Position Correction Using Echoes From a Navigation Fix for Synthetic Aperture Sonar Imaging

In synthetic aperture sonar (SAS), the platform position must be known sufficiently accurately for signals to be added coherently along the synthetic aperture. Often, the onboard navigation system is insufficiently accurate by itself, so corrections are needed. A well-known method is the displaced phase center antenna (DPCA) procedure for correcting platform position using seabed echoes. DPCA methods have the advantage of insensitivity to changing interference patterns, moving specular reflection, and changing occlusion, with aspect. However, when seabed echoes are unusable, either because they are too weak, or because they are corrupted by multipath, the seabed DCPA method may fail. Therefore, we present an alternative DPCA method using sonar echoes from a suitable navigation fix, based on an object detected after standard beamforming. In our proposed system, look angle is obtained by tracking the centroid of the rectified image of the fix object. When the standard DPCA correction equations are modified for a fixed reflector, it turns out that they provide incremental range and look-angle errors, precisely the values required when the target itself is used as the navigation fix. Moreover, the values obtained are then self-compensating for errors in estimating seabed depth or forward motion of the platform. The navigation fix is selected by bracketing in range, and beamforming overlapping subsets of the receiver array. In this paper, we present experimental results at transmitter frequencies of 25 and 100 kHz where our method enabled well-focused SAS images to be generated with little recourse to other navigation information. Hence, SAS can be carried out, even when a sophisticated inertial navigation system (INS) is not available.      

Bistatic synthetic aperture imaging of proud and buried targets from an AUV

The use of autonomous underwater vehicles (AUVs) for the detection of buried mines is an area of current interest to the Mine CounterMeasures (MCM) community. AUVs offer the advantages of lower cost, stealth, reduced operator risk, and potentially improved coverage rates over more traditional mine hunters. However, AUVs also come with their own set of difficulties, including significant error in navigation and low communication rates with the mother platform and each other. In the case of bistatic detection scenarios, AUVs will therefore have difficulty knowing where exactly in space they are and the trigger time of sources on other platforms, be they ships or other AUVs. However, the potential improvement in detection and coverage rates offered by bistatic sonar concepts makes resolution of these issues a high priority. In this paper, the problems of inaccurate navigation and source timing information are addressed for the Generic Oceanographic Array Technology data set. In this experiment, conducted off Marciana Marina during June 1998, a MIT AUV with a SACLANTCEN acoustic array and acquisition system was used together with a TOPAS parametric sonar to explore issues of buried target detection using AUVs. In this paper, solutions to the navigation and timing problems are proposed which enable the effective use of bistatic synthetic aperture sonar (SAS) concepts for the detection of buried objects in the mid-frequency regime of 2-20 kHz.     

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     

Relative Height Estimation by Cross-Correlating Ground-Range Synthetic Aperture Sonar Images

The relative height of the seafloor can be estimated by using two vertically displaced receivers. In this paper, we propose techniques to improve the accuracy of the estimated height. Our results are based on the use of synthetic aperture sonar (SAS) imaging, which implies coherent addition of complex images acquired from a moving platform. The SAS processing improves the along-track (or azimuth) resolution, as well as the signal-to-noise ratio (SNR), which in turn improves the estimated height accuracy. We show that the shift of the effective center frequency induced by coherent, frequency-dependent scattering affect the time-delay estimates from complex cross correlations, and we propose a correction technique for broadband signals with uneven magnitude spectra. To reduce the effect of geometrical decorrelation and increase the coherence between the images, we beamform the sonar images onto an a priori estimate of the seafloor height before correlating. We develop a mathematical model for the imaging geometry. Finally, we demonstrate our proposed estimators by providing relative seafloor height estimates from real aperture and SAS images, obtained during the InSAS-2000 experiment at Elba Island in Italy. In particular, we demonstrate that the SAS image quality is significantly improved by inclusion of the height estimates as a priori information.     

