基于累计量辨识非最小相位FIR系统的新方法

该文考虑用带有噪声输出数据的累计量实现对非最小相位ＦＩＲ系统的参数辨识问题。提出一个新的基于高阶累计量的方法。其特点如下,（１）灵活性：采用了两个任意阶次相邻的输出累计量;（２）线性：方法的表达式相对于未各量为线性。这不同于其它一些已存在的算法,因而,避免了额外的滞后处理,可提高参数估计的准确性。本文在ＡＲＭＡ高斯噪声及三种实际噪声情况下,做了大量的实验。结果表明,本文提出的算法不仅能有效地完成参     

无需参数配对的近场源距离、方位、频率联合估计算法

 本文提出了一种无需参数配对的近场源距离、方位、频率联合估计算法。通过对构造的阵列输出信号四阶累量矩阵进行一定的变换构造处理得到一个新的矩阵,利用其相应的特征值及特征向量估计出三维参数,无需谱峰搜索,算法适于任意高斯噪声环境。与现有算法相比,本文算法参数估计结果能自动配对,而无需再进行配对过程。最后,计算机仿真结果证实了提出方法的有效性。     

广义复相关熵与相干分布式非圆信号DOA估计

针对相干分布式非圆信号参数估计算法在脉冲噪声环境下性能退化的问题,本文提出了广义复相关熵的概念,并给出了基于广义复相关熵的相干分布式非圆信号DOA（Direction of Arrival）估计方法。该算法首先由分布式信源模型获得入射信号的阵列输出信号,利用信号的非圆特性得到扩展阵列输出信号,再通过扩展阵列输出信号的广义复相关熵矩阵获取信号子空间,避开了传统二阶统计量算法在脉冲噪声下不适应的问题,最后由信号子空间旋转不变特性得到信号的中心波达方向角度。仿真实验结果表明,在Alpha稳定分布噪声条件下,与传统算法相比,本文所提算法具有更好的性能。      

速度传感器阵列近场声源定位的孔径扩展算法

采用速度传感器阵列提出了一种近场声源定位(距离和DOA估计)的新算法.与目前提出的其它算法相比,本文算法有以下几个优点:无需计算高阶累积量,从而有较低的计算量;能够实现参数估计的自动配对;阵元间隔无需限制在1/4波长内,并可以通过增加阵元间隔扩展阵列孔径,从而提高算法的参数估计精度.论文最后给出了仿真实验,验证了算法的定位性能.     

基于高斯加权分数阶傅里叶变换的LFM信号参数估计

针对低占空比下传统算法参数估计性能下降的问题,提出了一种高斯加权分数阶傅里叶变换(GFRFT,Gaussian-weighted fractional Fourier transform)参数估计方法。给出了时限信号GFRFT的定义并推导了其模值平方的特性,研究了高斯白噪声背景下GFRFT的输出信噪比并给出了闭式表达式,进行了仿真实验并讨论说明了该方法的适用条件。仿真结果表明,该方法在低占空比的情况下可以有效地提高参数估计精度。     

近场源频率、波达方向和距离的联合估计方法

提出了一种基于高阶累积量的近场源频率、波达方向(Direction of Arrival,DOA)和距离的联合估计算法.算法采用类似状态空间的方法构造信号输出累积量矩阵,而近场源频率、DOA和距离的估计通过对该输出矩阵的分解处理得到.各近场源参数估计由闭式给出,无需复杂的搜索运算,并且各参数间自动配对,不需要繁琐的配对过程,算法计算复杂度适中.同时,该算法适用于任意加性高斯噪声情形,计算机仿真结果证实了该算法的有效性.     

基于QR-RPCA的双基地MIMO雷达参数估计方法

针对冲击噪声下因接收信号二阶及以上矩不存在而产生性能恶化的问题,提出一种基于QR分解和鲁棒性主成分分析法(QR-RPCA)的双基地多输入多输出(MIMO)雷达参数估计方法。针对RPCA算法适用于实数矩阵处理的情况,先将复数信号转化为实数;然后根据冲击噪声的稀疏特点与目标信号矩阵的低秩特点,利用QR-RPCA算法将低秩信号矩阵从受冲击噪声污染的接收信号中提取出来,并直接得到信号子空间,该算法避免了传统RPCA算法中的大规模奇异值分解,时间复杂度有所降低;最后根据信号子空间并利用旋转不变信号参数估计技术(ESPRIT)对目标方位进行估计。理论与仿真表明,本文算法相较于其他消除冲击噪声的算法,对于低特征指数的冲击噪声具有更好的估计性能。     

