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针对无源雷达中时延估计辐射源信号未知的情况,构建了一种新的时延最大似然估计模型.根据模型特点利用快速傅里叶变换(FFT)的计算方法实现时延估计.为了提高估计的精度,采用马尔科夫链蒙特卡罗(MCMC)抽样的方法估计时延值.该方法不需峰值检测,可直接给出时延估计结果.并推导了该模型下的时延估计的克拉美罗界(CRLB).仿真实验表明,MCMC算法可适用于窄带和宽带信号的时延估计;在样本相同的条件下,MCMC算法估计精度高于重要性采样(IS)算法和基于峰值检测的ML算法,计算复杂度低于IS算法,且MCMC算法可直接估计采样间隔非整数倍的时延. 相似文献
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为解决水下定位过程中的多途信道高分辨估计问题,文章给出了一种梯度投影最小二乘多途信道实数域高分辨估计方法,不同于频域最小二乘,方法利用时域迭代求解去除了矩阵求逆,降低了计算量,并且能够直接获得高分辨的实数域信道冲激响应。与MUSIC、Richardson-Lucy (RL)以及稀疏贝叶斯(SBL)等几种典型时域高分辨信道估计算法进行对比分析,仿真和实验结果皆表明,文中给出的梯度投影LS算法和SBL算法时延分辨力明显优于MUSIC和RL算法,水池实验结果时延分辨力可达1/(5B)(B为信号带宽)。所提方法幅度衰减估计精度略差于SBL算法。但最小二乘(LS)算法使用过程中不需要预知多途数目,更适用于大时延多途扩展情况,且计算量显著优于SBL算法。 相似文献
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针对窄带信号,通过构造互谱时间序列,在互谱域建立了平稳时间序列时延估计的最小方差无畸变响应(MVDR)滤波器模型;利用分段近似处理,类比空间MVDR自适应算法,给出了其具体算法(Algorithm of MVDR in cross spectral domain,CSMVDR);进行了数值仿真实验研究和海上实验数据处理。数值仿真与实验数据处理结果初步验证了CSMVDR时延估计对于舰船辐射噪声的适用性,CSMVDR时延估计有比相关检测更好的时延估计性能,能够提高信噪比增益和时延估计精度。 相似文献
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通过分析复杂环境中不同频带声信号时延估计的差异,提出多频带期望值最大时延估计方法。为了使各频带之间无重叠,该方法采用独立分带划分声信号不同频带,然后计算各频带广义互相关函数,并对子带广义互相关函数建立最大似然模型,最后利用期望值最大算法将多维优化转为一维优化的迭代式,获得最优子带广义互相关函数,在此基础上估计声信号的时延信息。数据仿真和实际实验结果表明,多频带期望值最大化时延估计相较常规时延估计有效估计值的百分比提升了10%,并将最优频带互相关函数应用到该定位算法中,在网格间距为0.3 m时,得到的峰值区域汇聚更明显,定位效果更好。 相似文献
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针对无线定位中时延估计在小样本(单快拍)、低信噪比条件下需要大量独立分布测量数据问题,提出了一种基于回溯筛选的稀疏重构时延估计算法,实现了单快拍、低信噪比条件下接收信号的精确时延估计.该算法首先建立接收信号的稀疏表示模型,然后基于该模型建立正交观测矩阵,最后在重构算法中引入回溯筛选思想,利用时延与观测矩阵之间的一一对应关系得到时延的无偏估计.对该模型下时延估计的克拉美罗界进行了推导.仿真分析表明,所提方法在单快拍、低信噪比条件下精度远高于求根多重信号分类算法,相比于正交匹配追踪算法,在较小的复杂度代价下性能得到了较大提升. 相似文献
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为了提高单基地多输入多输出(Multiple-Input Multiple-Output, MIMO)声呐阵列的波达方向(Direction of arrival, DOA)估计性能,提出了双尺度旋转不变子空间(Dual-Resolution Estimation of Signal Parameters via Rotational Invariance Techniques, DR-ESPRIT)算法。结合MIMO阵列虚拟阵列的结构特征,首先利用ESPRIT算法通过各条虚拟线阵内、基线间距不大于半波长的子阵间的旋转不变关系得到无模糊的粗估计结果,之后利用虚拟线阵间、基线较长的子阵间的旋转不变关系得到一组有模糊的精估计结果。参考粗估计结果对精估计结果进行解模糊,最终得到高精度无模糊的角度估计结果。为了降低运算复杂度,利用该思路对降维ESPRIT算法也进行改进,提出了双尺度降维ESPRIT算法。仿真试验首先验证了与传统算法相比,双尺度类DOA估计算法能够有效提高角度估计精度。此外,还分析了MIMO声呐阵列的发射、接收阵元的幅相扰动误差对算法角度估计性能的影响。 相似文献
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针对稀疏信号的超分辨方位估计问题,提出一种可变因子的稀疏近似最小方差算法(α-Sparse Asymptotic Minimum Variance,简记为SAMV-α)。该算法利用一个折衷参数进行最大似然估计值和稀疏性能的折衷处理,在迭代过程中改变稀疏近似最小方差算法(Sparse Asymptotic Minimum Variance,SAMV)的指数因子,得到强稀疏性能和超低旁瓣的方位谱图,实现邻近目标的超分辨方位估计和相干处理性能,且无需预估角度和信源数目等先验信息,并且折衷参数的取值为0到1之间,取值区间明确,避免了稀疏信号处理算法中正则因子选取困难的弊端。计算机仿真表明SAMV-α算法方位估计性能明显优于波束扫描类算法和子空间类算法,与同类型稀疏信号处理类算法相比仍具有较高的方位估计精度,同时对于邻近声源分辨能力,SAMV-α算法较SAMV-1算法性能提高约3dB。海上试验数据处理给出了分辨率更高的方位时间历程(Bering-Time Recording,BTR)图,有效验证了SAMV-α算法的性能。 相似文献
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为了对语音谐波/噪声模型中的语音截止频率轮廓进行更好的描述,本文提出了一种基于谐波和噪声能量改进的语音截止频率轮廓估计算法。改进算法对累积谐波和噪声能量函数进行对应谐波处的功率谱加权,并且在语音截止频率轨迹的平滑部分采用形态滤波的手段。实验表明,与原算法相比,通过改进算法得到的截止频率轮廓在语谱图上标注得更为准确,在主观评分测试中改进算法也获得了优于原算法的测试评分。改进算法比原算法能够更加准确的对语音截止频率轮廓进行估计,从而使得语音谐波/噪声模型在语音编码、语音合成及识别方面具有更为有效的应用。 相似文献
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Accuracy analysis on centroid estimation algorithm limited by photon noise for point object 总被引:1,自引:0,他引:1
