基于改进精估计与FrFT的Chirp信号参数估计

为了解决Chirp信号参数估计在低信噪比下保持高精度等问题,提出了一种基于粗精二次估计的Chirp参数算法。粗估计采用分数阶傅里叶变换（Fractional Fourier Transform,FrFT）,能够在低信噪比下检测到Chirp信号并估计出调频率的范围。同时,提出了一种FrFT插值算法提高FrFT估计中心频率的精度。精估计根据信号能量和周期长度一定时Chirp信号频谱幅度的平方与调频率成反比的特性,不断地用已知Chirp信号的共轭与未知Chirp信号相乘,寻找使相乘后频谱幅度平方最大的已知Chirp信号的调频率。为了提高精估计算法性能,用提出的H-Rife算法估计中心频率和相乘后信号的频谱中最大的幅值。〖JP2〗用二分法代替等步长使精估计在粗估计出的调频率范围内搜索,极大降低了复杂度。在信噪比不小于-10 dB时,该算法估计调频率归一化均方误差（Normalized Mean Square Error,NMSE）较FrFT线性提升,估计中心频率NMSE较FrFT线性提升,接近克拉美罗界。     

信号的二维到达角和极化估计

本文论述了一种基于子空间方法的多信号二维到达角和极化参量的估计算法。该方法采用了交叉偶极子阵元组成的L型阵列,利用子阵输出信号数据矩阵中包含的信号空间的旋转不变性质,借助于矩阵束方法求解出信号的二维到达角和极化参量的估汁值,并自动进行参数的配对。仿真结果证实了该算法的有效性。     

信号的二维到达角和极化估计

本文论述了一种基于子空间方法的多信号二维到达角和极化参量的估计算法。该方法采用了交叉偶极子阵元组成的Ｌ型阵列，利用子阵输出信号数据矩阵中包含的信号空间的旋转不变性质，借助于矩阵方法求解出信号的二维到达角和极化参量的估计值，并自动进行参数的配对，仿真结果证实了该算法的有效性。     

自适应频率和二维波达方向角跟踪估计算法

运用特征子空间分析方法的关键问题在于信号或噪声子空间的估计,在实际中有些信号的统计特性通常是随时间变化的,这时需要随时根据新的阵列接收数据对信号或噪声子空间进行更新,以得到参数的实时估计值,在该文中建立了多维信号参量联合估计的3D Unitary ESPRIT算法,然后提出了基于球面平均 ULV分解的子空间跟踪算法,将子空间跟踪算法与多维信号多量联合估计算法相结合,得到多维时变信号参数的跟踪估计算法,仿真计算结果验证了该算法的有效性。     

线性调频信号时/频差快速联合估计方法

提出了一种线性调频(Chirp)信号时/频差估计算法。首先估计Chirp信号互模糊函数中脊线的位置,再通过频率补偿使脊线通过原点,进而通过搜索信号在分数阶傅里叶变换域上的相关峰来代替沿脊线搜索模糊函数峰值的过程,最终获得时/频差的估计。该算法由于采用一维搜索,并且可用快速傅里叶变换实现,因此所需运算量显著降低。对于多分量Chirp信号,根据脊线位置的不同,算法能够分别估计出各分量信号的时/频差。仿真实验表明,该算法能够精确估计Chirp信号的时/频差,并且随着信噪比的提高,时/频差估计值的均方根误差逐渐接近克拉美罗下界。      

基于时频子空间分解的宽带线性调频信号DOA估计

针对具有时变方向向量的宽带线性调频信号,该文建立了基于短时Wigner-Ville分布(WVD)的空间时频分布矩阵,通过对各个空间时频矩阵的特征分解获得对应的信号子空间和噪声子空间,给出了基于时频子空间投影实现多个时频点综合估计信号DOA的算法。利用空间时频分布的前后向平滑解决了具有相同时频特性信号的均匀线阵DOA估计问题。算法不需要聚汇和插值等复杂的矩阵变换,精度较高,计算简便.仿真实验显示该算法性能显著优越于基于矩阵插值的宽带调频信号DOA估计算法.     

