光开关选通的光纤激光水听器时分复用阵列

为了降低传感器系统的成本及体积,利用光纤激光传感单元的特性,采用光开关选通的光纤激光水听器时分复用技术,构建了实用的基于光开关的4元光纤激光水听器时分阵列。进行了理论分析和实验验证,分析了使用光开关选通的光纤激光水听器时分复用阵列方案的选通脉宽和损耗,指出了各通道光脉冲对干涉信号的采样率的限制因素。结果表明,光开关选通的光纤激光水听器时分阵列可以很好地完成复用信号的采集和复用,实验中4元时分复用水听器相邻通道的串扰为-17dB。     

激光器强度噪声对光纤水听器相位载波解调的影响

相位载波(PGC)调制解调技术作为光纤水听器主要的检测技术之一,已经应用于许多光纤水听器阵列系统之中,该方案对光源提出了窄线宽、可调谐、低噪声等较高的要求。实验中发现,光源弛豫噪声对系统噪声性能产生了较大的影响。在理论分析光源弛豫噪声对相位载波解调影响的基础上,提出了通过调节抽运功率,控制弛豫振荡中心频率为相位载波调制频率的半倍频的奇数倍,来降低系统解调噪声的方法,实验验证了理论结果,解调噪声由最高的—86.7 dB减小到—106 dB。实验进一步采用了光电负反馈方法来抑制弛豫噪声,在弛豫振荡峰处抑制噪声约25 dB,得到了约—100 dB的较为平坦的激光器噪声谱级,使得相位载波解调噪声达到—110 dB,基本满足了光纤水听器系统的要求。     

光纤水听器的军事应用

本文简要叙述了光纤水听器在军事中的应用和发展现状,对光纤水听器阵列、光纤布格光栅水听器的发展现状也作了简要介绍。     

远程光纤水听器系统中传输光纤引入噪声的抑制

远程传输光纤受环境的影响会在水听器系统中引入噪声,利用迈克耳孙光纤干涉仪模拟光纤水听器,搭建了远程光纤水听器系统,采用参考干涉仪法解调对传输光纤引入的噪声进行抑制。实验结果表明,与常用的相位产生载波技术(PGC)解调相比,参考干涉仪法解调在不影响传感水听器对声信号检测能力的情况下,对传输光纤引入的扰动噪声能进行良好的抑制,频率为500 Hz噪声的抑制量可达到约25 d B,同时该方法使传感水听器系统中100 Hz位置的本底噪声也降低了17 d B。     

基于3×3耦合器的光纤水听器时分复用系统的光强补偿方法

提出一种在光纤水听器时分复用系统中以3×3耦合器作为匹配干涉仪的光强补偿方案,光纤水听器仍为2×2耦合器结构。该方案以时分复用中的非干涉脉冲作参考光强,通过对3×3耦合器两路输出进行简单数学运算并结合数字反正切技术实现信号解调,光强波动和耦合器分光比变化导致的信号畸变得到有效抑制。算法不需要载波调制,可实现光纤水听器近似等臂干涉,有利于降低系统相位噪声。算法的运算量小且适用于一般的非对称3×3耦合器,解调动态范围大。利用该方案实现了8路光纤水听器时分复用实验系统,在光功率变化约10%时,光强补偿方法可将总谐波失真平均值降低30dB,系统噪声本底在信号频率为1kHz时小于30μrad/Hz1/2。     

光纤水听器及阵列的发展概况

本文简要介绍各国光纤水听器的发展，对各种光纤水听器的归一化灵敏度作了比较，给出了一些干涉型光纤水听器的多路复用阵列及其性能。最后介绍了水听器阵列的试验情况。     

全保偏光纤水听器阵列

报道了全保偏光纤水听器阵列的研究结果。在详细介绍全保偏光纤水听器基元结构和工作原理基础上 ,描述了四基元阵列结构和性能 ,给出了光纤水听器基元相对接收灵敏度的频率响应和阵列指向性的测量结果     

光纤矢量水听器系统本底噪声的自适应抵消

本底噪声是制约干涉型光纤矢量水听器系统在低频远距离目标探测领域中应用的重要因素之一。为降低系统的本底噪声,提出了一种基于自适应噪声抵消的降噪方法。在光学系统中增加一个与传感迈克耳孙干涉仪结构参数相等的对声压不敏感的参考干涉仪作为参考通道,以获得由光源与电路等共同噪声源引入到各干涉仪的高相关噪声。然后,使用归一化均方根误差的自适应算法分别对声压及加速度信号中与参考信号高相关的噪声部分进行抵消。湖试数据的分析结果表明,该方案能够有效降低系统本底噪声,其中对电磁干扰带来的50 Hz倍频信号的抑制能力高达15~25 dB,对500 Hz以上的平坦噪声谱的抑制为3 dB左右。     

