基于加窗四阶累量时延估计的微瞬态电磁辐射信号检测算法

以研究微弱瞬态电磁辐射信号的探测手段为目的,提出了一种基于加窗四阶累量时延估计的检测算法。利用高阶累量对高斯噪声的抑制作用以及相干平滑变换对微弱窄带噪声的弱化效果,将低信噪比条件下非周期微弱放电信号的检测问题转化为周期性时延参数的估计问题。仿真及实验比较了加窗四阶累量互相关、非加窗四阶累量互相关及传统互相关时延估计3种算法的检验性能。结果表明,经过加窗处理的四阶累量时延估计算法在相关高斯白噪声、非相关高斯白噪声及窄带噪声3种噪声条件下的检测性能都优于其他两种方法,比较适合用于微弱电磁辐射源的远距离探测。

未知互耦条件下基于四阶累量的非均匀圆阵波达方向估计算法(英文)

基于互耦矩阵的特殊结构,给出了一种更具一般性的非均匀圆阵模型,提出了一种在未知互耦条件下的非均匀圆阵波达方向估计算法.由于采用了四阶累量,该算法可以估计出比阵元数更多信号的波达方向,并且对高斯噪声不敏感.由于未采用迭代算法,从而降低了算法运算复杂度.仿真实验证实了该算法的有效性.

一种基于四阶累量的二维空间谱估计的新方法

本文针对有色高斯噪声背景中空域信号参数估计问题，提出了一种基于四阶累量的二维空间谱估计的新方法。在这种方法中，利用两个具有位移特性的二维传感器子阵列的四阶累量信息构成一种新的矩阵—四阶累量混合波达方向矩阵。对四阶累量混合波达方向矩阵进行特征分解，可实现有色高斯噪声背景中空域信号二维空间谱估计。

基于TD-SCDMA无线定位虚拟线阵四阶累量修正MUSIC算法的研究

无线定位的圆-角定位技术中,DOA估计极其重要。本文针对基于TD-SCDMA智能天线预处理后的虚拟均匀线阵MUSIC算法带来的阵列孔径小,抗阵元误差扰动性差的不足,研究了基于模式空间虚拟均匀线阵四阶累量的MUSIC算法,由于虚拟线阵四阶累量MUSIC算法的应用范围局限于独立的信号源的DOA估计,不能用于相关信号源DOA估计,因而提出了基于模式空间虚拟均匀线阵四阶累量的修正MUSIC(FOC-MMUSIC)算法,有效地拓展了阵元孔径,改善了系统抗阵元误差扰动和算法对相关信号源DOA的估计性能。

用四阶累量估计相干信号源空间谱的一种新方法

提出用四阶累量来估计有色高斯噪声中相干信号源空间谱的一种新方法。这种方法中,首先利用四阶累量抑制有色高斯噪声,其次形成空间差分平滑矩阵,使其秩与相干源个数相等,然后用MUSIC方法估计空间谱,最后给出模拟实验来验证改进方法的有效性。

基于四阶累量的三次相位耦合特性分析

应用四阶累量分析三次相位耦合于舰船辐射噪声的特性提取。首先给出了四阶累量的一种特殊定义，然后分析其对角切片的傅立叶变换（维谱）在提取三次相位耦合特征方面的优良特性，最后将此方法应用于实际舰船辐射噪声的特征分析。结果表明：利用该方法分析三次相位耦合，具有运算量小、计算速度快的特点和良好的抗噪性。实际舰船辐射噪声数据分析表明该方法可用于不同类型舰船的分类识别。

基于四阶累量的PSK信号信噪比估计高效算法

在无线数字通信中,许多算法都需要知道信噪比以获得最优性能。提出一种基于四阶累量的PSK信号信噪比估计算法,该算法具有较好的性能和较低的运算复杂度,可以满足软件无线电系统对信噪比估计精度和实时性的要求。

