基于信息极大化的ICA算法研究

独立成分分析是在缺乏混合系统和源信号先验知识的条件下,仅通过观测信号来恢复出源信号,已经成为数字信号处理的重要研究方向。文中在研究独立成分分析理论的基础上,介绍了一种基于信息极大化的分离算法,并对混合音频信号进行仿真试验,成功分离出了混合信号中的各个独立成分,仿真结果验证了信息极大化分离算法在语音增强和助听器功能改善方面的优越性,具有一定的理论研究和应用价值。     

基于先验信息的M-APSK信号迭代信噪比估计

该文提出了一种适合多进制振幅移相键控信号信噪比估计的迭代算法。给定先验信息的条件下,由经过平坦衰落的多进制振幅移相键控信号的概率分布出发,对其对数期望求导得到信噪比参数和数据的关系,引入数据可信度更新信噪比参数,从而获得相应的迭代算法。与已有的矩方法相比,该文提出的基于先验信息迭代算法在迭代四次,互信息大于0.4以上时性能优于已有非数据辅助算法,而无先验信息下的五次迭代时中高信噪比下优于矩方法。     

Signal reconstruction from the phase of the bispectrum

The authors present a simple procedure, the bispectrum signal reconstruction (BSR) algorithm, to recover the Fourier phase of a signal from the phase of its bispectrum. By simple analogy, a procedure that recovers the Fourier magnitude of a signal from the magnitude of its bispectrum is also presented. In addition, the authors propose an iterative scheme, the bicepstrum iterative reconstruction algorithm (BIRA), for the reconstruction of a finite impulse response (FIR) sequence from only the phase of its bispectrum, and they demonstrate how some a priori information on the energy of the cepstra coefficients can improve significantly the convergence rate of the algorithm. Both schemes are based on the key observation that the differences of the bispectrum coefficients contain all the information concerning the Fourier phase of the signal, whereas their sums contain the Fourier-magnitude information     

基于峭度的盲分离在通信信号盲侦察中的应用

为实现复杂多信号环境下的通信信号侦察,采用一种新的盲侦察技术,即运用盲源分离算法,在没有任何先验知识的情况下分离出源信号,然后对分离的各个信号进行后续处理。提出一种改进的基于峭度的盲分离算法,可以自适应地确定激活函数。将其应用在通信信号盲侦察中,可以实现对任意源信号进行盲分离,而不管它是超高斯还是亚高斯信号。选择超高斯和亚高斯混合通信信号进行了仿真实验,结果验证了该算法的有效性。     

Robust adaptive array beamforming under steering vector errors

This paper considers adaptive array beamforming in the presence of random steering vector errors. We first formulate the problem of finding an optimal steering vector as an optimization problem. The cost function to be minimized consists of two terms which utilize a posteriori information due to the received signal data and a priori information due to the probabilistic distribution of steering errors, respectively. Two methods are then presented to find the optimal steering constraint vector. It is shown that each method yields a closed-form optimal solution if the steering error vector is an additive Gaussian random vector. We also investigate the performance for each method. Modification of the proposed methods and an implementation algorithm for dealing with the case of steering vector errors due to phase perturbation are also presented. Finally, several computer simulation examples are presented for illustration     

基于拟合优度检测的子带频谱检测算法

根据雷达对抗侦察接收信号的特点,设计了基于动态非均匀信道化滤波的接收结构.重点研究了其中的频谱感知环节,提出了基于拟合优度检测的子带频谱检测算法.该算法首先假设至少含有K个子带不存在信号,再根据能量最小的K个子带输出估计噪声方差,用估计的噪声方差标准化各子带输出,从而将子带频谱检测问题转化为子带拟合优度的检测问题.该算法能够适用于噪声方差未知且无任何信号先验信息情况.理论分析和仿真实验证明了算法的有效性.     

毫米波MIMO系统中稀疏度自适应的压缩感知信道估计

利用毫米波MIMO系统的稀疏特性,信道估计可以转化为稀疏信号重构的问题。解决毫米波MIMO稀疏信道估计问题时,传统的OMP方法需要信号的稀疏度作为先验信息,实际系统难以满足此需要。文中引入StOMP算法,根据信号已知的稀疏先验信息确定阈值,并结合动态的阈值调整,提出一种新的StOMP-D算法,实现了稀疏度自适应的毫米波MIMO信道估计。仿真结果表明,所提的方法与传统的LS方法比较,信道估计性能显著提高,并且与稀疏度已知的OMP方法性能十分接近,在稀疏度未知时有明显的优势。     

Constraint optimum well-log signal segmentation

A special classification algorithm is proposed that can be applied to a preprocessed (or the original noisy) well-log signal for segmentation. Knowledge of the number of segments or any other constraint, if existent, along with a criterion function can be used to complete the algorithm. The preprocessing routine consists of a running window change-detection algorithm which detects all the potential candidates for the location of changes in the signal. This routing can be applied in way that significantly overestimates the number of changes. These points of change along with other estimated parameters are used by the classification algorithm to find the global best-segmentation that agrees with the a priori knowledge of the number of segments (or any other constraint) and satisfies a criterion function. The resultant optimum classification algorithm is recursive and computationally efficient. The performance of the overall algorithm is demonstrated by several examples     

Fusion Forward–Backward Pursuit Algorithm for Compressed Sensing

The Forward–Backward Pursuit (FBP), which is a recently proposed method, receives wide attention due to the high reconstruction accuracy. In this paper, we use the fusion strategy and propose the Fusion Forward–Backward Pursuit (FFBP) algorithm. This strategy only needs the reconstruction information of two FBP with different parameters. According to the termination conditions of the FBP algorithm, FFBP adopts different operation strategies, during the signal reconstruction. Without other priori information, FFBP effectively improves the exact reconstruction rate, compared with the original algorithm. Moreover, FFBP, which fuses two FBP with non-optimal parameter, can reconstruct a better signal than a single FBP with optimal parameter. We demonstrate the advantage of the proposed method through numerical simulations.     

基于ES-DOA估计的室内非法信号源定位方法

针对室内非法信号源定位应用的特点,提出一种基于到达方向(DOA)估计的无源定位方法。采用具有解相干能力的基于特征空间的到达角(ES-DOA)估计算法估计来波到达角度空间谱和对应的信号功率;采用一种新的直达路径集成搜寻算法判断信号的直达路径方向,并通过交叉测量法估计非法信号源的位置。仿真实验表明,ES-DOA算法在处理相干阵列信号时,具有高分辨力和较强的抗噪声能力,提出的直达路径识别算法能够在不同室内环境中准确识别信号的直达路径方向,具有较强的鲁棒性。研究证明,该定位方法具有较强的系统可实现性,既能单站定位,又可多站协同工作,部署方便,使用灵活,是一种有效的室内非法信号源定位解决方案。