非高斯杂波中反辐射导弹的Wald检测方法

Alpha稳定分布是一种有广泛适用范围的非高斯分布模型,本文基于Alpha稳定分布杂波假设,提出了一种反辐射导弹(ARM)的Wald检测方法.当Alpha稳定分布的特征参数α值较小时,基于功率谱或基于共变α谱的频率估计性能将明显下降,为此本文提出基于分数低阶协方差(FLOC)谱方法估计ARM载机信号的多普勒频率,并针对载机信号的强相关性用对消器抑制载机信号,最后导出了标准对称Alpha稳定分布杂波下ARM的Wald检测统计量.仿真结果表明,基于FLOC谱能够准确地估计信号频率,抑制载机信号;基于Alpha稳定分布杂波假设的Wald检测性能与广义似然比检测(GLRT)的渐近性能相当,且明显优于基于高斯分布杂波假设的Wald检测性能.     

高斯混合噪声中弱信号的Rao检测方法

期望最大化(Expectation Maximization,EM)算法是求解参数最大似然估计(MLE)的最优迭代算法,但若参数初始化不恰当,会使估计值落入"初值陷阱",导致错误的参数估计值.为此,本文提出了估计高斯混合噪声参数的矩 - EM算法,即先求参数的矩估计,并用矩估计值初始化参数,再通过EM迭代算法估计参数.在此基础上,经高斯化滤波,导出了高斯混合噪声背景下未知幅度弱信号的Rao检验统计量.仿真结果表明,矩 - EM算法可以更准确地估计噪声参数;基于矩 - EM算法的Rao检测性能优于基于EM算法的Rao检测性能.     

复合高斯杂波中距离扩展目标的参数化Wald检测

本文研究复合高斯杂波环境中的距离扩展目标的自适应检测问题。有色杂波采用参数未知的自回归（AR）过程描述。结合Wald检测准则,仅需对H1假设条件下的未知参数进行最大似然估计,给出了一种新的基于参数化模型的扩展目标检测器——参数化Wald检测器。该检测器的检验统计量可解释为首先针对各个待测单元分别计算检验统计量,然后将所有待测单元的输出进行非相参累加,其对杂波的随机功率起伏具有恒虚警率(CFAR)特性。相比于常规的基于协方差矩阵的检测方法,参数化检测算法的执行过程不需要依赖辅助数据,仅利用待测扩展目标数据即可实现自适应处理,有效缓解了训练压力并降低了计算量。仿真实验表明,所提出的参数化Wald检测器的检测性能优于之前提出的参数化广义似然比检测器的性能。      

非均匀非高斯杂波的协方差矩阵估计

在雷达目标检测中,杂波的协方差矩阵估计利用了待检测单元附近的杂波数据。本文考虑一种非均匀环境中,非高斯杂波下的杂波协方差矩阵估计问题,即假定待检测单元与参考单元的杂波协方差矩阵之间满足某种统计关系,并假定杂波数据满足复合高斯统计分布模型。在这种场景下,常规的杂波协方差矩阵估计方法会导致信号检测性能的下降。采用共轭先验分布作为非均匀非高斯场景的统计分布模型,利用贝叶斯方法,本文给出了基于Gibbs抽样的杂波协方差最小均方误差估计方法。计算机仿真结果表明,与常规的杂波协方差矩阵估计方法相比较,本文所给出的杂波协方差矩阵的估计算法能够在参考数据较少,累积脉冲个数较少时,非均匀场景中获得较好的检测性能。本文还分析了先验分布模型参数误差对检测性能的影响。      

基于自适应GMM杂波估计的改进MHT算法

在传统多假设跟踪(MHT)算法中通常会假设杂波强度先验已知,当观测场景中杂波未知且空变时,该假设将会导致跟踪算法性能急剧下降。针对这一问题,本文提出一种基于自适应高斯混合模型(GMM)在线估计未知杂波的改进MHT算法。首先利用自适应GMM拟合未知杂波空间分布,并自适应地估计出波门内的杂波强度;然后将其应用于MHT处理中,有效改善航迹得分计算和最优假设航迹估计的准确性,进而实现在杂波未知场景中的稳定跟踪。仿真结果表明,在未知杂波观测场景中,所提算法相比传统MHT算法和MHT-GMM算法获得了更好的数据关联准确性和航迹维持性能。     