基于空时频协方差矩阵重构的高效跳频信号DOA估计

为了充分利用跳频信号的空域信息来进行信号的DOA估计,在信号空时频分析的基础上,本文提出了一种基于协方差矩阵重构的高效跳频信号DOA估计方法。首先将接收信号的均匀线阵（uniform linear array, ULA）平均划分成2个子阵,分别对每个子阵接收到的信号进行时频分析,在时频域选择有效跳,构造每跳的空时频矩阵（spatial time-frequency distribution, STFD）,然后求得2个子阵的互协方差矩阵。将2个子阵的互协方差矩阵进行重构运算得到等效的信号子空间,最后构造空间谱多项式求根估计出信号的DOA。仿真结果表明该方法相比于以往改进类子空间算法能够有效提高估计精度和降低算法复杂度。      

多跳频信号波达方向与极化状态联合估计算法

为了有效辅助跳频(FH)网台分选和信号识别、跟踪,该文用正交偶极子对构造极化敏感阵列,基于空间极化时频分析,在欠定条件下实现了多跳频信号波达方向(Direction Of Arrival, DOA)与极化状态的高效联合估计。首先建立跳频信号的极化敏感阵列观察模型,然后根据参考阵元时频分析结果建立各跳信号的空间极化时频分布矩阵,再利用该矩阵中蕴含的信号极化-空域特征信息分别运用线性、二次型空间极化时频以及多项式求根共3种方法实现DOA与极化参数联合估计,最后蒙特卡罗仿真结果验证了该算法的有效性。     

基于双通道DFRFT互谱法的Chirp信号时延估计

针对脉冲Chirp类信号的时延估计问题,理论推导了基于离散分数阶Fourier变换的脉冲Chirp信号的特性,分析了当时延参量等效的分数阶Fourier域的频率大于采样率时,脉冲Chirp信号的分数阶Fourier域谱产生混叠,造成时延估计模糊的问题,并提出基于离散分数阶Fourier变换（DFRFT）双通道互谱法进行时延估计,给出两个通道采样率选取的原则及算法的性能分析,实验结果表明,在一定的采样率下,算法能够快速精确地估计脉冲Chirp信号的时延参数.     

利用空间时频分布实现宽带FM信号2-D到达角估计

提出了一种新的宽带FM信号二维到达角估计方法。本算法将L型天一阵两个臂的空间时频分布矩阵分别进行相干信号子空间平滑处理，并用子窨 方法实现方位和俯仰角估计，计算机仿真证实了算法的有效性。     

Data-adaptive evolutionary spectral estimation

We present a novel data-adaptive estimator for the evolutionary spectrum of nonstationary signals. We model the signal at a frequency of interest as a sinusoid with a time-varying amplitude, which is accurately represented by an orthonormal basis expansion. We then compute a minimum mean-squared error estimate of this amplitude and use it to estimate the time-varying spectrum at that frequency, all while minimizing the interference from the signal components at other frequencies. Repeating the process over all frequencies, we obtain a power distribution that is consistent with the Wold-Cramer evolutionary spectrum and reduces to Capon's (1969) method for the stationary case. Our estimator possesses desirable properties in terms of time-frequency resolution and positivity and is robust in the spectral estimation of noisy nonstationary data. We also propose a new estimator for the autocorrelation of nonstationary signals. This autocorrelation estimate is needed in the data-adaptive spectral estimation. We illustrate the performance of our estimator using simulation examples and compare it with the recently presented evolutionary periodogram and the bilinear time-frequency distribution with exponential kernels     

Evolutionary periodogram for nonstationary signals

Presents a novel estimator for the time-dependent spectrum of a nonstationary signal. By modeling the signal, at any given frequency, as having a time-varying amplitude accurately represented by an orthonormal basis expansion, the authors are able to compute a minimum mean-squared error estimate of this time-varying amplitude. Repeating the process over all frequencies, they obtain a power distribution as a function of time and frequency that is consistent with the Wold-Cramer evolutionary spectrum. Based on the model assumptions, the authors develop the evolutionary periodogram (EP) for nonstationary signals, an estimator analogous to the periodogram used in the stationary case. They also derive the time-frequency resolution of the new estimator. The approach is free of some of the drawbacks of the bilinear distributions and of the short-time Fourier transform spectral estimates. It is guaranteed to produce nonnegative spectra without the cross-term behavior of the bilinear distributions, and it does not require windowing of data in the time domain. Examples illustrating the new estimator are given     

Spatial evolutionary spectrum for DOA estimation and blind signalseparation

We combine the concepts of evolutionary spectrum and array processing. We present a cross-power stationary periodogram for both direction-of-arrival (DOA) estimation and blind separation of nonstationary signals. We model the nonstationary signals received by each array sensor as a sum of complex sinusoids with time-varying amplitudes. These magnitudes carry information about the DOA that may also be time-varying. We first estimate the time-varying amplitudes using estimators obtained by minimizing the mean-squared error. Then using the estimated time-varying amplitudes, we estimate the evolutionary cross-power distributions of the sensor. Next, using cross-power estimates at time-frequency points interest, we estimate the DOAs using one of the existing methods. If the directions are time varying, we choose time-frequency points around the time of interest to estimate spontaneous source locations. If the sources are stationary, time-frequency points of interest can be combined for the estimation of fixed directions. Whitening and subspace methods used to find the mixing matrix and separate nonstationary signals received by the array. We present examples illustrating the performance of the proposed algorithms     

