共查询到20条相似文献，搜索用时 15 毫秒
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The results from an investigation of an analytically based method for determining the performance of echo classifiers are presented. In particular, the problem of classifying echo waveforms reflected from objects that are composed of multiple scatterers is considered. The time delays between the multiple echo returns from the individual scattering centers that characterize an object are investigated as features. A generic stochastic point scatterer model is developed for representing the classes of reflecting objects which are of interest. The model allows for uncertainty in prior knowledge about the exact relative location of the individual component scatterers or uncertainty in the delay measurements. A classifier decision algorithm, in the form of a general optimum Bayesian binary classification decision rule suitable for a large variety of classification problems, is derived for the case when the orientation of the reflecting object is known. The case of unknown aspect angle is addressed through the numerical implementation and analysis of two classifiers. The associated performance for all three classifiers is obtained in terms of the probability of error and tied to standard sonar equation parameters. Example binary classification problems are presented and analyzed and some general observations made. A pragmatic framework is established within which complex echo classification issues can be further examined 相似文献
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Chunhua Yuan Azimi-Sadjadi M.R. Wilbur J. Dobeck G.J. 《Oceanic Engineering, IEEE Journal of》2000,25(1):192-205
In this paper, new pre- and post-processing schemes are developed to process shallow-water sonar data to improve the accuracy of target detection. A multichannel subband adaptive filtering is applied to preprocess the data in order to isolate the potential target returns from the acoustic backscattered signals and improve the signal-to-reverberation ratio. This is done by estimating the time delays associated with the reflections in different subbands. The preprocessed results are then beamformed to generate an image for each ping of the sonar. The testing results on both the simulated and real data revealed the efficiency of this scheme in time-delay estimation and its capability in removing most of the competing reverberations and noise. To improve detection rate while significantly minimizing the incident of false detections, a high-order correlation (HOC) method for postprocessing the beamformed images is then developed. This method determines the consistency in occurrence of the target returns in several consecutive pings. The application of the HOC process to the real beamformed sonar data showed the ability of this method for removing the clutter and at the same time boosting the target returns in several consecutive pings. The algorithm is simple, fast, and easy to implement 相似文献
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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. 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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Daniel S. Le Leannec F. Roux C. Soliman B. Maillard E.P. 《Oceanic Engineering, IEEE Journal of》1998,23(3):245-259
This paper presents a method for the matching of underwater images acquired with acoustic sensors. As a final objective, the system aims at matching data from two-dimensional scenes. The proposed approach carries out a hypothetical reasoning based on objects, represented by shadows and echoes in the sonar images, and their available features. The problem of determining measures which are invariant to changes in sonar settings and noise characteristics is addressed by mapping robust features for sonar images to a qualitative representation. To cope with the viewpoint charging appearance, the method is based on the conservation of objects' relative position from one image to another. We attempt to match geometrical structures formed by the association of three objects. The hypothetical reasoning is conducted in a decision tree framework. A tree node is generated by two objects' association, each one belonging to a respective image. Hypotheses propagation consists of creating new nodes from neighboring associations. The matching solution is determined by the selection of the decision tree's longest branch. Thus, the association mechanism is a depth-first procedure. The proposed method has been applied to real high-resolution side-scan sonar images. The matching process has shown successful and promising results which have been further improved. In particular, the parceled shadows (during the segmentation procedure) problem has been tackled 相似文献
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Panoramic sweeps produced by a scanning range sensor often defy interpretation using conventional line-of-sight models, particularly when the environment contains curved, specularly reflective surfaces. Combining multiple scans from different vantage points provides geometric constraints necessary to solve this problem, but not without introducing new difficulties. Existing multiple scan implementations, for the most part, ignore the data correspondence issue. The multiple hypothesis tracking (MHT) algorithm explicitly deals with data correspondence. Given canonical observations extracted from raw scans, the MHT applies multiple behavior models to explain their evolution from one scan to the next. This technique identifies different topological features in the world to which it assigns the corresponding measurements. We apply the algorithm to real sonar scans generated specifically for this investigation. The experiments consist of interrogating a variety of two-dimensional prismatic objects, standing on end in a 1.2-m-deep freshwater tank, from multiple vantage points using a 1.25 MHz profiling sonar system. The results reflect the validity of the algorithm under the initial assumptions and its gradual performance degradation when these assumptions fail to characterize the environment adequately. We close with recommendations that detail extending the approach to handle more natural underwater settings 相似文献
