目标方位估计的相关矩阵最大特征值法

研究均匀线阵(ULA)波束形成时作者发现，当操纵线阵指向方位不断变化，相关矩阵的最大特征值也随着有规律地变化着。通过分析最大特征值的变化，能够估计目标方位和数目。本文从实验数据和计算机仿真两方面对单目标和多目标进行处理，验证上述方法的可行性。     

基于随机矩阵最大特征值分布的频谱感知算法

现有频谱感知算法在低信噪比时检测性能较低且受虚警概率影响大,针对此问题,提出了一种基于wishart矩阵样本协方差矩阵最大特征值的分布特性的频谱感知算法。该算法利用最大特征值与几何平均特征值的比值,不需要主用户的先验知识,不敏感于噪声,对相关信号和独立同分布信号均具有较高的检测性能。仿真结果表明,所提算法受虚警概率的影响较小,检测性能高,并且在采样点数、协作用户数、信噪比及虚警概率较小的情况下,也能获得较好的检测性能。     

一种深空背景空间小目标条痕检测算法

针对深空慢速运动目标(目标在焦面的运动速度小于1 pixel/frame)提出了一种最大似然条痕检测算法,能够在较低信噪比情况下实现有效的慢速目标检测。算法将目标脉冲形状信息引入信号模型中,是最大值投影算法的改进形式。建立基于高斯噪声分布的图像信号模型,在此基础上推导了最大似然条痕检测算法模型;分析该算法的实时性及其理论探测性能;采用蒙特卡罗仿真方法比较最大似然条痕检测算法与最大值投影检测算法的检测性能。仿真结果表明,输入信噪比为3.5时,最大似然条痕检测算法的探测概率为95%,其相同探测概率条件下所需信噪比比最大值投影算法降低了2.5(即最大值投影算法要达到95%的检测概率,所需信噪比为6)。算法实时性分析表明,最大似然条痕检测算法的实时处理能力为31.25 Mb/s。     

一种主动浮标阵的定位解算方法仿真分析

在研究单枚主动定向浮标定位原理的基础上,推导出一种利用最小二乘法解算多枚浮标的定位结果的方法,并针对圆阵就阵元个数、圆阵半径以及测距误差对该解算方法的性能影响进行了数值仿真分析。仿真结果表明,该算法在测向均方误差较大或者目标在距离圆内的情况下,性能远优于单枚主动定向浮标;圆阵半径2 km左右,阵元个数在6左右时,该解算方法的性能达到最佳。此外,该解算算法很容易被扩展至三维定位问题,因此有一定的理论和应用价值。     

联合多站阵元域数据的水下目标检测与跟踪

为了提高复杂海洋环境中目标的检测、跟踪性能,提出一种联合多站阵元域数据的水下目标检测与跟踪方法.该方法采用序列马尔科夫链蒙特卡洛思想对目标进行采样更新,通过对接收概率中的后验概率以及采样函数进行分解展开,并根据多站阵元域数据计算采样粒子的联合似然,在迭代过程中实现目标数目和目标状态的联合估计.研究结果表明,该方法对单目标的平均定位误差在较高信噪比下能够稳定在50 m以内,对多目标随机出入场景中新生及消失目标实现有效检测,同时对强干扰下弱目标及交叉目标实现有效检测跟踪。仿真结果和海试数据均验证该方法具有良好的目标检测与跟踪性能。      

基于EMD的单通道信源数估计方法

为了获取单通道接收信号的信源数目,针对普通信源数估计方法不能直接用于单通道接收信号的问题,提出了基于经验模态分解(empirical mode decomposition, EMD)的信源数估计方法。将单通道信号通过EMD处理,得到多个固有模态函数(intrinsic-mode function, IMF),据此构造数据协方差矩阵。对所构造的协方差矩阵进行特征值分解,采用基于信息论的AIC和MDL准则估计信源数。为进一步提高算法估计性能,引入对角加载技术对矩阵特征值进行平滑处理。仿真实验结果表面,本文提出的方法能够适用于单通道信源数估计,对角加载技术能够显著提高算法检测性能。     

一种频域-波数域峰值筛选的无源声呐宽带检测方法

在水声信号处理中,传统的无源声呐宽带目标检测在多目标、强干扰的复杂环境中输出信噪比低,使得检测性能急剧下降。针对此问题,提出一种基于均匀线列阵在频域-波数域上宽带信号能量分布特性进行目标检测的方法。该方法首先将阵列信号转换到频域-波数域,利用不同频率下波数主瓣、旁瓣宽度特征以及空间分布特征,设计针对主瓣的判别与分配方法,实现对同一目标不同频率下波数谱主瓣判别,使用主瓣能量累积、主瓣数目累积的方式来形成方位谱,从而进行目标检测。理论分析和仿真结果表明,所提方法只利用对目标检测有突出贡献的波数主瓣,降低了旁瓣的影响,有效提高了无源宽带水声目标的检测能力。海上试验数据处理结果表明,目标输出信噪比相比子带峰值能量检测算法可提高5.58 dB,较传统能量检测可提高8.73 dB,计算时间相比传统能量检测降低46%,验证了所提方法的有效性与实时性。     

