Mean–Standard Deviation Representation of Sonar Images for Echo Detection: Application to SAS Images

This paper addresses the detection of underwater mines echoes with application to synthetic aperture sonar (SAS) imaging. A detection method based on local first- and second-order statistical properties of the sonar images is proposed. It consists of mapping the data onto the mean-standard deviation plane highlighting these properties. With this representation, an adaptive thresholding of the data enables the separation of the echoes from the reverberation background. The procedure is automated using an entropy criterion (setting of a threshold). Applied on various SAS data sets containing both proud and buried mines, the proposed method positively compares to the conventional amplitude threshold detection method. The performances are evaluated by means of receiver operating characteristic (ROC) curves.     

Active sonar detection in shallow water using the Page test

The use of active sonar in shallow water results in received echoes that may be considerably spread in time compared to the resolution of the transmitted waveform. The duration and structure of the spreading and the time of occurrence of the received echo are unknown without accurate knowledge of the environment and a priori information on the location and reflection properties of the target. A sequential detector based on the Page test is proposed for the detection of time-spread active sonar echoes. The detector also provides estimates of the starting and stopping times of the received echo. This signal segmentation is crucial to allow further processing such as more accurate range and bearing localization, depth localization, or classification. The detector is designed to exploit the time spreading of the received echo and is tuned as a function of range to the expected signal-to-noise ratio (SNR) as determined by the transmitted signal power, transmission loss, approximate target strength, and the estimated noise background level. The theoretical false alarm and detection performance of the proposed detector, the standard Page test, and the conventional thresholded matched filter detector are compared as a function of range, echo duration, SNR, and the mismatch between the actual and assumed SNR. The proposed detector and the standard Page test are seen to perform better than the conventional thresholded matched filter detector as soon as the received echo is minimally spread in time. The use of the proposed detector and the standard Page test in active sonar is illustrated with reverberation data containing target-like echoes from geological features, where it was seen that the proposed detector was able to suppress reverberation generated false alarms that were detected by the standard Page test     

"Principal component inverse" algorithm for detection in thepresence of reverberation

Detection in the presence of reverberation is often difficult in active sonar, due to the reflection/diffusion/diffraction of the transmitted signal by the ocean surface, ground, and volume. A modelization of reverberation is often used to improve detection because classical algorithms are inefficient. A commonly used reverberation model is colored and nonstationary noise. This model leads to elaborate detection algorithms which normalize and whiten reverberation. In this paper, we focus on a more deterministic model which considers reverberation as a sum of echoes issued from the transmitted signal. The Principal Component Inverse (PCI) algorithm is used with this model to estimate and delete the reverberation echoes. A rank analysis of the observation matrix shows that PCI is efficient in this configuration under some conditions, such as when the transmitted signal is Frequency Modulated. Both methods are validated with real sonar surface reverberation noise. We show that whitening has poor performance when reverberation and target echo have the same properties, while PCI maintains the same performance whatever the reverberation characteristics. Further, we extend the algorithms to spatio-temporal data. We propose a new algorithm for PCI which allows better echo separation. This new method is shown to be more efficient on real spatio-temporal data     

Signal excess in K-distributed reverberation

Active sonar systems have recently been developed using larger arrays and broad-band sources to counter the detrimental effects of reverberation in shallow-water operational areas. Increasing array size and transmit waveform bandwidth improve the signal-to-noise ratio-and-reverberation power ratio (SNR) after matched filtering and beamforming by reducing the size of the range-bearing resolution cell and, thus, decreasing reverberation power levels. This can also have the adverse effect of increasing the tails of the probability density function (pdf) of the reverberation envelope, resulting in an increase in the probability of a false alarm. Using a recently developed model relating the number of scatterers in a resolution cell to a K-distributed reverberation envelope, the effect of increasing bandwidth (i.e., reducing the resolution cell size) on detection performance is examined for additive nonfluctuating and fluctuating target models. The probability of detection for the two target models is seen to be well approximated by that for a shifted gamma variate with matching moments. The approximations are then used to obtain the SNR required to meet a probability of detection and false-alarm performance specification (i.e., the detection threshold). The required SNR is then used to determine that, as long as the target and scatterers are not over-resolved, decreasing the size of the resolution cell always results in an improvement in performance. Thus, the increase in SNR obtained by increasing bandwidth outweighs the accompanying increase in false alarms resulting from heavier reverberation distribution tails for K-distributed reverberation. The amount of improvement is then quantified by the signal excess, which is seen to be as low as one decibel per doubling of bandwidth when the reverberation is severely non-Rayleigh, as opposed to the expected 3-dB gain when the reverberation is Rayleigh distributed.     