基于四阶累积量的近远场源多参数联合估计算法

该文提出了一种新的基于四阶累积量的信源多参数联合估计方法,可实现信源频率、方位及距离的联合估计。该算法无需峰值搜索,适用于任意高斯噪声环境,可有效降低阵列孔径损失。算法中,通过选取特定序号阵元上的输出构造四阶累积量矩阵,有效地避免了因同时存在远场源时而出现的矩阵降秩现象,因而该算法适用于近场、远场或混合信源的参数估计。仿真结果验证了提出方法的有效性。     

基于循环谱的MSK信号参数改进估计算法

利用循环谱理论对最小频移键控MSK信号的载频和码元速率进行参数估计,分析了其循环谱包络特征;在有限数据下,为实现更低信噪比情况下参数估计,提出了一种改进算法,该算法基于数据分段处理和重叠保留,通过在非零循环频率上的一维搜索实现参数估计,能够有效地抑制噪声的影响,避免多维搜索,计算量小;还分析了算法性能,给出了算法实现的具体步骤,并进行了计算机仿真。仿真结果证明了所提出算法的有效性。     

脉冲噪声环境下的自适应时间延迟估计新方法

基于自适应分数低阶协方差(AFLC)的时间延迟估计方法在脉冲噪声环境下具有良好的韧性,但是算法中参数a和b的取值对于算法的估计精度有一定的影响。针对信号噪声的非平稳特性,该文提出一种动态参数估计方法,并在此基础上提出一种不受约束条件限制的修正的自适应分数低阶协方差(M-AFLC)算法。计算机仿真结果表明,递推参数估计方法在平稳和非平稳噪声环境下都能够很好地工作,M-AFLC算法既保留了AFLC算法的全部优点,又避免了AFLC算法在约束条件不满足时的性能退化。     

New approaches without postprocessing to FIR system identificationusing selected order cumulants

In this paper, we address the problem of identifying the parameters of the nonminimum-phase FIR system from the cumulants of noisy output samples. The system is driven by an unobservable, zero-mean, independent and identically distributed (i.i.d) non-Gaussian signal. The measurement noise may be white Gaussian, colored MA, ARMA Gaussian processes, or even real. For this problem, two novel methods are proposed. The methods are designed by using higher order cumulants with the following advantages. (i) Flexibility: method 1 employs two arbitrary adjacent order cumulants of output, whereas method 2 uses three cumulants of output: two cumulants with arbitrary orders and the other one with an order equal to the summation of the two orders minus one. Because of this flexibility, we can select cumulants with appropriate orders to accommodate different applications. (ii) Linearity: both the formulations in method 1 and method 2 are linear with respect to the unknowns, unlike the existing cumulant-based algorithms. The post-processing is thus avoided. Extensive experiments with ARMA Gaussian and three real noises show that the new algorithms, especially algorithm 1, perform the FIR system identification with higher efficiency and better accuracy as compared with the related algorithms in the literature     

基于高阶累积量的空时分组码盲识别算法研究

针对MISO通信系统的空时分组码盲识别问题,提出了一种基于高阶累积量的空时分组码盲识别算法。首先,给出了MISO接收信号模型,利用高阶累积量的性质分析得到接收信号的四阶累积量的表达式;然后,利用编码矩阵的特性,证明接收信号在不同时延向量下的四阶累积量呈现非零值,其非零值取决于STBC的类型;最后,采用四阶累积量的实验值与理论值的最小欧式距离盲识别空时分组码的类型。仿真结果表明,即使在低信噪比条件下,所提方法能够较好地识别空时分组码。     

MA parameter estimation and cumulant enhancement

This paper addresses the problem of estimating the parameters of a moving average (MA) model from either only third- or fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed non-Gaussian sequence that is not observed. The unknown model parameters are obtained using a batch least squares method. Recursive methods are also developed and used to claim the uniqueness of the batch least squares solutions. A novel technique for the enhancement of third-order cumulants of MA processes is introduced. This new technique is based on the concept of composite property mappings and helps reduce the variance of the estimates of third- (or fourth)-order cumulants of MA processes. Simulation results are presented that demonstrate the performance of the new methods and compare them with a range of existing techniques     