认知无线电网络中利用FEC和DE的多节点频谱感知算法

针对认知无线电网络(CRN)中空闲频谱感知困难的问题,本文提出了基于前向纠错和差分进化算法的多节点频谱感知算法。首先,利用基于差分进化算法的协同检测完成信号感知;然后,研究了信道噪声对频谱感知性能的影响;最后,分析了前向纠错技术在信道存在噪声时对频谱感知性能的影响。仿真实验将纠错和无纠错控制信道的不同信噪比作为依据,采用三种不同的检测方法评估了本文算法。仿真实验结果表明,在存在噪声的认知无线电网络中,本文算法提高了系统的性能和检测概率,且协同感知算法的性能随着节点数目的增加而提高,该算法适合应用于实时性要求较高的应用程序。     

拖曳线列阵机动时的一种目标检测算法研究

根据水动力学流体运动特性,对Burgess建立的水中运动拖缆模型进行了修正,并基于有限差分法和牛顿迭代法提出修正模型的实时求解方法。同时,在阵形求解理论和自适应宽带聚焦波束形成理论的基础上,提出一种畸变阵的目标检测算法。将实时计算所获得的阵元位置信息作为变量引入自适应宽带聚焦波束形成算法中进行目标检测。并进行了海试。试验中在常规算法失效的情况下该算法准确的检测到目标。数据处理结果表明,该算法在阵形大幅度畸变时的输出信噪比比常规算法高15 dB。解决了拖线阵在机动(转向)过程因阵形畸变而无法检测目标的问题,充分验证了该算法的有效性。     

扩展拖曳阵宽带频域逆波束形成技术研究

为了提高扩展拖曳阵在低信噪比下对目标的探测性能,本文将基于扩展拖曳阵方法的被动合成孔径技术与宽带频域逆波束形成技术进行了结合,形成了一种新的被动合成孔径测向技术,有效地提高了扩展拖曳阵技术的探测性能,改善了传统基于被动合成孔径测向技术在低信噪比下性能急剧下降的问题,在低信噪比下获得了更高的方位分辨率,同时对于多目标及弱目标检测能力也更为优异。计算机仿真和海试数据的处理都证明了这一算法的良好性能。     

传声器阵列特征值滤波去噪方法

作为二阶统计量的互谱矩阵(CSM)是声学成像算法的核心输入量。为增强传声器阵列的去噪表现,研究了互谱矩阵特征值滤波的机理,并提出了两种新型的特征值滤波方法的设计准则:(1)声源互谱矩阵的Stein无偏风险估计(SURE收缩),即基于SURE准则的特征值软阈值收缩;(2)进一步提高声源互谱矩阵EYM (Eckart-Young-Mirsky)估计误差的最优收缩(Opt-Shrinkage)方法,即对声源特征值所含有的噪声进一步去除。然后,在3000个快照数,0 dB信噪比(SNR),100个传声器的环境下进行仿真。通过仿真,与经典的MUSIC方法比较原声源信号与去噪后信号的互谱矩阵对角线误差,其中MUSIC方法的互谱矩阵对角线误差为74.15%,SURE收缩为41.97%,最优收缩为20.62%。并通过改变快照数、声源数与SNR,对比了不同特征值滤波去噪方法的效果。从仿真结果上看,在少声源(少于40个)工况下SURE收缩方法的去噪效果优于MUSIC方法与最优收缩方法;在声源数超过42个后,最优收缩方法取得更显著的效果。最后,在3个声源、60个传声器数、-5 dB信噪比的声源定位实验中,SURE收缩与最优收缩均获得了相比MUSIC方法更好的去噪效果。研究表明在现有传声器阵列特征值滤波方法的基础上,对特征值的进一步处理可以得到更好的去噪效果。      

Automatic determination of the number of targets present when using the time reversal operator

