基于分数阶傅里叶变换的双基地雷达线性调频信号的参数联合估计新方法

该文提出一种双基地MIMO雷达线性调频(LFM)信号参数的联合估计新方法。在所提出的新的双基地MIMO雷达的信号模型基础上,利用分数阶傅里叶变换对线性调频(LFM)信号的能量聚集特性进行提取,根据分数阶傅里叶变换域内的峰值点对多普勒频移尺度和时延进行估计,并采用FRFT-MUSIC算法实现了线性调频(LFM)信号收发角的联合估计,实现了收发角的自动配对。仿真实验验证了算法的有效性。     

脉冲噪声环境下基于宽带模糊函数的双基地MIMO雷达目标参数估计新方法

以Alpha稳定分布作为噪声模型,研究了脉冲噪声环境下宽带双基地MIMO雷达系统中参数估计问题.针对在脉冲噪声环境中,基于传统的信号模型和算法效果显著退化的问题,本文提出了基于分数低阶统计量的宽带模糊函数算法.首先根据分数低阶宽带模糊函数的峰值点实现对多普勒频率尺度因子和时延的联合估计.接下来基于分数低阶宽带模糊函数构造两个子阵.通过采用改进的MUSIC算法和ESPRIT算法实现了收发角的联合估计.仿真实验表明本文算法具有很好的性能.     

一种改进的AR模型反辐射导弹检测算法

反辐射导弹(ARM)具有多普勒频率时变特征,其回波信号的传统功率谱为一宽带频谱,不利于对其进行检测.针对这种情况,提出一种改进的AR模型检测算法,该算法将分数阶相关理论引入到AR模型功率谱估计中,把传统的功率谱转变为分数阶谱,使ARM回波信号能量得到聚集,从而实现ARM的有效检测.仿真结果表明,该方法能够在ARM信噪比低达-16 dB的情况下,准确将ARM检测出来,实现实时告警.     

基于FRFT的双基地MIMO雷达多普勒频率和收发角联合估计新方法

提出了一种新的双基地MIMO雷达系统中参数联合估计方法.首先提出了一个新的双基地MIMO雷达阵列信号模型,利用分数阶傅里叶变换能量聚集特性对多普勒频率的初始频率和调频率进行联合估计.然后,在分数阶傅里叶变换域内构造2个子阵,采用FRFT-MUSIC算法和FRFT-ESPRIT算法实现了收发角的联合估计.仿真实验表明,本算法在低信噪比时同样具有较好的性能.     

用FrFT插值实现LFM信号的参数估计

分数阶傅立叶域的分辨率由信号时宽带宽积决定,有限的分辨率影响了利用FrFT(分数阶傅立叶变换)估计LFM(线性调频)信号参数的精度.针对此问题,提出了一种利用FrFT插值的LFM信号参数估计方法.在分析引起峰值点偏差的因素后,得到FrFT在峰值点的分数阶傅立叶域的函数表达式,基于此,在分数阶傅立叶域进行插值计算,突破了原有分辨率的限制,提高了参数估计的精度.仿真实验结果验证了算法的有效性.     

改进的一维时变海面模型及其分数阶功率谱研究

该文针对经典双尺度海面模型不能有效分析高海况和低掠射角条件下的非Bragg散射问题,在模型中引入调频率描述多普勒频率的变化,提出了一种更符合工程实际的1维时变海面散射模型。然后,通过计算改进模型的海面平均散射功率,得到了时变海面的角度散射特性。其次,研究了改进模型的分数阶功率谱(FPS)特性,得出在分数阶Fourier变换(FRFT)域,海面散射信号的功率谱由多分量冲激信号组成,其在FRFT域的位置可用于估计模型的频率参数。最后,采用X波段实测海面回波数据对改进模型进行验证,并讨论了入射波长和变换角对FPS的影响,仿真结果表明该模型适合分析和提取时变海面回波的频率变化及多普勒频移。     

一种LFMCW雷达距离速度去耦合方法

宽带线性调频连续波雷达应用非常广泛,但目标运动会带来距离与速度耦合的问题从而使距离谱产生模糊。文中在分析目标回波信号基础上,提出了一种基于Radon-Ambiguity变换与分数阶傅里叶变换的距离速度去耦合新方法。该方法利用Radon-Ambiguity变换和分数阶傅里叶变换分别估计回波信号的调频率和估计初始频率,最终实现距离速度去耦合。仿真结果验证了该算法的有效性,算法同时具有速度快、精度高等优点。     

