一种可用于声目标特征提取的超分辨率混合谱估计算法

本文研究了一种估计有色噪声中多组谐波信号参数的去耦参数估计算法。其中提出了一种基于ＦＦＴ的“谐波组迭代清除算法”来估计谐波信号的参数，并根据“谐波图”的值来确定谱波信号的个数。该算法不仅计算简单并可获得较高的频率的分辨率，而且对信号模型具有较强的鲁棒性。     

多族谐波信号估计的子空间匹配投影算法

该文根据子空间匹配投影分解算法,将信号自适应地分解在谐波子空间上,得到一种多族谐波信号参数估计的方法。这种方法利用插值FFT算法和谐波参数的先验信息,能分离基频或谐波频率结构较为紧密的不同族谐波分量,谐波频率估计精度高。仿真表明,这种新方法能准确地估计基频频率、谐波频率和阶数,对随机相位抖动也不敏感;用于分析实船噪声也准确反映了船舶辐射噪声功率谱的线谱规律和特点,较好地揭示了船舶的螺旋桨现场实际运行状况,是一种很有价值的多谐波参数估计方法。     

基于信号稀疏表示的Chirp信号参数估计方法

单自由度多分量线性调频信号的参数估计是SAR/ISAR成像中的重要问题。文中在一组含参数的过完备基上展开信号,提出了一种基于信号稀疏表示估计单自由度多分量线性调频信号参数的新方法。并将该方法应用于目前流行的BP算法、FOCUSS算法和稀疏贝叶斯学习算法中,分析比较了以上算法的估计性能。仿真实验结果验证了该方法的有效性,并表明稀疏贝叶斯学习算法具有更好的稀疏表示能力,从而可以更有效地估计信号参数。     

基于谐波提取的欠定语音盲分离方法

现有的欠定语音信号盲分离算法往往不能同时兼顾分离性能及效率。针对此问题,本文提出一种基于谐波提取的欠定盲分离方法。首先,利用频谱校正从混合信号的短时傅立叶变换中提取谐波参数,其次利用相位一致性准则甄别这些参数的单源属性,进而用自适应K-均值方法对单源模式做聚类而获得源数估计和混合矩阵估计,最后再用子空间投影法恢复源信号。其中谐波提取和单源参数筛选可保证低复杂度地精确估计出混合矩阵。仿真实验表明,相比于原始子空间投影算法,本文方法可获得更高的信号恢复质量,且在谐波相关领域也具有潜在应用价值。      

基于复调制细化谱分析的轧辊偏心谐波参数估计

针对轧辊的上、下支撑辊偏心信号是密集型频谱的特点,提出了一种基于复调制细化谱分析方法的轧辊偏心谐波参数估计新方法。利用复调制细化谱分析方法准确估计出偏心谐波的频率及幅值;同时,结合快速傅里叶变换算法估计出偏心谐波的相角参数。仿真实验结果验证了该方法在偏心谐波参数估计上的有效性和可行性。     

基于单电磁矢量传感器的多参数联合估计

利用电磁矢量传感器来实现信号多参数的联合估计是近年来信号处理领域的一种新的方法和途径。基于单个电磁矢量传感器通过构造两组具有固定时延的接收数据,利用TLSESPRIT算法和矢量叉积相结合实现了多参数的联合估计,参数自动完成配对,可以最多同时估计5个不相关的信号,且空间到达角、极化参数的估计和信号频率的估计是两个相互独立的过程互不影响,与利用极化敏感阵列进行参数估计相比计算量小,并通过计算机仿真验证了此方法的有效性。     

基于稀疏迭代协方差矩阵的谐波参数快速估计方法

针对稀疏迭代协方差估计(sparse iterative covariance-based estimation, SPICE)方法功率谱估计精度较低和计算复杂度较高的局限性,提出了一种基于稀疏迭代协方差矩阵的谐波信号功率谱和频率参数的快速估计方法。该方法主要结合渐近最小方差准则和快速傅里叶变换,对功率谱参数进行快速迭代校正估计。首先,使用SPICE算法得到功率谱和频率参数的初估计。然后,通过渐近最小方差准则得到功率谱参数的迭代校正表达式。最后,利用功率谱迭代校正式获得谐波信号的功率谱和频率参数的估计。为提高算法的计算效率,利用观测数据协方差矩阵的Toeplitz结构和导向矢量的指数形式,对协方差矩阵进行(Gohberg-Semencul, G-S)分解,通过快速傅里叶变换对协方差矩阵求逆和矩阵与向量相乘部分进行求解,从而使参数估计的计算时间大大减少。仿真实验表明,验证了所提算法对谐波功率谱和频率参数具有较高的估计精度,并且计算复杂度较低。      

有色噪声中正弦信号参数的去耦估计算法

首先介绍了一种估计有色噪声中多个正弦信号参数的去耦参数估计算法，然后提出了一种基于ＦＦＴ的“迭代清除算法”来估计有色噪声中多个周期相近信号的参数。该算法不仅计算简单，而且可获得较高的频率分辨率。数值仿真结果表明了该算法的有效性。     

有色噪声中一维谐波频率估计的稳健迭代算法

本文通过构造基于观测信号的统计量,采用三步迭代（TSI）算法来估计乘性和加性有色噪声中一维谐波信号频率参数,得到了最终估计量的渐近分布,证明了估计的一致性。TSI算法通过引入周期图估计作为初估计,从谐波模型内在特性出发构造统计量,采用迭代方式逐步提高初估计精度,仅需三次迭代就能达到加性噪声情形下最小二乘估计（LSE）的关于样本的收敛速度。由于只需要三次迭代就可以达到收敛,所以算法的计算量比较少。另外相比较传统的迭代算法而言,TSI算法能保证每次迭代后都能够提高估计的精度,从而克服了传统的迭代算法收敛不够稳定不足。仿真实验证实了估计的一致性以及估计的渐近分布,而且在较大噪声情形下该迭代算法依然可行。最后,由于TSI算法具备小的计算量以及高的估计精度,因而十分适合作为一维谐波参数估计的在线算法。      