Acoustical time reversal mirrors have been shown to provide a highly accurate means of studying and focusing on acoustical sources. The DORT method is a derivation of the time reversal process, which allows for focusing on multiple targets. An important step in this process is the determination of the number of targets or sources present. This is achieved by examining the eigenvalues of the time reversal operator (TRO). The number of significant eigenvalues is then chosen as the number of sources present. However, as mentioned in [N. Mordant, C. Prada, and M. Fink, J. Acoust. Soc. Am. 105, 2634-2642 (1999) and C. Prada, M. Tanter, and M. Fink, in Proceedings of the IEEE Symposium, 1997, pp. 679-683], factors such as low signal to noise ratio (SNR), small data sample, array configuration and the target location may result in the eigenvalues corresponding to the targets no longer being distinguishable from the background noise eigenvalues. This paper proposes a robust method of automatically determining the number of targets even in the presence of a small number of snapshots. For white Gaussian noise, the profile of the ordered eigenvalues is seen to fit an exponential law. The observed eigenvalues are then compared to this model and a mismatch is detected between the observed profile and the noise-only model. The index of the mismatch gives the number of scatterers present.     

An empirical characterization of the quality of DTI data and the efficacy of dyadic sorting

Metrics calculated from images acquired using the diffusion tensor imaging (DTI) technique possess a systematic bias that depends on signal-to-noise ratio (SNR). Dyadic sorting provides a simple method for remediating some of this bias within a region(s) of interest (ROI). Although this bias and its removal using dyadic sorting have been studied previously within a theoretical framework, one can employ precise geometric knowledge of microstructures to perform an empirical comparison between expected DTI results and those measured with a scanner. In this project, the biasing effect of low SNR (approximately 1-10) on DTI eigenvalues was measured directly using water-filled capillary structures of two different sizes, and the magnitude of the corrective effect of dyadically sorting eigenvector-eigenvalue pairs was characterized. Multiple DTI series were acquired for determining DTI metrics at eight unique SNR values, using T(R) to vary signal intensity via T(1) contrast. Differences between the second and third eigenvalues, which should be equal for prolate geometry, ranged from approximately 23% to 45% and from 19% to 41% for large and small inner diameter capillaries after sorting eigenvalues by magnitude, and ranged from approximately 1% to 18% and from 1% to 4% after dyadic sorting. A high-resolution DTI series was used to observe the effect of ROI size on dyadic sorting. For restriction of diffusion on the scale of the small capillary at SNR approximately 18, an ROI with > or =50 pixels is adequate to determine fractional anisotropy to 99% accuracy, while larger ROI are required to resolve the two smaller eigenvalues to the same accuracy ( approximately 330-390 pixels). At low values of SNR, the iteration of dyadic sorting is suggested to achieve good accuracy. A method for the incorporation of empirical measurements into a bias-correction map, which would be useful for characterizing uncertainty and for reducing systematic bias in DTI data, is introduced.     

进行子阵加权波束形成的波达方向估计

针对水声信道的多径效应以及海底散射信号信噪比低导致方位估计性能较差的问题,提出了一种基于子阵加权波束形成的UESPRIT算法(Weighted Beamspace UESPRIT Based on Subarrays,BS-BUESPRIT)。首先利用密集波束域转换矩阵估计回波信号的方位谱,进而估计同一时刻到达阵列的回波数目;之后将均匀线阵分为多个尺寸相同、相互重叠的子阵,利用加权波束形成对各子阵接收信号做指定方向的空域滤波;最后基于各子阵波束形成后的输出结果,利用UESPRIT算法实现回波方向的估计。仿真和湖试、海试试验结果表明,与UESPRIT算法相比,BS-BUESPRIT算法提高了信号波达方向估计性能,在多径和较低信噪比条件下有着更高的估计精度,应用于高分辨率测深侧扫声呐时有效地提高了声呐的测深性能。      

消频散阵不变量距离估计方法

针对传统阵不变量方法在低信噪比和远距离测距性能差的问题,提出了消频散阵不变量测距方法。该方法首先对垂直阵接收信号进行消频散波束形成,消除频散影响,压缩脉冲宽度,提高简正波到达时间分辨率;然后,对得到的角度-时间谱采用反卷积处理提高简正波俯仰角度分辨率。通过消频散和反卷积处理,减小了简正波相对到达时间和俯仰角在角度-时间谱图中的聚焦范围,有利于准确的提取阵不变量和估计目标声源的距离。计算机仿真证明了该方法在低信噪比条件下的鲁棒性;2009年崂山湾实验数据处理结果表明:相比于传统阵不变量测距方法,该方法距离估计误差减小2.5%。      

