浅海多径时延估计的功率谱加权自相关方法

针对浅海近距离时倒谱多径时延峰变弱难以提取的问题,提出了一种功率谱加权自相关多径时延估计方法。该方法采用FFT估计信号功率谱,对功率谱取1-α次幂(0<α<1),再进行IFFT变换,对其实部进行峰值检测获得多径时延估计。根据目标能量通过特性在正横前递增正横后递减的特点,在已检测时延前后一定区间内搜索下一个时延值,实现多径时延的自动提取.理论分析表明,多径时延估计的倒谱法与自相关法都可以看作功率谱加权自相关法的特例.海试数据处理与基于Bellhop射线模型的仿真计算表明,该方法在近距离时多径时延峰比倒谱法更清晰,输出信噪比提高了3~4 dB.采用该方法自动提取的海底反射多径时延用于单个水听器对目标距离的估计,与GPS测量距离基本相符,验证了方法的有效性.      

基于两次谱分析的时延估计方法研究

时延估计是目标定位跟踪系统的关键技术之一,在水声、雷达、声探测等领域广泛应用。时延估计的基本方法是互相关法和相位谱法。互相关法时延估计分辨率与信号带宽近似成反比,因此很难估计多目标时延。相位谱时延估计只能估计单目标时延,并且存在相位解绕问题。本文提出了两次谱分析时延估计方法,即将互功率谱函数再次进行谱估计,二次谱峰值位置间距即为时延估计,这种方法既能够估计单目标时延,又能够估计多目标时延,并且不用相位解绕。仿真计算验证了两次谱时延估计方法的可行性。     

互谱域MVDR自适应时延估计方法研究

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。     

基于FICP算法提高对低频噪声时延的估计精度

针对非平稳随机噪声（直升机飞行声音，导弹喷流噪声）信号，研究了提高时延估计计算精度的方法，通过计算信号的细化频谱，并避免了标准FFT计算带来的栅栏效应，从而使得对于超短取样信号的频谱计算精度与信号长度无关，采用快速计算方法FICP由N点互谱计算2N点相关波形，提高了相关波形的分辨率，使得时延估计精度得以提高。对实测直升机飞行噪声和导弹噪声计算了不同取样长度和不同采样率下的时延估计，得出了谱分辨率越高，时延估计精度越高的结论。     

分数低阶协方差谱用于改进的时延估计方法

时延估计技术是一项活跃的研究课题。近年来,满足稳定分布的脉冲噪声模型得到了广泛的研究,并在时延估计技术中得到了应用。为提高时延估计方法在脉冲噪声环境下的估计精度和稳定性,本文提出了一种改进的基于分数低阶协方差谱的广义互相关时延估计方法,并进一步引入经验参数对所提出方法的性能进行调整。仿真分析表明,新方法在高斯和脉冲噪声环境条件下都具有良好的顽健性。     

基于二次相关的语音信号时延估计改进算法

目前语音信号的时延估计研究,大部分采用的是广义互相关算法。然而,广义互相关时延估计算法易受噪声和混响环境影响。为此,本文提出了一种基于二次相关的语音信号时延估计改进算法,该算法对语音信号进行二次互相关运算,并结合Hilbert变换,对二次互相关峰值进行进一步的锐化处理,使得反映时延的峰值点检测更为准确。实验结果表明,改进的时延估计方法在非平稳的语音信号中能够有效地抑制噪声干扰,且在不同混响条件下时延估计具有更好的性能。     

一种改进的非整数自适应时延估计方法*

针对基于时延估计的机电设备故障声定位中的低信噪比和脉冲噪声情况,用α稳定分布建模噪声,改进了非整数自适应时延估计方法。共变相关法对观测序列进行时延估计粗测,将得到的估计值作为非整数自适应时延估计器的初值;将共变相关法中共变序列作为时延估计器的输入信号,在最小平均p范数准则下迭代得到非整数时延估计值。共变序列保留了原始序列间的时延信息,削弱了不相关的噪声。计算机仿真对比实验验证了改进的方法在脉冲环境和低信噪比条件下有更好的性能。     