未知杂波下多目标跟踪AEM-PHD平滑滤波算法

针对未知杂波强度下的多目标跟踪问题,提出了加速期望最大化概率假设密度(AEM-PHD)平滑滤波算法。首先,对杂波的强度进行建模;接着,根据杂波的量测估计出杂波的个数;然后,利用高斯有限混合模型对杂波密度函数进行建模,在EM算法的基础上提出了AEM算法,将AEM算法用于高斯有限混合模型参数的估计,获得了杂波的密度函数;最后,将估计的杂波信息应用于多目标跟踪,对目标状态进行了平滑。仿真结果表明,在杂波强度未知的环境下,所提算法能准确估计出杂波的参数,具有跟踪精度高、目标数目估计准确的优点。     

重拖尾海杂波下基于MCSEAPHD目标跟踪算法

针对重拖尾海杂波下的对海探测问题，提出了一种基于多帧杂波稀疏度估计和幅值辅助概率假设密度的海面目标跟踪算法。首先，提出重拖尾海杂波下的多帧杂波稀疏度估计技术，利用高斯混合后验强度和多帧杂波累积剔除源自幸存目标和新生目标的测量，实时估计空间分布未知且时变的重拖尾海杂波密度；然后，将杂波密度估计参数传递到跟踪器中，并建立带幅值的量测似然函数，在概率假设密度算法框架下进行状态更新，并将更新后的高斯混合后验强度用于下一帧的杂波密度与目标状态估计；最后，进行了仿真试验，仿真结果验证了所提算法的有效性。     

基于LGJMS-GMPHDF的多机动目标联合检测、跟踪与分类算法

线性高斯跳变马尔可夫系统模型下的高斯混合概率假设密度滤波器(LGJMS-GMPHDF)为杂波背景下多机动目标跟踪提供了一种有效方法。该文将类别辅助信息引入LGJMS-GMPHDF,提出了一种密集杂波背景下多机动目标联合检测、跟踪与分类算法。该算法在LGJMS-GMPHDF中用属性向量扩展单目标状态向量,用位置和属性的组合测量似然函数代替单目标位置及杂波位置测量似然函数,提高了不同类目标与杂波测量间的鉴别能力,进而改善了目标数目及状态的估计精度;在更新目标状态的同时,对目标属性信息进行更新。该算法实现了时变数目的目标状态和类别估计。杂波背景下交叉和临近并行机动目标的跟踪实验验证了该文算法的联合检测、跟踪与分类性能。     

非高斯杂波中知识辅助的信号检测算法

杂波的非高斯性会严重影响到常规信号检测算法的性能,提高非高斯杂波中信号检测性能是雷达信号检测领域的一个研究重点。本文考虑了一种在非高斯杂波中,利用先验信息进行信号检测的方法。假定杂波统计特性满足复合高斯模型,即可以表示为散斑分量与纹理分量的乘积。选择逆伽马分布作为纹理分量的先验分布,基于贝叶斯方法,给出了一种知识辅助的信号检测算法。计算机仿真结果表明,该检测算法的检测性能优于常规的自适应检测算法。进一步,本文采用McMaster大学的IPIX雷达海杂波数据作为研究对象,利用最大似然估计获得杂波非高斯性的先验信息,分析了该算法在实测数据中的检测性能。分析结果表明,在不同的雷达分辨率海杂波中,该算法也具有较好的检测性能。      

非高斯杂波下修正的SDD-GLRT距离 扩展目标检测器

 在球不变随机向量非高斯杂波背景下,针对SDD-GLRT方法在统计平均意义下对距离扩展目标进行最优检测所带来的检测损失,通过充分利用目标散射点的先验信息,基于有序统计检测理论,提出了修正的SDD-GLRT距离扩展目标检测方法.理论分析表明,在不存在目标的假设下,虚警概率与纹理分量的混合分布、杂波协方差矩阵以及确知信号的方向向量无关,即修正的SDD-GLRT方法具有恒虚警率(constant false alarm rate,CFAR)特性.仿真结果表明,随着积累脉冲数及目标散射点所占距离单元个数的增加,修正的SDD-GLRT检测性能得到提高.在散射点分布稀疏条件下,修正的SDD-GLRT的检测性能要明显好于SDD-GLRT和NSDD-GLRT,且在估计目标散射点个数存在微弱偏差和不同杂波尖峰情况下,修正的SDD-GLRT具有很好的鲁棒性.     