An improved automatic time-of-flight picker for medical ultrasound tomography

Objective and motivationTime-of-flight (TOF) tomography used by a clinical ultrasound tomography device can efficiently and reliably produce sound-speed images of the breast for cancer diagnosis. Accurate picking of TOFs of transmitted ultrasound signals is extremely important to ensure high-resolution and high-quality ultrasound sound-speed tomograms. Since manually picking is time-consuming for large datasets, we developed an improved automatic TOF picker based on the Akaike information criterion (AIC), as described in this paper.MethodsWe make use of an approach termed multi-model inference (model averaging), based on the calculated AIC values, to improve the accuracy of TOF picks. By using multi-model inference, our picking method incorporates all the information near the TOF of ultrasound signals. Median filtering and reciprocal pair comparison are also incorporated in our AIC picker to effectively remove outliers.ResultsWe validate our AIC picker using synthetic ultrasound waveforms, and demonstrate that our automatic TOF picker can accurately pick TOFs in the presence of random noise with absolute amplitudes up to 80% of the maximum absolute signal amplitude. We apply the new method to 1160 in vivo breast ultrasound waveforms, and compare the picked TOFs with manual picks and amplitude threshold picks. The mean value and standard deviation between our TOF picker and manual picking are 0.4 μs and 0.29 μs, while for amplitude threshold picker the values are 1.02 μs and 0.9 μs, respectively. Tomograms for in vivo breast data with high signal-to-noise ratio (SNR) (∼25 dB) and low SNR (∼18 dB) clearly demonstrate that our AIC picker is much less sensitive to the SNRs of the data, compared to the amplitude threshold picker.Discussion and conclusionsThe picking routine developed here is aimed at determining reliable quantitative values, necessary for adding diagnostic information to our clinical ultrasound tomography device - CURE. It has been successfully adopted into CURE, and allows us to generate such values reliably. We demonstrate that in vivo sound-speed tomograms with our TOF picks significantly improve the reconstruction accuracy and reduce image artifacts.     

大孔径静态干涉成像光谱仪的信噪比分析

根据大孔径静态干涉成像光谱仪的原理和特点，分析了光辐射能在光学系统内的传输情况，对探测器上接收到的光辐射能信号和噪音信号做了详细的推导，得到了仪器理论信噪比的表达式.在实验上以大口径积分球为辐射源，采集仪器输出的干涉图像，计算得到仪器的实际信噪比曲线，对大孔径静态干涉成像光谱仪的理论信噪比曲线与实际的信噪比曲线做分析比较，结果验证了用推导的论信噪比的表达式来预估仪器信噪比的可行性.     

改进维纳滤波器及其在目标方位估计中的应用

针对复杂的水声环境以及信噪比较低的目标信号导致方位估计性能较差的问题,本文提出了一种基于改进维纳滤波器和波束形成器的方位估计方法,该方法能够抑制噪声,提高目标方位估计性能。首先利用改进维纳滤波器抑制各通道接收数据中的噪声,提高输出信噪比。在此基础上,将改进维纳滤波器的输出通过波束形成器,获得目标的方位估计。改进维纳滤波器能够通过调整滤波器参数,控制滤波器的噪声抑制能力和信号失真。因此,针对不同的波束形成器对信号失真的敏感程度不同,可以通过调节改进维纳滤波器的参数,获得噪声抑制与信号失真之间的最佳折中,从而提高输出信噪比,降低目标方位估计的信噪比门限和均方根误差。仿真和实验结果验证了本文方法。      

结合卷积神经网络的浅海有源探测信道匹配

信道匹配方法在有源探测领域是一种重要的提升检测信噪比的方法。针对非确知海底参数环境下的有源探测信道匹配问题,提出一种结合卷积神经网络进行信道匹配的算法。该算法基于海底参数扰动开展声场仿真生成卷积网络训练数据;首先通过分类网络将信号按照海底底质类型分类,在每个分类区间内采用单独的卷积网络反演海底参数;然后结合声场模型估计信道传递函数,进行信道匹配,从而在非确知环境下抑制多途影响,提升回波检测能力。仿真与实验结果表明,该算法能够在不确知海底环境条件下,有效估计信道传递函数,实现信道最优化匹配,在实验条件下可提高回波检测信噪比4 dB左右。相比传统方法,该算法可以在海底参数不确知条件下对低接收信噪比的信号实现信道匹配,同时不需要高信噪比的实验参考信号,有效提高了信道匹配方法的环境宽容性。      

Higher-order cross-correlation-based Doppler optical coherence tomography

A method based on higher-order cross-correlation is proposed to fetch the Doppler information on flow velocity within areas under low signal-to-noise ratio (SNR) by spectral domain optical coherence tomography. The proposed method is theoretically developed and validated by measurement of a moving mirror with known velocities. Standard deviations of flow velocities of the mirror under different SNRs are determined by the proposed method and compared with those by the modified phase-resolved method. Measurement of flowing particles within a glass capillary is also conducted, and Doppler flow velocity maps of the glass capillary are reconstructed by both methods. All experimental results demonstrate that the proposed method can significantly suppress noise, thus rendering it suitable for flow measurement under low SNR cases.