基于四阶累量对角切片谱的线谱估计方法

研究了一种基于信号四阶累量对角切片谱的时频分布图的水中目标辐射噪声的线谱估计方法。分析了四阶累量对角切片谱具有的性质,即抑制高斯噪声和对称分布噪声;与功率谱具有相似的谱结构。介绍了Lofar处理的特点和Lofar谱图的计算步骤。结合Lo-far谱图和四阶累量对角切片谱的各自特点,提出了四阶累量对角切片谱图的处理方法。再利用图形学中的Radon变换,将谱图中的直线转换成Radon域中的峰值。得到峰值坐标后,可以计算谱图中的线谱频率等参数。仿真实例表明,该方法效果明显。

基于高阶累量与神经网络覆盖算法的数字调制识别

提出了一种把归一化四阶累量作为分类特征参数,应用神经网络覆盖算法进行分类的调制识别算法。对MFSK信号的三种归一化四阶累量进行了理论推导,对几种不同调制方式的数字通信信号的三种特征参数随信噪比的变化进行了仿真,证明了高阶累量不受高斯噪声影响的理论结果。最后应用神经网络覆盖算法进行分类识别,得到了比较好的识别结果。

谐波恢复中累量估计的强收敛问题

本文讨论有噪声污染的谐波信号累量的单一记录估计。证明了样本自相关函数的强收敛性,并得到了强收敛速度。对于四阶矩估计,给出了四阶遍历条件。在这些遍历条件下,建立了四阶矩和四阶累量样本估计的强收敛性,并得到了强收敛速度。最后给出了数值仿真结果。

阵列通道复增益盲估计

在阵列接收机各通道增益和相位不一致时，基于模型的超分辨波达方向估计性能大大下降。在已知２个信号的波达方向差的条件下，提出一种基于四阶累量进行盲信号处理的新算法。在该方法中首先求出阵列流形，然后利用搜索法估计阵列通道复增益矩阵。

JOINTLY ESTIMATING BOTH RANGE AND DOA OF NEAR FIELD SOURCE

A computationally efficient method for jointly estimating both Directions Of Arrival (DOA) and ranges of near field sources is presented. The proposed algorithm does not need any spectral peak searching and the 2-D parameters are automatically paired. It is suitable for arbitrary additive Gaussian noise environment. Furthermore, its performances are confirmed by computer simulations.

An Array Extension Method in a Noisy Environment

An array extension method in a noisy environment was proposed to improve angular resolution and array gain. The proposed method combines the FOC （fourth-order cumulants） technique with the ETAM （extended towed array measurements） method to extend array aperture and suppress Gaussian noise, First, successive measurements of a virtual uniform linear array were constructed by applying lburth-order cumulants to measurements of uniform linear array; Gaussian noise in these measurements was also eliminated. Then, the array was extended by compensating phase differences using the ETAM method, Finally, the synthetic aperture was extended further by the fourth-order cumulants technique. The proposed FOC-ETAM-FOC method not only improves angular resolution and array gain, but also effectively suppresses Gaussian noise. Furthermore, it inherits the advantages of the ETAM method. Simulation results showed that the FOC-ETAM-FOC method achieved better angular resolution and array gain than the ETAM method. Furthermore this method outperforms the ETAM method in Gaussian noise environment.

Resolution performance analysis of cumulants-based rank reduction estimator in presence of unexpected modeling errors

Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and mitigating the negative influences of the Gaussian colored noise. However, in the presence of unexpected modeling errors, the resolution behavior of the FOC-RARE also deteriorate significantly as SOS-RARE, even for a known array covariance matrix. For this reason, the angle resolution capability of the FOC-RARE was theoretically analyzed. Firstly, the explicit formula for the mathematical expectation of the FOC-RARE spatial spectrum was derived through the second-order perturbation analysis method. Then, with the assumption that the unexpected modeling errors were drawn from complex circular Gaussian distribution, the theoretical formulas for the angle resolution probability of the FOC-RARE were presented. Numerical experiments validate our analytical results and demonstrate that the FOC-RARE has higher robustness to the unexpected modeling en＇ors than that of the SOS-RARE from the resolution point of view.

Performance of cumulant-based rank reduction estimator in presence of unexpected modeling errors

Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.