Maximum likelihood estimation, analysis, and applications ofexponential polynomial signals

We model complex signals by approximating the phase and the logarithm of the time-varying amplitude of the signal as a finite order polynomial. We refer to a signal that has this form as an exponential polynomial signal (EPS). We derive an iterative maximum-likelihood (ML) estimation algorithm to estimate the unknown parameters of the EPS model. The initialization of the ML algorithm can be performed by using the result of a related paper. A statistical analysis of the ML algorithm is performed using a finite-order Taylor expansion of the mean squared error (MSE) of the estimate about the variance of the additive noise. This perturbation analysis gives a method of predicting the MSE of the estimate for any choice of the signal parameters. The MSE from the perturbation analysis is compared with the MSE from a Monte Carlo simulation and the Cramer-Rao Bound (CRB). The CRB for this model is also derived     

A Mixed Real-time Algorithm for the Forward Kinematics of Stewart Parallel Manipulator

Aimed at the real-time forward kinematics solving problem of Stewart parallel manipulator in the control course, a mixed algorithm combining immune evolutionary algorithm and numerical iterative scheme is proposed. Firstly taking advantage of simpleness of inverse kinematics, the forward kinematics is transformed to an optimal problem. Immune evolutionary algorithm is employed to find approximate solution of this optimal problem in manipulator＇s workspace. Then using above solution as iterative initialization, a speedy numerical iterative scheme is proposed to get more precise solution. In the manipulator running course, the iteration initialization can be selected as the last period position and orientation. Because the initialization is closed to correct solution, solving precision is high and speed is rapid enough to satisfy real-time requirement. This mixed forward kinematics algorithm is applied to real Stewart parallel manipulator in the real-time control course. The examination result shows that the algorithm is very efficient and practical.     

Iterative Joint Detection, Decoding, and Channel Estimation in Turbo-Coded MIMO-OFDM

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of a list detector, a turbo decoder, and a channel estimator that is based on the space-alternating generalized expectation-maximization (SAGE) algorithm. This paper proposes a way to improve the convergence of the iterative detection and decoding by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. A new list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal losses of performance. Furthermore, the organization of spectrally efficient decision-directed (DD) SAGE channel estimation under a constrained number of detector-decoder iterations is optimized by computer simulations, and the SAGE algorithm itself is modified for nonconstant envelope constellations. The list recalculation is shown to improve convergence. It is also shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. Despite the low preamble density and fast-fading channel, the proposed iterative receiver shows robust performance.     

Improvements of a state-space iterative noise reduction algorithmfor harmonic retrieval

When the model of a noisy sinusoidal process is autoregressive moving average (ARMA), then the AR spectrum is biased. However, since the AR spectrum contains all the second-order information of the process, it is possible to retrieve the noiseless predictor from the noisy one. An iterative algorithm enabling the computation of the ARMA parameters from the AR parameters and a new well-suited initialization scheme are presented. Simulations of the state-space iterative noise reduction algorithm (SINA) are performed using various AR estimators. The mean-square-error graph is plotted for all these estimators and performances of the methods are discussed     

Iterative receiver for amplify‐and‐forward relay networks with unknown noise correlation

For amplify‐and‐forward relay networks, we propose an iterative scheme to estimate channel and detect information symbols for the multi‐antenna destination in spatially correlated noise. The equivalent channel coefficients and noise covariance are estimated by expectation–maximization algorithm. In addition, we discuss the initialization of iteration and analyze the modified Cramér–Rao bound to show the performance of the proposed iterative estimation. Moreover, on the basis of the structure of the proposed iterative estimator, a joint channel estimation and detection receiver is also provided. Finally, simulation results show that the proposed channel estimator and receiver can achieve the optimal performances in amplify‐and‐forward relay networks with unknown noise correlation. Copyright © 2012 John Wiley & Sons, Ltd.     