基于时域聚汇和补偿估计WLFM信号DOA的FRFT-MUSIC算法

本文提出了分数阶付氏变换(FRFT)-MUSIC算法估计多个宽带信号到达角参数(DOA)。该算法通过对到达角的初始估计,在时域内经补偿和聚汇将宽带线性调频信号的时变方向矩阵变换为固定的方向矩阵,然后利用分数阶付氏变换和MUSIC算法实现入射信号DOA估计。本文同时提出了一种对FRFT-MUSIC的组合估计方法以减少计算量。仿真实验证明了算法的有效性。     

方向依赖的阵元复增益条件下宽带chirp信号二维角估计

在方向依赖的阵元复增益条件下,该文提出一种宽带chirp信号二维角估计新方法。该方法基于信号时变阵列流形结构分析,构造出一种阵列流形时频变换,无需估计和补偿即可消除阵元幅相不一致误差的影响,实现宽带chirp信号二维角闭式解析估计。仿真实验表明了新方法的有效性。     

基于FRFT的时变幅度Chirp信号的参数估计

分析了时变幅度线性调频信号参数估计的一般方法，提出了将分数阶傅里叶变换用于时变幅度线性调频信号的参数估计，并对相关问题进行了较为深入的研究。研究了时变幅度线性调频信号初始相位，初始角频率、调频率及幅度信息提取与估计的方法，并以幅度随高斯函数而变化以及幅度随机变化的线性调频信号为对象对参数估计性能（参数估计的均方误差）作了传真分析。     

Difference equation representation of chirp signals andinstantaneous frequency/amplitude estimation

We present a time-varying coefficient difference equation representation for sinusoidal signals with time-varying amplitudes and frequencies. We first obtain a recursive equation for a single chirp signal. Then, using this result, we obtain time-varying coefficient difference equation representations for signals composed of multiple chirp signals. We analyze these equations using the skew-shift operators. We show that the phases of the poles of the difference equations produce instantaneous frequencies (IF), and the magnitudes are proportional to the ratio of successive values of the instantaneous amplitudes (IA). Then algorithms are presented for the estimation of instantaneous frequencies and instantaneous amplitudes for multicomponent signals composed of chirps using the difference equation representation. The first algorithm we propose is based on the skew-shift operators. Next we derive the conditions under which we can use the so-called frozen-time approach. We propose an algorithm for IF and IA estimation based on the frozen-time approach. Then we propose an automatic signal separation method. Finally, we apply the proposed algorithms to single and multicomponent signals and compare the results with some existing methods     

时频干涉仪到达角估计性能分析

传统干涉仪测向是对单个脉冲信号测向的，对于多信号没有分辨能力，对于线性调频等时变频率信号也不能直接应用。本文提出了一种时频干涉仪算法以实现对宽带线性调频信号的到达角（DOA）估计；同时该算法可实现多信号分辨；讨论了通道误差对算法性能的影响；分析表明，通道增益不一致不会造成DOA估计错误，而通道时延的不一致将造成DOA估计错误；给出了通道时延误差校正算法，通过校正可实现DOA的正确估计；计算机仿真结果证实了分析的正确性。     

Evolutionary Maximum Entropy Spectral Estimation and Heart Rate Variability Analysis

Spectral analysis has been used extensively in heart rate variability (HRV) studies. The spectral content of HRV signals is useful in assessing the status of the autonomic nervous system. Although most of the HRV studies assume stationarity, the statistics of HRV signals change with time due to transients caused by physiological phenomena. Therefore, the use of time-frequency analysis to estimate the time-dependent spectrum of these non-stationary signals is of great importance. Recently, the spectrogram, the Wigner distribution, and the evolutionary periodogram have been used to analyze HRV signals. In this paper, we propose the application of the evolutionary maximum entropy (EME) spectral analysis to HRV signals. The EME spectral analysis is based on the maximum entropy method for stationary processes and the evolutionary spectral theory. It consists in finding an EME spectrum that matches the Fourier coefficients of the evolutionary spectrum. The spectral parameters are efficiently calculated by means of the Levinson algorithm. The EME spectral estimator provides very good time-frequency resolution, sidelobe reduction and parametric modeling of the evolutionary spectrum. With the help of real HRV signals we show the superior performance of the EME over the earlier methods.     

应用WVD估计AM-FM信号的瞬时频率

该文研究了应用WVD谱峰检测估计AM-FM信号的瞬时频率的方法及其性能。理论分析表明;对线性调频的AM-FM信号,只要其幅度的WVD在频率为零处取得最大值在任意时刻都成立,则基于WVD谱峰检测得到的瞬时频率估计是无偏的,并给出了估计的方差。仿真实验使用高斯包络的线性调频信号表明,利用WVD可以有效地估计AM-FM信号的瞬时频率。