Experience in measuring the wind-velocity profile in an urban environment with a Doppler sodar

The experience of long-term acoustic remote measurements of vertical wind-velocity profiles at two sites in Moscow is reported. Equipment performances and measurement conditions are described. Acoustic measurement features characteristic of a large city with high traffic noise and spurious reflections from buildings are discussed. Criteria and techniques of rejecting noisy and false signals are described as well as the methods of statistical data processing suitable in the case of a signal-to-noise ratio rapidly varying in time and a significant number of rejected signals. Preliminary results of measurements are given.     

Signal recovery in a reverberation-limited environment

The problem of recovering signals masked by reverberation is considered. Reverberation data from a shallow-water active sonar experiment in conjunction with simulated echoes are used to examine the potential for signal recovery offered by adaptive filtering and prediction. The deterministic least squares lattice filter is the central adaptive estimator of choice. The prediction error lattice is used to selectively "whiten" the composite process by controlling the algorithm adaptation speed. This is shown to result in significant signal enhancement for low-Doppler echoes masked by reverberation. Adaptive noise canceling with multiple reference beams is shown to be successful in extracting even zero-Doppler echoes from the reverberation background.     

一种新的用于极化SAR图像船只检测的散射相似性测度

本文提出了一种新的用于极化合成孔径雷达（SAR）海上船只目标检测的测度。首先利用散射相似性参数研究船只与海杂波散射机制的差异。基于这些差异,提出了一种新的船只检测测度,该测度能够有效区分船只目标和海杂波。然后提出了利用核密度估计（KDE）方法对该测度进行建模的方法。基于统计模型,实现了自适应恒虚警率（CFAR）的检测方案。本文利用多景C波段RADARSAT-2极化SAR（Pol-SAR）数据上进行实验,系统分析了所提出测度的海杂波拟合性能与船只检测性能,并与两个经典的极化测度进行了比较,实验和比较结果证明了所提的测度的可行性。     

多波束测深海底混响仿真研究

海底混响是海洋混响的重要组成部分,采用模拟仿真进行验证分析,是仿真技术的一项重要应用。采用单元散射模型,研究在单发射阵元下,分布在同一直线上多个接收阵元接收的海底回波,忽略声波传播的相位起伏,只考虑振幅起伏,将传播损失、声吸收系数、海底反射损失、海底沉积层密度等参数带入海底混响仿真数学模型,仿真海底混响,使其更加接近海底的实际情况。     

基于风险的海洋结构物无损检测功能的分级

提出了基于风险的海洋结构物无损检测功能分级方法,采用检测概率、错误识别概率和裂纹出现概率度量检测功能,并修正了错误识别概率的取值范围。根据风险值对无损检测功能进行了分级,研究了风险值随检测概率、错误识别概率和裂纹出现概率的变化规律,分析了风险值对检测费用、维修费用及失效费用的敏感性。在此基础上,将基于风险的海洋结构物无损检测功能分级方法应用于优化检测策略,用算例证明了文中所提方法的有效性。     

Design of chemical sensor arrays for monitoring disposal sites onthe ocean floor

We develop methods for automatic environmental monitoring of disposal sites on the deep ocean floor using chemical sensor arrays and statistical hypothesis testing. Such sites have been proposed to relocate dredge materials from harbors and shipping channels. The transport of pollutants is modeled as a diffusion process, and the measurement and statistical models are derived by exploiting the spatial and temporal evolution of the associated concentration distribution. We derive two detectors, the generalized likelihood ratio (GLR) test and the mean detector and determine their performance in terms of the probabilities of false alarm and detection, The results are applied to the design of chemical sensor arrays satisfying criteria specified in terms of these probabilities, and to optimally select a number of sensors and time samples. Numerical examples are used to demonstrate the applicability of our results     