利用多级时延估计与传递函数拟合的水下自干扰抑制法

提出了一种利用传递函数拟合与多级时延估计相结合的自干扰抑制方法来抑制接收信号中的强自干扰信号。首先通过频域解卷积估计发射泄漏的传递函数,进而构建出基本的对消信号,再通过互相关法与互谱法相结合的多级时延估计,精细补偿对消信号,获得良好的自干扰抑制效果。对实际海试数据的处理结果表明,当采用无人水下航行器(UUV)搭载一体化系统,在舷侧阵工作扇面内发射多帧线性调频(LFM)信号对目标船进行有源探测时,该方法的抑制比可达36 dB,且使得可检测回波的信干比从-30 dB降低到-55 dB,对后续的自干扰信号存在同样的抑制效果。以上结果表明该方法可抑制强自干扰信号,呈现出了较强的稳定性,使得淹没于强自干扰信号中的回波信号得以检测。     

用时延估计法提高光纤光栅中心波长检测精度

提出将雷达、声呐领域中的时延估计算法用于均匀应变场或温度场中任意形状的光栅反射谱中心波长的检测.分析了信噪比(SNR)对不同形状反射谱进行峰值提取的影响.分析结果表明,对均匀周期的单模光纤布拉格光栅,其长度越长,折射率变化越大,峰值检测受信噪比影响也越严重;实验结果表明,与常规算法相比,采用时延估计中的相位谱法进行中心波长偏移量的提取,估计精度不受波长扫描精度的限制,对噪声造成的中心波长随机漂移有很好的抑制作用,而且无需对反射谱的形状进行假设与曲线拟合,计算量小,检测精度可达到皮米量级.     

一种单频水声信号多径时延估计算法

提出一种针对单频水声信号的多径时延估计算法,通过采用频域加权并结合进化规划算法解决代价函数强烈振荡下的高效全局优化.算法引入多径径数建立由多径时延、幅度系数及径数组成的水声信道多径参数模型,在采用频域加权预白化进行非线性最小二乘代价函数解耦处理的基础上,结合进化规划算法进行非线性参数解空间的高效全局寻优,从而实现多径时延的高分辨率估计.利用仿真数据和海上实验数据进行的实验表明,本算法具有较高的多径时延估计精度并可同时实现多径径数的估计.     

基于倒谱分析的单水听器目标运动参数估计

基于浅海射线声学多途结构,提出了一种具有高稳健性、高精度的单水听器目标运动参估计方法.针对匀速直线运动目标,综合多途时延差和运动学几何关系,构建了目标三维多途时延模型,进而获得目标运动参数与多途时延差的非线性时间映射.研究了典型水声信道的倒谱表达式,提出了利用倒谱提取多途时延差的策略.采用差分进化综合优化手段估计目标运动参数,提高了算法的稳健性.理论及仿真结果表明,倒谱的时间分辨率不受信号带宽的限制,而主要取决于信号类型和信噪比;CW信号的倒谱对多普勒不敏感;参数估计精度主要取决于时延差估计精度和参与差分进化运算的信息量,当包含有最近点信息时参数估计性能较好.水池实验结果进一步验证了方法的正确性和有效性.     

Multipath time delay estimation of underwater acoustic sinusoidal signals

To overcome the performance limitation of multipath time-delay estimation posed by underwater acoustic sinusoidal signals, an approach incorporating the frequency-domain weighting of the highly oscillatory Nonlinear Least Squares （NLS） cost function with the evolu- tionary optimization was proposed to facilitate the accurate estimation of the multipath timedelay of sinusoidal signals. In the described method, the number of the effective multipath signals, which is included into the parameter model as well as the multipath time-delay and amplitude factor, can be estimated simultaneously thus avoiding the requirement of additional computation. The experimental results performed with numerical simulation and sea-trial data are provided, demonstrating the effectiveness and precision enhancement of the proposed algorithm.     

Multipath structure of the typical under-ice sound channel in the Arctic:theory and experiment

Considering the typical acoustic environment of the Arctic, this paper proposes a method based on OASES-Bellhop coupled model to rapidly analyze the multipath structure of the under-ice sound channel. Firstly the proposed model refers to ice plate as stratified elastic media with some roughness. Secondly,it uses the perturbation method and Kirchhoff approximation theory to solve the scattering loss due to the sea ice inhomogeneity. Finally, the model predicts the multipath structure of the under-ice channel through Ray theory. The results of the numerical simulation and experiment indicate that the typical Arctic sound channel presents multipath structures due to the sea ice and seabed in the range of 6 km, respectively.The sea ice reflection paths are stable,with a short multipath spread within 14 ms. The seabed reflection paths have relatively weak strength. The proposed OASES-Bellhop coupled model successfully predicts the amplitude and delay of the multipath structure with small error,which indicates the proposed model is able to analyze and predict the multipath structure of the observed acoustic environment in experiment.     

