共查询到20条相似文献，搜索用时 15 毫秒
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介绍了多波束测深系统和侧扫声纳系统的工作原理,通过实例说明了多波束测深系统和侧扫声纳系统在海底目标探测的工作流程,总结出两种探测系统在探测海底目标上的优缺点,说明了多种探测手段的综合应用是海底目标探测技术的发展方向。 相似文献
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Dasgupta N. Runkle P. Carin L. Couchman L. Yoder T. Bucaro J. Dobeck G.J. 《Oceanic Engineering, IEEE Journal of》2003,28(2):271-282
In underwater sensing applications, it is often difficult to train a classifier in advance for all targets that may be seen during testing, due to the large number of targets that may be encountered. We therefore partition the training data into target classes, with each class characteristic of multiple targets that share similar scattering physics. In some cases, one may have a priori insight into which targets should constitute a given class, while in other cases this segmentation must be done autonomously based on the scattering data. For the latter case, we constitute the classes using an information-theoretic mapping criterion. Having defined the target classes, the second phase of our identification procedure involves determining those features that enhance the similarity between the targets in a given class. This is achieved by using a genetic algorithm (GA)-based feature-selection algorithm with a Kullback-Leibler (KL) cost function. The classifier employed is appropriate for multiaspect scattering data and is based on a hidden Markov model (HMM). The performance of the class-based classification algorithm is examined using both measured and computed acoustic scattering data from submerged elastic targets. 相似文献
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Canonical correlation analysis is employed as a multiaspect feature extraction method for underwater target classification. The method exploits linear dependence or coherence between two consecutive sonar returns, at different aspect angles. This is accomplished by extracting the dominant canonical correlations between the two sonar returns and using them as features for classifying mine-like objects from nonmine-like objects. The experimental results on a wideband acoustic backscattered data set, which contains sonar returns from several mine-like and nonmine-like objects in two different environmental conditions, show the promise of canonical correlation features for mine-like versus nonmine-like discrimination. The results also reveal that in a fixed bottom condition, canonical correlation features are relatively invariant to changes in aspect angle. 相似文献
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探雷声呐搜索水雷时会发现录取大量随机触点,导致分类辨识效率低下。 用海底微小目标的随机强反射特性解释随机触点的产生;运用聚类方法获得海底小目标的概率分布曲线,划分纯随机虚触点、 低重复虚目标和高重复似雷目标 3 个组成部分;提出直接剔除虚触点可有效提高辨识效率。 相似文献
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One of the most difficult challenges in shallow-water active sonar processing is false-alarm rate reduction via active classification. In impulsive-echo-range processing, an additional challenge is dealing with stochastic impulsive source variability. The goal of active classification is to remove as much clutter as possible while maintaining an acceptable detection performance. Clutter in this context refers to any non-target, threshold-crossing cluster event. In this paper, we present a clutter-reduction algorithm using an integrated pattern-recognition paradigm that spans a wide spectrum of signal and image processing-target physics, exploration of projection spaces, feature optimization, and mapping the decision architecture to the underlying good-feature distribution. This approach is analogous to a classify-before-detect strategy that utilizes multiple informations to arrive at the detection decision. After a thorough algorithm evaluation with real active sonar data, we achieved over an order of magnitude performance improvement in clutter reduction with our methodology over that of the baseline processing 相似文献
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The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities. 相似文献
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Duncan Tamsett 《Marine Geophysical Researches》1993,15(1):43-64
This work is concerned with the automatic characterisation and classification of the sea-bed for side-scan sonar trace power-spectra.A parametric model of side-scan sonar trace power-spectra is developed from the equation for the magnitude frequency response of a Butterworth filter. The model's parameters are optimised to give a least squares fit with observed spectra. Three of the optimised parameters are used to define features. 相似文献
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基于数学形态学的侧扫声呐图像轮廓自动提取 总被引：1，自引：0，他引：1
侧扫声呐图像特征自动提取的难点在于特征地貌边缘检测较困难,依据图像灰度突变检测得到的边缘比较粗糙、不连续,而且有断口和小洞。本文在对图像进行预处理和阈值化的基础上,采用数学形态学方法对图像进行处理,即用具有一定形态的结构元素去量度和提取图像中的对应形状,得到连续化、粗化、圆滑的特征区域边缘填充目标内部阴影且消除背景噪声。基于数学形态学的侧扫声呐图像特征自动提取的主要步骤为:首先对侧扫声呐图像进行预处理,然后进行灰度阈值化,接着采用数学形态学方法进行处理,最后对处理后的图像进行边缘检测,提取出特征地貌边缘。实验表明,采用数学形态学方法进行处理后,错断、离散的海底目标物变得连续,背景噪声大大减少,自动提取结果准确可靠。 相似文献
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An automatic approach to the detection and extraction of mine features in sidescan sonar 总被引：6，自引：0，他引：6