基于LabVIEW的深水网箱鱼群监测系统的信号处理

深水网箱鱼群活动状态的实时监测是开展深水网箱渔业资源可持续发展中急需解决的关键技术。文中介绍了深水网箱鱼群监测系统中基于LabVIEW的数据信号处理方法。该方法采用软件编程技术取代传统的硬件电路设计,实现了深水网箱鱼群监测系统的时间增益补偿、信号相关处理、图像处理和鱼群量估计等功能,海上现场实验证明系统具有较高的抗噪声和混响干扰能力,鱼群回波图像显示简洁、海上操作方便,采用鱼群回波能量积分方法能较好地估计鱼群量大小。     

Application of neural and statistical classifiers to the problem ofseafloor characterization

In this paper neural and statistical classifiers are applied to the problem of seafloor classification. The feature vectors used consist of acoustic backscatter as a function of angle of incidence. Simulated seafloor backscatter is obtained by employing the Helmholtz-Kirchhoff approximation and the statistical properties of bottom reverberation. These synthetic data are used initially to train multilayer perceptrons and then to test them for their ability to discriminate among signal returns produced by seafloors with different roughness parameters. The same data are also processed with optimum Bayesian classifiers. A comparison of the results indicates a suboptimum performance for the perceptrons. The same procedures are applied to real data collected by the Sea Beam bathymetric system over two Central North Pacific seamounts. In this case, the perceptron performance is similar to that of the statistical classifier, which is no longer optimum, since no prior knowledge of the probability distribution parameters is available. In addition, Self Organizing Maps are applied to both synthetic and real data and are shown to result in a successful separation of the output space into distinct regions corresponding to different seafloor classes     

Specific features of reverberation in the Black-Sea underwater sound channel

We consider the results of instrumental investigations of specific features of reverberation in the Black Sea and obtain qualitative dependences of the duration of volume reverberation on the parameters of the Black-Sea underwater sound channel, (width, drop of the sound velocity, and dimensions of the inhomogeneities of stratification). We also analyse the behaviour of the intensity of surface reverberation in the far-field zone of acoustic illumination and the influence of bottom reverberation on the detection of underwater objects. Translated by Peter V. Malyshev and Dmitry V. Malyshev     

Statistical characterization of active sonar reverberation using extreme value theory

The statistics of reverberation in active sonar are characterized by non-Rayleigh distributed amplitudes in the normalized matched filter output. Unaccounted for, this property can lead to high false-alarm rates in fixed-threshold detectors. A new approach to modeling threshold-crossing statistics based on extreme value theory is proposed, which uses the generalized Pareto distribution as the unique asymptotic model of the tail distribution, valid at large thresholds. Methods of parameter estimation are discussed and applied to active sonar reverberation collected on a hull-mounted sonar system. The statistics of reverberation in active sonar are found to generally have a power-law behavior in the tails with a shape parameter that is persistent in time and bandwidth dependent. The threshold needed for accurate parameter estimation is generally found to be well below that of typical fixed-threshold detectors.     

基于窄带混合高斯噪声的最佳检测

论文首先研究高斯柯西卷积噪声模型和窄带混合高斯噪声模型用来对混响建模,前者主要用于仿真分析,而后者因其参数有明确物理意义且可通过样本序列估计得到,因此被用于海洋的工程实现.文章重点研究混合高斯噪声背景下确知信号的非高斯最佳检测器的构造,并对检测器进行了仿真.验证结果表明混合高斯噪声背景下确知信号的非高斯最佳检测器检测性能优于常规的匹配滤波器3～4.5 dB.     

Pulse-length-tolerant features and detectors for sector-scan sonar imagery

This paper presents a neural-network-based system to detect small man-made objects in sequences of sector-scan sonar images created using signals of various pulse lengths. The detection of such objects is considered out to ranges of 150 m by using an experimental sector-scan sonar system mounted on a vessel. The sonar system considered in this investigation has three modes of operation to create images over ranges of 200, 400, and 800 m from the vessel using acoustic pulses of a different duration for each mode. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery is segmented to extract objects for analysis. A set of 31 features extracted from each object is examined. These features consist of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. Optimal sets of 15 features are then selected for each mode and over all modes using sequential forward selection (SFS) and sequential backward selection (SBS). These features are then used to train neural networks to detect man-made objects in each sonar mode. By the addition of a feature describing the sonar's mode of operation, a neural network is trained to detect man-made objects in any of the three sonar modes. The multimode detector is shown to perform very well when compared with detectors trained specifically for each sonar mode setting. The proposed detector is also shown to perform well when compared to a number of statistical detectors based on the same set of features. The proposed detector achieves a 92.4% probability of detection at a mean false-alarm rate of 10 per image, averaged over all sonar mode settings.     