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Dura E. Yan Zhang Xuejun Liao Dobeck G.J. Carin L. 《Oceanic Engineering, IEEE Journal of》2005,30(2):360-371
A data-adaptive algorithm is presented for the selection of the basis functions and training data used in classifier design with application to sensing mine-like targets with a side-scan sonar. Automatic detection of mine-like targets using side-scan sonar imagery is complicated by the variability of the target, clutter, and background signatures. Specifically, the strong dependence of the data on environmental conditions vitiates the assumption that one may perform a priori algorithm training using separate side-scan sonar data collected previously. In this paper, a novel active-learning algorithm is developed based on kernel classifiers with the goal of enhancing detection/classification of mines without requiring an a priori training set. It is assumed that divers and/or unmanned underwater vehicles (UUVs) may be used to determine the binary labels (target/clutter) of a small number of signatures from a given side-scan collection. These sets of signatures and associated labels are then used to train a kernel-based algorithm with which the remaining side-scan signatures are classified. Information-theoretic concepts are used to adaptively construct the form of the kernel classifier and to determine which signatures and associated labels would be most informative in the context of algorithm training. Using measured side-looking sonar data, the authors demonstrate that the number of signatures for which labels are required (via diver/UUV) is often small relative to the total number of potential targets in a given image. This procedure designs the detection/classification algorithm on the observed data itself without requiring a priori training data and also allows adaptation as environmental conditions change. 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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《Oceanic Engineering, IEEE Journal of》1997,22(1):40-46
Sector-scanning sonar systems image the sea bottom to detect objects that can be distinguished from the background structure of the sea bottom. In current systems, images are displayed and discarded as new image data become available, In this paper, a method for improving sonar detection by utilizing all images in a sequence is investigated. The proposed method requires that sonar data are acquired with a sector-scanning sonar in a side-looking configuration. It is demonstrated that these data can be used to detect observation-point-dependent changes in sea-bottom backscattering characteristics. These changes provide additional cues for discrimination that can improve the detection of objects on the sea bottom. Results of applying the method to experimental data are presented 相似文献
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Image processing techniques are discussed that correct distortions in GLORIA II side scan sonar imagery including water column
offset, slant-range distortion, multiple returns, aspect ratio, speckle noise, striping, and cross-track power drop-off. The
software operates within NASA's ELAS image processing system and is applied to the original 12-bit GLORIA II data. Procedures
are discussed for generating large scale mosaics and three-dimensional overlays with sea floor bathymetry. The results are
shown in four sonographs acquired off the southern coast of California. 相似文献
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A field experiment was carried out to examine the time variation of scattering from man-made objects placed near the water-sediment interface and within the sediment. The objects (spheres) were monitored for a period of about two months using a sonar system capable of measuring scattering levels, bottom bathymetry, and correlation of scattering over time. In addition, divers performed focalized biological treatments that were also monitored over extended periods. The results of these monitoring activities are presented and related to previous studies that used the same data sets for other purposes. One notable result is that the buried sphere becomes undetectable (by scattering level alone) within two days of deployment. The rapid changes in the first few days after the buried sphere is introduced are quantified relative to the rate of changes for undisturbed regions of the sediment 相似文献
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A new pattern-recognition algorithm detects approximately 90% of the mines hidden in the Coastal Systems Station Sonar0, 1, and 3 databases of cluttered acoustic images, with about 10% false alarms. Similar to other approaches, the algorithm presented here includes processing the images with an adaptive Wiener filter (the degree of smoothing depends on the signal strength in a local neighborhood) to remove noise without destroying the structural information in the mine shapes, followed by a two-dimensional FIR filter designed to suppress noise and clutter, while enhancing the target signature. A double peak pattern is produced as the FIR filter passes over mine highlight and shadow regions. Although the location, size, and orientation of this pattern within a region of the image can vary, features derived from higher order spectra (HOS) are invariant to translation, rotation, and scaling, while capturing the spatial correlations of mine-like objects. Classification accuracy is improved by combining features based on geometrical properties of the filter output with features based on HOS. The highest accuracy is obtained by fusing classification based on bispectral features with classification based on trispectral features. 相似文献
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Automatic Registration of TOBI Side-Scan Sonar and Multi-Beam Bathymetry Images for Improved Data Fusion 总被引：1，自引：0，他引：1