Time-delay estimation of reverberated speech exploiting harmonic structure.

The relative time delay associated with a speech signal received at a pair of spatially separated microphones is a key component in talker localization and microphone array beamforming procedures. The traditional method for estimating this parameter utilizes the generalized cross correlation (GCC), the performance of which is compromised by the presence of room reverberations and background noise. Typically, the GCC filtering criteria used are either focused on the signal degradations due to additive noise or those due exclusively to multipath channel effects. There has been relatively little success at applying GCC weighting schemes which are robust to both of these conditions. This paper details an alternative approach which attempts to employ a signal-dependent criterion, namely, the estimated periodicity of the speech signal, to design a GCC filter appropriate for the combination of noise and multipath distortions. Simulations are performed across a range of room conditions to illustrate the utility of the proposed time-delay estimation method relative to conventional GCC filtering approaches.     

典型北极冰下声信道多途结构分析及实验研究

针对北极海域典型声场环境,提出了基于OASES-Bellhop耦合模型的冰下声信道多途结构快速分析方法。模型将海冰等效为具有粗糙界面的弹性分层介质,利用微扰法与Kirchhoff近似,估计海冰界面不均匀造成的散射损失,结合射线传播理论对典型北极冰下声信道多途结构进行分析与预报。数值仿真与实验结果表明,在6 km距离处,典型北极冰下声信道由于海冰与海底反射分别形成多途结构,海冰多次反射路径叠加形成的多途结构较为稳定,时延扩展在14 ms范围内,海底反射路径强度相对较弱。OASES-Bellhop模型对冰下声信道多途结构幅度和时延预测误差较小,能够较好的解释及预报实验观测到的多途结构环境特性。      

分布式光纤Sagnac干涉仪中基于倒谱的多分辨率入侵定位算法

基于Sagnac结构的分布式光纤干涉仪具有灵敏度高和动态范围大等特点, 特别适合监测入侵信号, 并确定其发生位置. 不同于现有的时域自相关及频域陷波点定位方法, 本文提出将两相干光束相位差信号变换到倒谱域, 通过倒谱系数峰来方便准确地确定入侵位置. 此外, 为克服倒谱定位中降采样带来的分辨率粗糙问题, 提出了多分辨率定位综合的改进方法. 通过改变降采样因子和利用高阶倒谱峰两种措施对同一入侵信号得到一系列定位值, 并对所有定位值取平均作为入侵信号的最终定位值. 实验首先在距离传感光缆尾端40.498 km处制造3个入侵信号. 采用本文提出的倒谱方法对该入侵得到84个定位值, 综合后的定位误差分别只有9 m, 17 m 和 11 m, 显示了对入侵信号定位的准确性. 之后对不同位置的入侵信号进行定位, 7次实验的定位误差的标准差达到12 m, 而相同情况下的自相关方法和陷波点方法的定位误差标准差分别为695 m和118 m. 此外, 这种方法还具有操作简单、易于自动测量等特点, 有望在Sagnac干涉传感系统中发挥重要作用.     

A novel OCDMA receiver with all-optical auto-correlation peak clock extraction by using Fabry–Perot filter and optical hard-limiter

A novel OCDMA receiver with all-optical auto-correlation peak clock extraction is proposed and simulated. The Fabry–Perot filter (FPF) is used to realize all optical clock extraction. To reduce the effects of cross-correlation and auto-correlation side-lobes on clock extraction, the optical hard-limiter (OHL) is adopted. The simulation results with OptiSystem show that the scheme based on FPF and OHL is effective to extract the auto-correlation peak clock of OCDMA system in multi-user environment.     

A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.     

基于双耳模型的倒谱系数在声目标识别中的应用

在声目标识别中,基于单路信号的特征提取算法难以保证特征在低信噪比条件下的鲁棒性。针对该问题,本文使用由两个传声器组成的小孔径阵列采集信号,采用Stereausis双耳听觉模型对两路信号进行综合处理,在此基础上设计了基于双耳模型的倒谱系数,并将其作为识别特征应用于多类地面目标识别。试验表明该特征可有效提高识别系统的识别性能和抗噪声能力。