Phenomenological and global optimization inversion

This paper discusses geoacoustic inversion results based on benchmark range-dependent data using SAGA, a global inversion package, and using phenomenological inversions. In phenomenological inversions, physical and signal-processing approaches are used to enhance the data to extract specific features. The global optimization approach is carried out on complex-valued vertical array data, transmission loss data, and reverberation data. The importance of checking the solution is emphasized by inspecting the match with the data and the error estimates and by checking the solution using data that has not been used in constructing the solution. The results show that we are able to estimate the geoacoustic parameters and that these parameters could be used to predict the field for different frequencies and/or source-receiver geometry than used in the inversion.     

Validation of AltiKa Matching Pursuit Rain Flag

The major drawback of Ka band, operating frequency of the AltiKa altimeter on board SARAL, is its sensitivity to atmospheric liquid water. Even light rain or heavy clouds can strongly attenuate the signal and distort the signal leading to erroneous geophysical parameters estimates. A good detection of the samples affected by atmospheric liquid water is crucial. As AltiKa operates at a single frequency, a new technique based on the detection by a Matching Pursuit algorithm of short scale variations of the slope of the echo waveform plateau has been developed and implemented prelaunch in the ground segment. As the parameterization of the detection algorithm was defined using Jason-1 data, the parameters were re-estimated during the cal-val phase, during which the algorithm was also updated. The measured sensor signal-to-noise ratio is significantly better than planned, the data loss due to attenuation by rain is significantly smaller than expected (<0.1%). For cycles 2 to 9, the flag detects about 9% of 1Hz data, 5.5% as rainy and 3.5 % as backscatter bloom (or sigma0 bloom). The results of the flagging process are compared to independent rain data from microwave radiometers to evaluate its performances in term of detection and false alarms.     

Statistics of broad-band bottom reverberation predictions in shallow-water waveguides

A new coherent reverberation model developed at the Naval Research Laboratory, Washington, DC, and the Supreme Allied Commander Atlantic Undersea Research Centre, La Spezia, Italy, is exercised in the 17-750-Hz band to estimate the degree of non-Rayleighness of shallow-water reverberation envelopes as a function of waveguide multipath, system bandwidth, directivity, and frequency. Findings suggest that reverberation from diffuse, but non-Gaussian, scatterer distributions is significantly more Rayleigh for multipath environments than for equivalent environments excited by a single or small number of modes or for broadside receiver array processing that extracts narrow angles of reception. These findings suggest that the problem of non-Rayleigh reverberation in shallow-water waveguides can be ameliorated through the use of tuned ensonification and reception schemes, which retain high probabilities of detection while reducing the associated probability of false alarm.     

Non-Rayleigh acoustic scattering characteristics of individual fish and zooplankton

It has long been known that the statistical properties of acoustic echoes from individual fish can have non-Rayleigh characteristics. The statistical properties of echoes from zooplankton are generally less understood. In this study, echoes from individual fish and zooplankton from a series of laboratory measurements from the past decade are investigated. In the experiments, acoustic echoes from various individual organisms were measured over a wide range of frequencies and orientations, typically in 1/spl deg/-3/spl deg/ increments. In the analysis in this paper, the echoes from most of those measurements are grouped according to ranges of orientation, which correspond to typical orientation distributions of these organisms in the natural ocean environment. This grouping provides a distribution of echo values for each range of orientation. This approach, in essence, emulates a field experiment whereby distributions of echoes would be recorded for different distributions of orientations of the organisms. For both the fish and zooplankton data, there are conditions under which the echoes are strongly non-Rayleigh distributed. In some cases, the distribution is quantitatively connected to the physics of the scattering process while, in other cases, the connection is described qualitatively. Exploitation of the animal-specific statistics for classification purposes is suggested.