Mine detection and classification using high-resolution sidescan sonar is a critical technology for mine counter measures (MCM). As opposed to the majority of techniques which require large training data sets, this paper presents unsupervised models for both the detection and the shadow extraction phases of an automated classification system. The detection phase is carried out using an unsupervised Markov random field (MRF) model where the required model parameters are estimated from the original image. Using a priori spatial information on the physical size and geometric signature of mines in sidescan sonar, a detection-orientated MRF model is developed which directly segments the image into regions of shadow, seabottom-reverberation, and object-highlight. After detection, features are extracted so that the object can be classified. A novel co-operating statistical snake (CSS) model is presented which extracts the highlight and shadow of the object. The CSS model again utilizes available a priori information on the spatial relationship between the highlight and shadow, allowing accurate segmentation of the object's shadow to be achieved. 相似文献
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针对侧扫声呐图像斑点噪声强、背景海底散射干扰严重,海底目标轮廓自动提取困难的问题,提出了一种基于K-means聚类与数学形态学相结合的海底目标轮廓自动提取算法。为克服噪声干扰,该算法首先利用中值滤波去除侧扫声呐图像中的强斑点噪声;然后采用K-means聚类算法对侧扫声呐灰度图像进行分割,并二值化,除去大部分海底背景噪声,初步提取出目标;接着利用数学形态学运算去除提取结果中的孤立噪点,并填充目标内部孔洞,得到连续化、圆滑的目标边缘;最后对处理后的侧扫声呐图像进行边缘检测,提取出目标轮廓。实验结果表明:该算法思想简单易行,具有很强的克服背景噪声的能力,自动提取的目标轮廓连续性较好,结果准确可靠。目前,在侧扫声呐图像目标轮廓提取过程中,主要采用人工方式,自动性较差,效率较低。本文算法可以实现目标轮廓的自动提取,提高效率,具有较强的实用价值。 相似文献
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全极化合成孔径雷达(Synthetic Aperture Radar,SAR)数据具有丰富的极化信息,能够提取出大量异构性特征。核学习方法在解决小样本、高维特征分类问题上具有优势,但异构特征对不同核函数具有响应差异。本文利用一种引入先验标签的多核学习方法进行全极化SAR的溢油信息提取,即基于分析结果对特征集进行遴选与组合,分别在每个特征组合中训练得到一个预备层核函数,以新获取的预备层核函数作为新的底层核函数,对全部特征进行学习分类。通过提取与分析溢油和海水的统计特征、物理散射特征和纹理特征,建立溢油全极化SAR特征谱,并利用引入先验标签的多核学习分类器进行溢油提取实验。结果表明,该方法能够利用全极化SAR多维异构特征的互补特性有效提高溢油分类提取精度。 相似文献
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Geometric distortions in side-scan sonar images: a procedure fortheir estimation and correction 总被引：1，自引：0，他引：1
A procedure is introduced for the estimation and correction of geometric distortions frequently observed in side-scan sonar images as a result of motion instabilities of the sonar towfish. This procedure estimates geometric distortions from the image itself, without requiring navigational or altitude measurements. Estimates of the local degree of geometric distortion are obtained by cross-correlating segments of adjacent lines of the image. A mathematical model for the distortions is derived from the geometry of the problem and is applied to these estimates to reconstruct the sampling pattern on the seabed, under the assumption of a planar bottom. The estimated sampling pattern is then used for resampling the image to correct the geometric distortions. The model parameters may also be used for calculating approximate estimates of the attitude parameters of the towfish. A simulation is employed to evaluate the effectiveness of this technique and examples of its application to high-resolution side-scan sonar images are provided 相似文献
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Autonomous underwater vehicle-based concurrent detection and classification of buried targets using higher order spectral analysis 总被引：1，自引：0，他引：1
This paper presents a processing concept for autonomous underwater vehicle (AUV)-based concurrent detection and classification (CDAC) of mine-like objects. In the detection phase, the AUV seeks objects of interest using a simple energy detector combined with a peak tracking mechanism. Upon detection, the processing mechanism changes to a higher order spectral (HOS) classification process. The system is demonstrated through theory, simulation and at-sea experiments to have promise in reducing the false alarm rate of mine detections. The HOS classification mechanism is also shown to have some benefit over classical spectral estimation in all cases. Components of the system concept were also demonstrated live onboard the AUV during the Generic Oceanographic Array Technology Sonar (GOATS 2002) experiment off the coast of Italy, while others are demonstrated using a comprehensive AUV sonar simulation framework. 相似文献
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In ocean surveillance, a number of different types of transient signals are observed. These sonar signals are waveforms in one dimension (1-D). The hidden Markov model (HMM) is well suited to classification of 1-D signals such as speech. In HMM methodology, the signal is divided into a sequence of frames, and each frame is represented by a feature vector. This sequence of feature vectors is then modeled by one HMM. Thus, the HMM methodology is highly suitable for classifying the patterns that are made of concatenated sequences of micro patterns. The sonar transient signals often display an evolutionary pattern over the time scale. Following this intuition, the application of HMM's to sonar transient classification is proposed and discussed in this paper. Toward this goal, three different feature vectors based on an autoregressive (AR) model, Fourier power spectra, and wavelet transforms are considered in our work. In our implementation, one HMM is developed for each class of signals. During testing, the signal to be recognized is matched against all models. The best matched model identifies the signal class. The neural net (NN) classifier has been successfully used previously for sonar transient classification. The same set of features as mentioned above is then used with a multilayer perceptron NN classifier. Some experimental results using “DARPA standard data set I” with HMM and MLP-NN classification schemes are presented. A combined NN/HMM classifier is proposed, and its performance is evaluated with respect to individual classifiers 相似文献