The cumulant theory of cyclostationary time-series. I. Foundation

The problem of characterizing the sine-wave components in the output of a polynomial nonlinear system with a cyclostationary random time-series input is investigated. The concept of a pure nth-order sine wave is introduced, and it is shown that pure nth-order sine-wave strengths in the output time-series are given by scaled Fourier coefficients of the polyperiodic temporal cumulant of the input time-series. The higher order moments and cumulants of narrowband spectral components of time-series are defined and then idealized to the case of infinitesimal bandwidth. Such spectral moments and cumulants are shown to be characterized by the Fourier transforms of the temporal moments and cumulants of the time-series. It is established that the temporal and spectral cumulants have certain mathematical and practical advantages over their moment counterparts. To put the contributions of the paper in perspective, a uniquely comprehensive historical survey that traces the development of the ideas of temporal and spectral cumulants from their inception is provided     

Cumulant based identification approaches for nonminimum phase FIRsystems

Recursive and least squares methods for identification of non-minimum-phase linear time-invariant (NMP-LTI) FIR systems are developed. The methods utilize the second- and third-order cumulants of the output of the FIR system whose input is an independent, identically distributed (i.i.d.) non-Gaussian process. Since knowledge of the system order is of utmost importance to many system identification algorithms, new procedures for determining the order of an FIR system using only the output cumulants are also presented. To illustrate the effectiveness of the methods, various simulation examples are presented     

Blind identification of MISO-FIR channels

In this paper, we address the problem of determining the order of MISO channels by means of a series of hypothesis tests based on scalar statistics. Using estimated 4th-order output cumulants, we exploit the sensitiveness of a Chi-square test statistic to the non-Gaussianity of a stochastic process. This property enables us to detect the order of a linear finite impulse response (FIR) channel. Our approach leads to a new channel order detection method and we provide a performance analysis along with a criterion to establish a decision threshold, according to a desired level of tolerance to false alarm. Afterwards, we introduce the concept of MISO channel nested detectors based on a deflation-type procedure using the 4th-order output cumulants. The nested detector is combined with an estimation algorithm to select the order and estimate the parameters associated with different transmitters composing the MISO channel. By treating successively shorter and shorter channels, it is also possible to determine the number of sources.     

A Least-Squares-Based Algorithm for Identification of Non-Gaussian ARMA Models

A new recursive method for estimating the parameters of autoregressive moving average (ARMA) models is presented in this paper. The recursive linear identification method is developed using higher-order statistics of the observed output data and is based on a least-squares solution. Namely, a matrix consisting of third-order statistics (or cumulants) of the observed output data is constructed so that it almost possesses a full rank structure. The signal is embedded in a Gaussian noise that may be colored. The system is driven by a zero-mean independent and identically distributed non-Gaussian process. The excitation signal is unobserved. Simulation results are given to illustrate the performance of the proposed algorithm with respect to existing well-known methods.     

Extending HOC-based methods for identifying the diagonal parameters of quadratic systems

In this paper, methods developed for the linear case of identifying the diagonal parameters of quadratic systems are extended to nonlinear case. Firstly, nonlinear relationships between model kernels and output cumulants are presented. Secondly, the relationship linking output cumulants and the coefficients of systems presented in the linear case, is extended to the general case of nonlinear quadratic systems identification. According to this concept, two nonlinear approaches are developed, the first use the fourth-order cumulants, and the second combined the third- and fourth-order cumulants. The numerical simulation results, for various signal to noise ratio (SNR) and 200 Monte Carlo runs, show that the proposed approaches achieve better accuracy, as compared with the related algorithm in the literature. Furthermore, the second algorithm is more precise in high noise environment (smallest \(\mathrm{SNR}=0\) dB), but the first algorithm more efficient in the weak noise environment case (highest SNR \(\ge \) 8 dB) comparing to using others methods.     

Cumulants: A powerful tool in signal processing

The impulse response of a linear, time-invariant system is related in a simple closed-form solution to the output cumulants, when the input is assumed to be non-Gaussian and independent. This expression permits the use of one-dimensional processing of the output cumulants for identification of non-minimum-phase systems, and opens new directions in other signal processing applications.     

基于高阶累积量实现数字调相信号调制识别

给出了基于高阶累积量进行多进制数字相位调制(MPSK)信号调制识别的特征值构造方法,对该特征值抑制多径干扰能力进行了理论分析,证明累积量阶数越高抑制多径干扰的性能越好。推导出基于6阶累积量进行MPSK信号调制识别的特征值公式,并给出具体分类方法,对4阶和6阶累积量调制识别性能进行了分析比较。计算机仿真试验证明:多径信道时,6阶累积量分类特征值优于4阶;当符号信噪比低于0dB时,4阶累积量分类特征值优于6阶。