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。     

基于RELAX的气象雷达回波参数化谱矩估计方法

气象雷达回波的功率、平均频率和谱宽等谱矩参数与气象目标的类型密切相关,如何准确估计一直是关注的问题。地杂波也是气象雷达面临的严重干扰。气象目标回波和地杂波的频谱呈现高斯谱的特征,其参数化模型可以利用来估计这些谱矩参数。提出了拟合信号功率谱的参数化谱矩估计方法,并采用RELAX的思想进行多个高斯谱信号混合时的谱矩参数估计。最后利用仿真气象雷达信号进行了实验和性能分析,实验结果表明提出的方法能准确估计出高斯功率谱的谱矩参数,且具有较高的分辨率。与脉冲对法相比可用于估计多个高斯谱混合的情况（比如地杂波和气象回波叠加在一起时）,与最大似然法相比降低了运算量。      

LFM信号参数估计的FRFT-HOC方法

引入高阶累积量的概念对不同阶次FRFT对LFM信号的聚焦能力进行定量描述,从而构造了LFM信号的FRFT-HOC特征谱,以此为基数分别结合步进式搜索和粒子群优化算法提出两种峰值点精搜方法,并根据峰值点坐标与LFM信号之间的定量关系实现了LFM参数估计。由于算法只涉及到一维参数搜索且定义域区间宽度可控,因此运算量大大降低。论文比较了四种FRFT-HOC特征的峰值区分能力,分析了FRFT阶次估计精度对LFM信号参数估计的影响,最后通过仿真实验对改进算法的LFM参数估计性能进行了验证。      

Parameter Estimation Based on Fractional Power Spectrum Density in Bistatic MIMO Radar System Under Impulsive Noise Environment

This paper takes an Alpha-stable distribution as the noise model to solve the parameter estimation problem of bistatic multiple-input multiple-output (MIMO) radar system in the impulsive noise environment. For a moving target, its echo often contains a time-varying Doppler frequency. Furthermore, the echo signal may be corrupted by a non-Gaussian noise. It causes the conventional algorithms and signal models degenerating severely in this case. Thus, this paper proposes a new signal model and a novel method for parameter estimation in bistatic MIMO radar system in the impulsive noise environment. It combines the fractional lower-order statistics (FLOS) and fractional power spectrum density (FPSD), for suppressing the impulse noise and estimating parameters of the target in fractional Fourier transform domain. Firstly, a new signal array model is constructed based on the \(\alpha \)-stable distribution model. Secondly, Doppler parameters are jointly estimated by peak searching of the FLOS–FPSD. Furthermore, two modified algorithms are proposed for the estimation of direction-of-departure and direction-of-arrival (DOA), including the fractional power spectrum density based on MUSIC algorithm (FLOS–FPSD–MUSIC) and the fractional lower-order ambiguity function based on ESPRIT algorithm (FLOS–FPSD–ESPRIT). Simulation results are presented to verity the effectiveness of the proposed method.     

Parameter estimation based on fractional power spectrum under alpha-stable distribution noise environment in wideband bistatic MIMO radar system

This paper takes the alpha-stable distribution as the noise model and works on the parameter estimation problem of wideband bistatic Multiple-Input Multiple-Output (MIMO) radar system in the impulsive noise environment. In many applications, it is not appropriate to approximate the wideband signal by the narrowband model. Furthermore, the echo signal may be corrupted by the non-Gaussian noise. The conventional algorithms degenerate severely in the impulsive noise environment. Thus, this paper proposes a new wideband signal model and a novel method in wideband bistatic MIMO radar system. It combines the fractional lower order statistics and fractional power spectrum, for suppressing the impulse noise and estimating parameters of the target. Firstly, a new signal array model is proposed under the alpha-stable distribution noise model. Secondly, Doppler stretch and time delay are jointly estimated by peak searching of the FLOS-FPSD. Furthermore, two modified algorithms are proposed for the estimation of the direction-of-departure and direction-of-arrival, including the fractional power spectrum density based on MUSIC algorithm (FLOS-FPSD-MUSIC) and the fractional lower-order ambiguity function based on ESPRIT algorithm (FLOS-FPSD-ESPRIT). Simulation results are presented to verity the effectiveness of the proposed method.     

基于EM算法的SAI图像插值改进技术

提出了一种基于期望最大（Expectation Maximization,EM）算法对自适应二维自回归建模与软判决估计联合插值（SAI）算法进行改进的算法,它将插入像素的估计值反馈回分段二维自回归模型（PAR）参数的估计过程中,修正估计出的模型参数。实验结果显示：该算法能产生良好的视觉效果及较高的峰值信噪比（PSNR）。     