Deep towed side-scan sonar vehicles such as TOBI acquire high quality imagery of the seafloor with very high spatial resolution but poor locational accuracy. Fusion of the side-scan sonar data with bathymetry data from an independent source is often desirable to reduce ambiguity in geological interpretations, to aid in slant-range correction and to enhance seafloor representation. The main obstacle to fusion is accurate registration of the two datasets.The application of hierarchical chamfer matching to the registration of TOBI side-scan sonar images and multi-beam swath bathymetry is described. This matches low level features such as edges in the TOBI image, with corresponding features in a synthetic TOBI image created by simulating the flight of the TOBI vehicle through the bathymetry. The method is completely automatic, relatively fast and robust, and much easier than manual registration. It allows accurate positioning of the TOBI vehicle, enhancing its usefulness as a research tool. The method is illustrated by automatic registration of TOBI and multi-beam bathymetry data from the Mid-Atlantic Ridge. 相似文献
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Rongxing Li 《Marine Geodesy》2013,36(2-3):115-127
Shape from shading is one of the methods that derive geometric information of objects from analysis of monocular images. Application of this technique to underwater sonar images enables the conversion of imposed reflectance characteristics in sonar images to shape information, namely, slopes, about the seafloor surface. A combination of this shape information and available sparse distributed depth points results in improved dense bathymetric data. The reconstruction of shape models of seafloor surfaces from sonar images is treated as an inverse problem and is solved by the regularization theory. Sparse gridded points are used for boundary constraints. The regularization is implemented as a relaxation procedure with a hierarchical structure of multiresolution grids. 相似文献
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The Wigner-Ville distribution (WVD) function was originally proposed by Wigner in quantum mechanics and Ville applied it for signal analysis. This method has made it possible to represent a signal's power density spectrum in the time-frequency domain as a natural extension of the Fourier transform method (FTM). Recently, it has attracted great interest for its validity to analyze time-varying signals accomplished by the development of high-speed digital signal processing, and it is used for analyzing nonstationary signals. Conventionally, a sonar beamformer is constructed using delay lines, but the development of the high-speed processor has made it possible to apply the FTM for sonar beamforming. However, the bearing resolution of the beamformer is not enough for discriminating small underwater objects on the sea bottom by this method. To solve this problem, we aim to apply the WVD method, which can represent finer structure of signals as a natural extension of the FTM, for sonar beamforming to obtain sharper beam patterns than those of the beamforming method by FTM. Simulation results by computational calculations to clarify the resolution by the WVD method, which is presented in this paper, becomes approximately twice as high as by conventional FTM. The results of an experiment at sea also show the performance of this method 相似文献
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An active sonar is described that adaptively changes its location and configuration in response to the echoes it observes in order to locate an object, position it at a known location, and identify it using features extracted from the echoes. The sonar consists of a center transmitter flanked by two receivers that can rotate and is positioned at the end of a robot arm that has five degree-of-freedom mobility. The sonar operates in air using Polaroid transducers that are resonant at 60 kHz with a nominal wavelength equal to 6 mm. The emitted pulse has a short duration with a useful bandwidth extending from 20 to 130 kHz. Using binaural information, the transmitter rotates to position an echo-producing object on its axis to maximize the acoustic intensity incident on the nearest echo-producing feature. The receivers rotate to maximize the echo amplitude and bandwidth. These optimizations are useful for differentiating objects. The system recognizes a collection of ball bearings, machine washers, and rubber O-rings of different sizes ranging from 0.45 to 2.54 cm, some differing by less than 1 mm in diameter. Learning is accomplished by extracting vectors of 32 echo envelope values acquired during a scan in elevation and forming a data base. Recognition is accomplished by comparing a single observed echo vector with the data base to find the least squared error match. A bent-wire paper clip illustrates the recognition of an asymmetric pose-dependent object 相似文献
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The fast update rate and good performance of new generation electronic sector scanning sonars is now allowing practicable use of temporal information for signal processing tasks such as object classification and motion estimation. Problems remain, however, as objects change appearance, merge, maneuver, move in and out of the field of view, and split due to poor segmentation. This paper presents an approach to the segmentation, two-dimensional motion estimation, and subsequent tracking of multiple objects in sequences of sector scan sonar images. Applications such as ROV obstacle avoidance, visual servoing, and underwater surveillance are relevant. Initially, static and moving objects are distinguished in the sonar image sequence using frequency-domain filtering. Optical flow calculations are then performed on moving objects with significant size to obtain magnitude and direction motion estimates. Matches of these motion estimates, and the future positions they predict, are then used as a basis for identifying corresponding objects in adjacent scans. To enhance robustness, a tracking tree is constructed storing multiple possible correspondences and cumulative confidence values obtained from successive compatibility measures. Deferred decision making is then employed to enable best estimates of object tracks to be updated as subsequent scans produce new information. The method is shown to work well, with good tracking performance when objects merge, split, and change shape. The optical flow is demonstrated to give position prediction errors of between 10 and 50 cm (1%-5% of scan range), with no violation of smoothness assumptions using sample rates between 4 and 1 frames/s 相似文献