The variance of the position of point object because of photon noise for centroid estimation algorithm is derived for the first time when the background cannot be eliminated by threshold. On this foundation, the accuracy of centroid estimation algorithm is analyzed by comparing its estimated efficiency (the ratio of the Cramer-Rao lower bound of point object and the variance for centroid algorithm) based on the classic estimation theory. The results indicated that the following factors influence the accuracy of centroid estimation algorithm: SBR (the ratio of the outputs from the signal light and the background), the Gaussian width of signal spot and the size of detected window. The effects of these factors are also described. 相似文献
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《声学学报:英文版》2015,(3)
To aim at the problem that the horizontal directivity index of the vector hydrophone vertical array is not higher than that of a vector hydrophone,the high-resolution azimuth estimation algorithm based on the data fusion method was presented.The proposed algorithm first employs MUSIC algorithm to estimate the azimuth of each divided sub-band signal,and then the estimated azimuths of multiple hydrophones are processed by using the data fusion technique.The high-resolution estimated result is achieved finally by adopting the weighted histogram statistics method.The results of the simulation and sea trials indicated that the proposed algorithm has better azimuth estimation performance than MUSIC algorithm of a single vector hydrophone and the data fusion technique based on the acoustic energy flux method.The better performance is reflected in the aspects of the estimation precision,the probability of correct estimation,the capability to distinguish multi-objects and the inhibition of the noise sub-bands. 相似文献
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Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning 下载免费PDF全文
《中国物理 B》2015,(11)
This paper addresses the direction of arrival(DOA) estimation problem for the co-located multiple-input multipleoutput(MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes compressive sensing(CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio(SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification(MUSIC) algorithm and other CS recovery algorithms. 相似文献
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The estimation of the point spread function (PSF) is a very important and indispensable task for practical image restoration.
Various PSF estimation algorithms have been developed, especially for the out-of-focus blur. However, a majority of them are
useless in an extremely noisy environment. This paper describes a new robust PSF estimation algorithm based on a distribution
of gradient vectors on the logarithmic amplitude spectrum mapped to the polar plane. The proposed algorithm can estimate the
out-of-focus PSF accurately and robustly, even for an image highly corrupted by noise. The effectiveness of the proposed algorithm
is verified by applying it to the PSF estimation for out-of-focus blurred and noisy images. 相似文献