基于循环统计量检测的QAM信号多普勒参数盲估计

针对动中通卫星通信系统中QAM信号多普勒参数盲估计难的问题,提出了一种基于循环统计量检测的盲估计算法。算法无需同步和匹配滤波等预处理,通过检测QAM信号四倍频偏处的循环频率来估计多普勒频偏,并利用数据分段求频率的方法计算变化率。仿真结果表明,在低信噪比的高斯噪声信道和多径衰落信道下,算法均可以准确而有效地实现QAM信号多普勒参数的盲估计。     

基于周期谱包络的线性调频信号特征参数估计

线性调频信号是二阶循环平稳信号,其周期谱幅度包含了信号的最小载频、最大载频以及调制斜率等信息。本文提出了利用非零循环频率处谱峰的位置与大小估计这三个参数的算法,该算法不需要信号参数的先验信息;对参数的估计是在非零循环频率处进行一维搜索,可减小平稳噪声和干扰的影响,适用于低信噪比情况,且避免了多维搜索;文中给出了算法的实现过程,分析了其估计性能,最后进行了计算机仿真实验。     

A Survey on Wireless Position Estimation

In this paper, an overview of various algorithms for wireless position estimation is presented. Although the position of a node in a wireless network can be estimated directly from the signals traveling between that node and a number of reference nodes, it is more practical to estimate a set of signal parameters first, and then to obtain the final position estimation using those estimated parameters. In the first step of such a two-step positioning algorithm, various signal parameters such as time of arrival, angle of arrival or signal strength are estimated. In the second step, mapping, geometric or statistical approaches are commonly employed. In addition to various positioning algorithms, theoretical limits on their estimation accuracy are also presented in terms of Cramer–Rao lower bounds.

基于斜投影的DOA估计和盲信号识别协同算法

阵列信号处理领域中,经常需要分析阵列接收信号的波达方向(DOA)和波形信息。在缺乏信号先验DOA和波形信息的情况下,提出了一种新的斜投影DOA估计和盲信号识别协同算法,实现对未知阵列接收信号的自适应分析。该算法交替地采用斜投影空域滤波技术将阵列接收信号拆分为单信号群组,在每个单信号群组内进行DOA和波形联合估计。算法适用于主瓣干扰存在的情况,并且在中小快拍数下有较好的估计性能。理论分析和仿真实验证实了文中算法的有效性。     

A fast algorithm for joint two-dimensional direction of arrival and frequency estimation via hierarchical space–time decomposition

In this paper, we present a fast algorithm for joint estimation of the two-dimensional (2-D) directions of arrival (DOAs) and frequencies of the incoming signals in wireless communications using a hierarchical space–time decomposition (HSTD) technique. Based on the HSTD, the proposed algorithm makes use of a sequence of one-dimensional (1-D) Unitary estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms to estimate these parameters alternatively in a hierarchical tree structure. Also, in between every other 1-D Unitary ESPRIT algorithm, a temporal filtering process or a spatial beamforming process is invoked to partition the incoming signals into finer groups stage by stage to enhance the estimation accuracy as well as to alleviate the contaminated noise. Furthermore, with such a tree-structured estimation scheme, the pairing of these parameters is automatically determined without extra computational overhead. Simulation results show that the new algorithm provides satisfactory performance but with drastically reduced computations compared with previous works.     

Joint Doppler frequency, 2D-DOD and 2D-DOA estimation for bistatic MIMO radar in spatial coloured noise

In this paper, we address the problem of four-dimensional angle and Doppler frequency estimation for L-shaped bistatic multiple input multiple output radar in spatial coloured noise. A novel method of joint estimation of Doppler frequency, two-dimensional direction of departure and two-dimensional direction of arrival based on the propagator method is discussed. Utilising the cross-correlation matrix which is formed by the adjacent outputs of matched filter in the time domain, the special matrix is constructed to eliminate the influence of spatial coloured noise. The proposed algorithm provides lower computational complexity and has very close parameter estimation to the estimation of signal parameters via rotational invariance techniques algorithm and DOA-matrix algorithm in high signal-to-noise ratio and Cramér–Rao bound is given. Furthermore, multidimensional parameters can be automatically paired by this algorithm to avoid the performance degradation resulting from wrong pairing. Numerical simulation results demonstrate the effectiveness of the proposed method.     

Modified ADALINE algorithm for harmonic estimation and selective harmonic elimination in inverters

In digital signal processing, algorithms are very well developed for the estimation of harmonic components. In power electronic applications, an objective like fast response of a system is of primary importance. An effective method for the estimation of instantaneous harmonic components, along with conventional harmonic elimination technique, is presented in this article. The primary function is to eliminate undesirable higher harmonic components from the selected signal (current or voltage) and it requires only the knowledge of the frequency of the component to be eliminated. A signal processing technique using modified ADALINE algorithm has been proposed for harmonic estimation. The proposed method stays effective as it converges to a minimum error and brings out a finer estimation. A conventional control based on pulse width modulation for selective harmonic elimination is used to eliminate harmonic components after its estimation. This method can be applied to a wide range of equipment. The validity of the proposed method to estimate and eliminate voltage harmonics is proved with a dc/ac inverter as a simulation example. Then, the results are compared with existing ADALINE algorithm for illustrating its effectiveness.     

Joint Semi-Blind Channel Estimation and Data Sequence Detection of OFDM Systems

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