Deformable boundary detection of stents in angiographic images

In this paper, a procedure is described for deformable boundary detection of medical tools, called stents, in angiographic images. A stent is a surgical stainless steel coil that is placed in the artery in order to improve blood circulation in regions where a stenosis has appeared. Assuming initially a set of three-dimensional (3-D) models of stents and using perspective projection of various deformations of the 3-D model of the stent, a large set of synthetic two-dimensional (2-D) images of stents is constructed. These synthetic images are then used as a training set for deriving a multivariate Gaussian density estimate based on eigenspace decomposition and formulating a maximum-likelihood estimation framework in order to reach an initial rough estimate for automatic object recognition. The silhouette of the detected stent is then refined by using a 2-D active contour (snake) algorithm integrated with a novel iterative initialization technique, which takes into consideration the geometry of the stent. The algorithm is experimentally evaluated using real angiographic images containing stents.     

高斯尺度混合大气噪声模型的参数估计

非高斯大气噪声的参数估计对甚低频、超低频信号的最佳接收有重要意义。对大气噪声采用基于逆高斯分布的高斯尺度混合分布模型建模,研究了基于逆高斯分布的高斯尺度混合分布模型参数的性质,设计了高斯尺度混合大气噪声模型参数的马尔可夫链蒙特卡罗（ MCMC）算法。算法在贝叶斯层次模型下,采用Gibbs抽样和M-H抽样更新参数。仿真结果表明,该模型对大气噪声有很好的适用性,MCMC算法迭代效率和精度高,具有实际的应用价值。     

Covariance Structure Maximum-Likelihood Estimates in Compound Gaussian Noise: Existence and Algorithm Analysis

Recently, a new adaptive scheme [Conte (1995), Gini (1997)] has been introduced for covariance structure matrix estimation in the context of adaptive radar detection under non-Gaussian noise. This latter has been modeled by compound-Gaussian noise, which is the product c of the square root of a positive unknown variable tau (deterministic or random) and an independent Gaussian vector x, c=radictaux. Because of the implicit algebraic structure of the equation to solve, we called the corresponding solution, the fixed point (FP) estimate. When tau is assumed deterministic and unknown, the FP is the exact maximum-likelihood (ML) estimate of the noise covariance structure, while when tau is a positive random variable, the FP is an approximate maximum likelihood (AML). This estimate has been already used for its excellent statistical properties without proofs of its existence and uniqueness. The major contribution of this paper is to fill these gaps. Our derivation is based on some likelihood functions general properties like homogeneity and can be easily adapted to other recursive contexts. Moreover, the corresponding iterative algorithm used for the FP estimate practical determination is also analyzed and we show the convergence of this recursive scheme, ensured whatever the initialization.     

基于独立混合模型的EM算法参数初始化实现方法

隐马尔科夫树( Hidden Markov Tree, HMT )的状态不能被观测到,只能观测到另一个与状态有联系的量,通过观测量估计HMT模型参数是一个不完全数据参数估计问题。期望最大化( Expectation Maximization, EM )算法是一种求参数极大似然估计的迭代算法,可以用于解决不完全数据参数估计问题,因此被广泛应用于HMT模型的参数估计中。当初始参数偏离真实参数较大时,EM算法迭代次数多,收敛速度慢,通过一个计算量不大的参数初始化处理,能够有效减少EM算法的迭代次数,加快收敛速度。本文提出了一种基于独立混合模型的参数初始化方法,详细介绍了该方法的实现过程,通过采用独立混合模型进行参数初始化,使得EM算法的迭代次数明显减少,收敛速度大大提高。最后,计算机仿真验证了该方法的可行性和有效性。      

基于ARM的检测平台中电气测量模块的设计

介绍了基于ARM的短波综合检测平台中常用电气参数测量模块的设计与实现。给出了部分硬件原理图。模块用FS9721作为测量芯片,用ARM芯片作为主控芯片对FS9721芯片进行控制,读取数据。给出了ARM控制FS9721工作的软件程序框图和读取数据程序中的ARM寄存器初始化。实验表明文中的模块设计方法正确可行且适用性好,可以满足短波综合检测平台的要求。