空间任意阵的多宽带信源定位算法性能研究

针对宽带信号非平稳特性,以及常用宽带DOA估计算法要求信源个数是已知的情况,提出一种新的基于空间任意阵的非平稳信号DOA估计算法。首先,通过短时傅里叶变换将信号转为频域表示,然后构造阵列频域数据模型,最后利用短时功率谱矩阵的联合对角化特性实现宽带信号的波达方向估计。本文对提出的算法进行了理论分析,并在常见阵型（如线阵、十字阵）上进行了仿真和性能分析,仿真结果表明该算法可高分辨率地估计出DOA,并且估计误差小。相对于已有的MUSIC谱估计方法,该算法无需进行信源个数估计,更具实用性。      

Distributed Cooperative Wideband Spectrum Sensing Algorithm Based on Subspace Methods

We present a novel distributed cooperative spectrum sensing algorithm from compressive sampling in wideband cognitive radio (CR) networks. Each CR utilizes compressive sampling to reduce data acquisition costs. A subspace method is then adopted to directly detect occupied channels without reconstructing the sparse spectrum. To obtain the spatial diversity gain, global signal subspace is estimated by the distributed projection approximation subspace tracking (DPAST) algorithm in which the CRs exchange information locally and cooperate without the need for a fusion center. Then, the orthogonality property of the signal subspace and noise subspace can be exploited to find spectral support to complete the spectrum sensing. We study the convergence behavior of the DPAST algorithm and evaluate the performance of spectrum sensing. Simulation results indicate that the DPAST can effectively estimate the global signal subspace, and the proposed compressed wideband spectrum sensing scheme performs better than spectrum sensing at a single CR.     

基于 Gerschgorin 圆盘理论的认知无线电宽带频谱感知

摘 要：提出了基于Gerschgorin圆盘理论的宽带频谱感知算法：Gerschgorin似然估计算法和Gerschgorin圆盘半径迭代算法。通过在宽带频谱感知中引入Gerschgorin圆盘理论,将认知无线电用户频谱观测数据中噪声圆盘空间和信号圆盘空间进行分离,并基于对主用户所占用子频段集合势的估计,实现对宽带授权频谱中多个子频段状态的监测。为了进一步提高感知性能,还提出利用宽带频谱中主用户信号占用子频段的连续性特性改善算法性能。理论推导和仿真结果表明,在信噪比较小时,Gerschgorin似然估计算法较基于信息论准则的宽带感知算法具有更稳定的检测性能;Gerschgorin圆盘半径迭代算法与传统能量检测方法相比,优势在于不依赖任何噪声功率先验信息,且在采样次数较少情况下的感知错误率较小。因此,基于Gerschgorin圆盘理论的频谱感知更适合于实际CR系统,可为宽带频谱感知提供行之有效的算法实施方案。     

Spectrum estimation of short-time stationary signals in additivenoise and channel distortion

In this work, spectrum estimation of a short-time stationary signal that is degraded by both channel distortion and additive noise is addressed. A maximum likelihood estimation (MLE) algorithm is developed to jointly identify the degradation system and estimate short-time signal spectra. The source signal is assumed to be generated by a hidden Markov model (HMM) with state-dependent short-time spectral distributions described by mixtures of Gaussian densities. The distortion channel is linear time-invariant, and the noise is Gaussian. The algorithm is derived by using the principle of expectation-maximization (EM), where the unknown parameters of channel and noise are estimated iteratively, and the short-time signal power spectra are obtained from the posterior sufficient statistics of the source signal. Other spectral representation parameters, such as autoregressive model parameters or cepstral parameters, are obtained by minimum mean-squared error (MMSE) estimation from the power spectral estimates. The estimation algorithm was evaluated on simulated signals at the signal-to-noise ratios (SNRs) of 20 dB down to 0 dB, where it produced convergent estimation and significantly reduced spectral distortion     

Wideband LFM Interference Suppression Based on Fractional Fourier Transform and Projection Techniques

A novel wideband LFM interference suppression method is proposed based on fractional Fourier transform (FRFT) and projection techniques. By using the concentration property of LFM interferences in fractional Fourier domain, the initial frequency, frequency modulation rate and direction-of-arrival (DOA) of LFM interferences are first estimated. Afterwards, the space–time interferences subspace is constructed. In order to suppress wideband LFM interferences, the received signal is projected onto the subspace orthogonal to the interferences subspace when the signals are uncorrelated. Furthermore, considering the scenario when the signals are coherent, according to the properties of oblique projection, LFM interferences are suppressed by projecting the received signal onto the joint signal–noise subspace. Then, the desired signal is extracted by beamforming. Unlike the existing algorithms, the proposed method not only can exactly estimate parameters and directly solve the DOA of LFM interferences but also can construct the interferences subspace with the time-variant steering vector of interferences. Moreover, our method can effectively suppress LFM interferences without reducing the freedom, and significantly improve the performance, even at low signal-to-noise ratio (SNR) or small number of snapshots. Simulation results demonstrate the effectiveness of the proposed method.