive steering vector structure than that in time-frequency domain. Several algorithms for DOA estimation in ambiguity domain for coherent and incoherent LFM signal are proposed, which can be applied to arrays of any aperture size and arbitrary chirp rate signals. The frequency of the sources can be higher than the non-aliasing frequency of array designed for signals and the number of sources can be greater than the number of sensors. The simulations demonstrate the improvements in the DOA estimation of the wideband LFM signal.
At last but not the least, the factor analysis technique is introduced for sources number estimation and array channel calibration, which are two key problems to influence the performance of DOA estimation. Factor analysis model is shown to approximate the model of covariance matrix of data received. Then the statistic signal processing method for factor analysis is applied to estimate the sources number in colour noise condition. The simulation proves that the performance of the method for sources number estimation is better than that of GDE (Gerschgorin Disk Estimator) in certain false alarm rate. The two novel methods of array channel calibration, Least Square algorithm and Column Ration algorithm, are proposed by factor analysis technique. Based on an auxiliary testing source on microwave front-end, the two algorithm can extracting the amplitude-phase gain and noise parameters from the estimated covariance matrix in real-time.
引文
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