麦克风阵列的协同自适应滤波语音增强方法

在麦克风阵列语音增强方法中,传统的广义旁瓣抵消器在处理存在显著脉冲噪声的语音信号时效果较差。为提高在脉冲噪声干扰下的语音信号增强效果,提出一种麦克风阵列的协同自适应滤波语音增强方法。该方法采用协同自适应滤波取代线性自适应滤波,基于NLMS算法导出了滤波器权值和协同因子的自适应更新算法。仿真实验结果表明,所提方法能有效地消除掉语音信号的脉冲噪声和高斯噪声,克服线性自适应滤波对非线性脉冲噪声的不敏感性,比广义旁瓣抵消器效果优越很多。     

水下小孔径阵列自适应匹配滤波检测方法

为了解决水下小孔径阵列有色噪声场中目标检测问题,该文提出了一种基于目标方位似然估计的自适应匹配滤波器(ML-AMF)方法,该方法采用似然方法估计目标方位,并使用预先估计的方位信息进行能量检测,通过推导得到检验统计量。该方法克服了自适应匹配滤波器(AMF)目标方位失配带来的影响。仿真和实测数据的结果均验证了该方法在水下色噪声场中的有效性。8元均匀小孔径线列阵湖试数据的仿真结果表明,ML-AMF的检测性能优于MVDR 1~5 dB,优于CBF 12~17 dB。     

多传感器自适应滤波融合算法

该文提出了一种在线调整权值的多传感器自适应滤波数据融合跟踪算法,用于解决复杂背景下机动目标跟踪问题。首先自适应寻找各个传感器所对应的最优加权因子,确定融合后某一时刻目标最优观测值;其次,以输入信号作为相关自适应滤波器的观测信号,通过新息相关自适应滤波算法根据状态方程及观测方程中误差的变化,实时动态地调整增益矩阵,同时依据自适应滤波状态偏差输出信号及当前观测数据,应用模糊推理在线调整各传感器权值,最终系统输出即为测量轨迹在两级自适应调整融合下最优轨迹。仿真结果证明了算法有效性。     

Horizontal array gain in shallow water

Some results of a comprehensive model study on the use of horizontal hydrophone arrays in shallow water are presented. Well-known signal processing techniques are applied to a shallow-water sound-propagation model in order to investigate the particular influence of shallow-water conditions on the design of spatial receiver structures. A great variety of array processors (optimum, suboptimum, quadratic, linear, adaptive, non-adaptive) are considered. Detection of targets in presence of directive noise sources (e.g. ships) is of particular interest. Some conclusions concerning array processor design for real-time operation are drawn.     

一种用于水听器电压检测的模拟前端电路

提出了一种用于水听器电压检测的模拟前端电路,包括低噪声低失调斩波运算放大器,跨导电容(gm-C)低通滤波器,增益放大器三部分主体电路;低噪声低失调斩波运算放大器用于提取水听器前端传感器输出的微弱电压信号;gm-C低通滤波器用于滤除电压信号频率外的高频噪声和高次谐波;最后经过增益放大器放大至后级模数转换器的输入电压范围,输出数字码流;芯片采用台积电(TSMC)0.18μm单层多晶硅六层金属(1P6M)CMOS工艺实现。测试结果表明,在电源电压1.8 V,输入信号25 kHz和200 kHz时钟频率下,斩波运放输入等效失调电压小于110μV;整体电路输出信号动态范围达到80 dB,功耗5.1 mW,满足水听器的检测要求。     

An adaptive Kalman filter for ECG signal enhancement

The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.     

