共查询到20条相似文献，搜索用时 437 毫秒
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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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Bottom-penetrating sonar can be used to visualize large areas, for example by normal logging and printing of collected pings. In many applications, it is necessary to obtain an impression of three-dimensional (3-D) structures, but this is not easy because of the irregular spatial sampling due to coarse ship trajectories. Normally, the ping map and the ping data, cover only a very small part of a region of interest. In this paper, we describe a new method for interpolating irregularly spaced sonar data. The basic idea is to use a two-dimensional quadtree of the ping map in order to guide the 3-D interpolation process: since gaps between pings become smaller at higher tree levels, the volume can be filled by marking neighborhood relations in the quadtree and interpolating available pings when they become neighbors. Different marking schemes and their central processing unit times are compared. In the interpolation process, we apply cross correlations of ping data in order to construct continuity of sloping reflections. Our results show that excellent results can be obtained on real sonar data sets, even for volumes filled for less than 7%, for which processing times are reasonable even for large areas, and that the interpolated data can be used for volumetric interactive visualization. 相似文献
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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 相似文献
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Passive sonar systems that localize broadband sources of acoustic energy estimate the difference in arrival times (or time delays) of an acoustic wavefront at spatially separated hydrophones, The output amplitudes from a given pair of hydrophones are cross-correlated, and an estimate of the time delay is given by the time lag that maximizes the cross correlation function. Often the time-delay estimates are corrupted by the presence of noise. By replacing each of the omnidirectional hydrophones with an array of hydrophones, and then cross-correlating the beamformed outputs of the arrays, the author shows that the effect of noise on the time-delay estimation process is reduced greatly. Both conventional and adaptive beamforming methods are implemented in the frequency domain and the advantages of array beamforming (prior to cross-correlation) are highlighted using both simulated and real noise-field data. Further improvement in the performance of the broadband cross-correlation processor occurs when various prefiltering algorithms are invoked 相似文献
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《Oceanic Engineering, IEEE Journal of》2006,31(2):345-355
Predicting sonar detection performance is important for the development of sonar systems. The classical sonar equation cannot accurately predict sonar detection performance because it does not incorporate the effect of ocean environmental and source position uncertainty. We propose an analytical receiver operating characteristic (ROC) expression that characterizes the performance of the optimal Bayesian detector in the presence of ocean environmental and source position uncertainty. The approach is based on a statistical model of the environment and a physical model of acoustic propagation, which translates ocean environmental and source position uncertainty to signal wavefront uncertainty. The analytical ROC expression developed in this paper is verified for source position uncertainty due to source motion using both simulated data and real data collected during the Shallow Water Evaluation Cell Experiment (SWellEx-96). The results showed that the primary effect of source position uncertainty on optimal sonar detection performance is captured by the rank that corresponds to the significant eigenvalues of the signal matrix, an ensemble of replica signal wavefronts (normalized acoustic pressure vector) at the receiving array. The results also showed that the proposed ROC expression provides a realistic detection performance prediction for the Bayesian detector for source position uncertainty using real data. The proposed approach to sonar detection performance prediction is much simpler and faster than those using conventional Monte Carlo approaches. 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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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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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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Two-way time spreading and path-loss measurements were collected in water 100 m deep, off the coast of Nova Scotia. Data were collected at frequencies of 20-22 kHz, 27-29 kHz, and 35-37 kHz using linear FM pulses 0.160 s in duration. The source-receiver was an anchored, high-frequency active sonar, and the target was a free-drifting echo repeater. Sonar and target positions were recorded using a portable tracking range. In the paper, two-way time spreading and path loss measurements are compared with modeled estimates obtained using an enhanced version of the generic sonar model (GSM). The GSM estimates of time spreading due to multipath propagation compare favorably with the experimental data. The model indicates that the path loss for individual eigenrays was extremely sensitive to fluctuations in the sound-speed profile. This led to substantial variation in the model output depending on the choice of profile. In place of the model, an empirical estimate of path loss was computed from the data. We obtained a two-way spreading loss of 2[18.4log10(R)] where R is the range from sonar to target. The data were also used to compute the standard deviation of the received echo intensity at each frequency. The standard deviation was computed two different ways. First it was computed using the peak echo level from each of the pulses at a given frequency. Then, it was computed from the total energy received from each of the pings. At all frequencies, the standard deviation was 1-2 dB lower when computed from the total received energy 相似文献
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This paper focuses on estimating the two-dimensional (2-D) target-speed vector (course and speed) using a multistatic sonar that consists of one monostatic sonar and one bistatic receiver. The speed and course estimates are obtained after a single transmission. The theory on bistatic Doppler and 2-D target-speed vector estimation is first considered and then applied to simulated and real data. The results can be used to improve classification algorithms or to feed speed information to tracking algorithms, for example. 相似文献
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The Cramer-Rao lower bounds on the cross-track translation and rotation of a displaced phase-center antenna (DPCA) in the slant range plane between two successive pings (known as DPCA sway and yaw in what follows) are computed, assuming statistically homogeneous backscatter. These bounds are validated using experimental data from a 118-182-kHz sonar, showing an accuracy of the order of 20 microns on the ping-to-ping cross-track displacements. Next, the accuracy required on the DPCA sway and yaw in order to achieve a given synthetic aperture sonar (SAS) beampattern specification, specified by the expected SAS array gain, is computed as a function of the number P of pings in the SAS. Higher accuracy is required when P increases to counter the accumulation of errors during the integration of the elementary ping-to-ping estimates: the standard deviation must decrease as P/sup -1/2/ for the DPCA sway and P/sup -3/2/ for the yaw. Finally, by combining the above results, the lower bounds on DPCA micronavigation accuracy are established. These bounds set an upper limit to the SAS length achievable in practice. The maximum gain Q in cross-range resolution achievable by a DPCA micronavigated SAS is computed as a function of the key SAS parameters. These theoretical predictions are compared with simulations and experimental results. 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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针对UUV避碰声呐探测障碍物过程中自主选择分割阈值进行障碍物检测的问题,提出了基于分区自适应阈值的障碍物检测算法。首先将避碰声呐图像均匀分为相同大小的图像块,对每个图像块基于最大类间方差算法确定该区域障碍物图像分割的高低阈值,然后对检测到的障碍物进行形态学处理去除孤立噪声点,对目标区域进行连通性分析及内部空洞处理,最终得到完整的障碍物轮廓信息。通过湖试数据验证表明了该方法对声呐障碍物检测的有效性。 相似文献
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The techniques of linearized least squares inversion (LLSI) and simulated annealing (SA) are both used to invert a series of synthetic and real normal-incidence, geo-acoustic sonar returns for estimates of impedance versus two-way travel time in the top several meters of ocean floor sediment. The objective is to determine the better (faster, more accurate) method for inverting this class of data. LLSI uses an over parameterized earth, i.e., one composed of layers whose thickness corresponds to a travel time equal to the sample interval. This makes the inverse problem quite large, but also makes it nearly linear. SA uses a more efficient parameterization, one whose layers have variable thickness as well as variable impedance. Because of the relatively narrow frequency band (~1 octave at 20 dB down from the peak) the time domain signal is oscillatory and inversion for layer thickness is nonlinear. Results show greater time efficiency in solving the large linear problem (LLSI) than in solving the small nonlinear problem (SA). However, in both cases almost all of the waveform energy was modeled, indicating that essentially all the information in the data had been successfully recovered. The inversions are applied to 10-20 kHz field data acquired offshore Florida, and several techniques are employed to enhance the effectiveness of each inversion method 相似文献