水面声光耦合光纤水听器的抗波浪解调方法

为了减弱基于水面声光耦合的光纤水听器水面波动对解调信号造成的幅度衰落,采用了利用检测信号臂光强度信息补偿解调信号幅度的方法。从理论上推导了该方法修正解调信号的过程,并对该过程进行了数值仿真,同时实际搭建光纤水听器探测系统,利用/2相位解调法对水面恒定振动信号进行了实验验证。结果表明,相比原始解调信号,修正后的信号幅度基本稳定,幅度值整体提高5dB,解调信号失真度减小,相位噪声得到抑制,信噪比得到提高,可辨认信号时间长度扩展为原来的3.5倍。该方案能有效克服由水面波动引起的解调信号衰落现象,信号质量得到优化,系统抗波浪解调能力得到提高。     

自适应线谱增强器的改进及实现

针对舰船辐射噪声中线谱检测及提取难问题,研究了基于矢量水听器的自适应线谱增强器。利用矢量水听器同步、同点测量声场的声压和振速的特点,将传统自适应线谱增强器改进为双输入结构,由于所测声压信号与振速信号间的相关性,提高了线谱增强的效果。通过实测数据对改进算法进行了验证,并在Simulink中的DSP Bulider平台下对改进的自适应线谱增强算法予以实现。     

Noise emissions sources direction-finding in the process of their background air threats detection in radars with phased antenna array

We analyze two options of combined systems for spatial signal processing in radars with phased antenna array, where the detection of noiseless point targets with background noise emissions is accompanied by the direction finding of their sources. In the first option the direction finding of noise emissions is based on the shape analysis of the adaptive radiation pattern of the phased antenna array, formed during the adaptive target finding with background noise emissions. In the second option the bearing angles are determined on the basis of maxima of “spectral functions” of different evaluations of correlation matrix, formed on the basis of input readings. An example how to build such a combined system for spatial signal processing based on general adaptive grid filter is presented. It is shown that the effect of simultaneous target finding in external noise background and direction finding of their sources is achieved by single utilization of the most complex tuning operation of the adaptive grid filter. This operation is same for both of these tasks, and it is easier comparing to solving these two problems separately.     

Applications of detection and estimation theory to large array seismology

The statistical theory of signal detection and estimation has been applied to problems in large array seismology. Using this theory the structure of the optimum detector for a known signal and long observation time in additive Gaussian noise is derived. The array processing filter employed by the optimum detector is known as the maximum-likelihood filter. This filter also has the property that it provides a minimum-variance unbiased estimate for the input signal when it is not known, which is the same as the maximum-likelihood estimate of the signal if the noise is a multidimensional Gaussian process. A series of experiments was performed using data from the large aperture seismic array to determine the effectiveness of the maximum-likelihood method relative to simpler methods such as beam-forming. These results provide significant conclusions regarding the design and processing of data from large seismic arrays. The conventional and high-resolution estimation of the frequency-wavenumber spectrum of the background microseismic noise is also presented. The diffuse structure of this spectrum is shown to aid in explaining the relative performance of array processing methods.     

Adaptive digital notch filter design on the unit circle for theremoval of powerline noise from biomedical signals

Investigates adaptive digital notch filters for the elimination of powerline noise from biomedical signals. Since the distribution of the frequency variation of the powerline noise may or may not be centered at 60 Hz. Three different adaptive digital notch filters are considered. For the first case, an adaptive FIR second-order digital notch filter is designed to track the center frequency variation. For the second case, the zeroes of an adaptive IIR second-order digital notch filter are fixed on the unit circle and the poles are adapted to find an optimum bandwidth to eliminate the noise to a pre-defined attenuation level. In the third case, both the poles and zeroes of the adaptive IIR second-order filter are adapted to track the center frequency variation within an optimum bandwidth. The adaptive process is considerably simplified by designing the notch filters by pole-zero placement on the unit circle using some suggested rules. A constrained least mean-squared algorithm is used for the adaptive process. To evaluate their performance, the three adaptive notch filters are applied to a powerline noise sample and to a noisy EEG as an illustration of a biomedical signal     

Reduction of noise in AVHRR channel 3 data with minimum distortion

The channel 3 data of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of weather satellites (NOAA 6-12) are contaminated by instrumentation noise. The signal to noise ratio (S/N) varies considerably from image to image and the between sensor variation in S/N can be large. The characteristics of the channel noise in the image data are examined using Fourier techniques. A Wiener filtering technique is developed to reduce the noise in the channel 3 image data. The noise and signal power spectra for the Wiener filter are estimated from the channel 3 and channel 4 AVHRR data in a manner which makes the filter adaptive to observed variations in the noise power spectra. Thus, the degree of filtering is dependent upon the level of noise in the original data and the filter is adaptive to variations in noise characteristics. Use of the filtered data to improve image segmentation, labeling in cloud screening algorithms for AVHRR data, and multichannel sea surface temperature (MCSST) estimates is demonstrated. Examples also show that the method can be used with success in land applications. The Wiener filtering model is compared with alternate filtering methods and is shown to be superior in all applications tested