"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     

基于分区自适应阈值的声呐障碍物检测算法研究

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。     

A processing requirement and resolution capability comparison ofside-scan and synthetic-aperture sonars

The processing requirements and resolution capabilities of both side-look sonar (SLS) and synthetic-aperture sonar (SAS) systems are outlined. Side-look sonar is presented as a real-beam imaging technique along with expressions for relevant system- and image-related parameters. Synthetic-aperture sonar is discussed, and the limitations imposed by the speed of sound in the ocean environment are identified. A specific side-look system (SeaMARC I) is presented under two configurations and comparable SAS designs are proposed. Based on the examples provided by the SeaMARC I system and the hypothetical SAS designs, it is shown that single-beam SAS systems can be designed to achieve area coverage rates comparable to single-beam side-scan systems, yet with improved azimuth resolution     

联合小波和NLM滤波的侧扫声纳回波信号降噪

为进一步降低侧扫声纳回波信号中非高斯分布的乘性噪声,获取更佳效果的侧扫声纳图像,提出了一种利用小波和NLM(nonlocal means)滤波的组合降噪方法.首先采用同态变换将侧扫声纳回波ping信号中的乘性噪声转换为加性噪声,然后利用小波阈值和NLM滤波对侧扫声纳每ping回波数据实施降噪处理,最后经过小波反变换和指...     

Morphological and statistical approaches to improve detection in the presence of reverberation

The detection of a target echo in a sonar image is usually a difficult task since the reverberation, consisting of a large number of spurious echoes, generates a lot of false alarms. In this paper, we propose two new detectors derived from image processing algorithms. These detectors are respectively based on a morphological and a statistical contrast. Each detector only requires setting a few parameters. This setting is done using some prior knowledge about the data (shape of the emitted signal and the used antenna, characteristics of the reverberation). Nevertheless, an extensive statistical study of the detection performances proves that the proposed methods are robust and that even an imprecise setting of the parameters leads to satisfactory results. Applied to the real data, these detectors and their sequential combination lead to a significant improvement on the performances: The false alarm rate is drastically reduced while the detection probability is preserved. Based on different contrasts, these detectors have complementary behaviors. Therefore, a further improvement is achieved by a fusion of the different results to classify the remaining echoes as whether spurious or true detection.     

Statistical normalization of spherically invariant non-Gaussian clutter

Conventional detection in active sonar involves comparing the normalized matched filter output power to a fixed preset threshold. Threshold crossings from contacts of interest are labeled as detections and those from undesired clutter echoes as false alarms. To maintain a constant false-alarm rate (CFAR) in the presence of strong transient clutter, the system can either increase the threshold or apply some function that suppresses this background down to an acceptable level. The latter approach leads to a more consistent background on the display, which enables operator-assisted detection. Background clutter suppression should not come at the expense of contact detection; to maximize the probability of detection (PD) for a given probability of false alarm (PFA), the likelihood ratio test (LRT) is used. However, the LRT does not address display issues, since the threshold that achieves a desired PFA varies with the input distribution. Ideally, the LRT output is monotonically transformed using a "statistical normalizer" (SN) that returns a consistent CFAR background without degrading the optimized PD. Within the radar community, clutter suppression is proposed using a LRT tuned to a K-distributed spherically invariant random vector (SIRV) model. However, this model does not lend itself to SN, as a closed-form expression for the LRT output density does not exist. In contrast, the proposed SIRV clutter model, with Pareto distributed power, leads to a closed-form density from which the SN function is readily derived. This combined Pareto-LRT/SN detector nearly matches the optimized PD performance of the K-distributed LRT and maintains a consistent CFAR background for display purposes.     

Real- and synthetic-array signal processing of buried targets

Results from two field experiments aimed at investigating the detection and classification of buried targets are presented. In both experiments a 2-16-kHz parametric source was used. In the first experiment, the source was used in combination with a 12-m horizontal line array and in the second with a 1.4-m vertical line array which was displaced horizontally along an underwater rail to form a 10 m /spl times/ 1.4 m two-dimensional synthetic aperture sonar (SAS). To increase the SAS integration time, the parametric source was electronically scanned in azimuth during the displacement along the rail, as in spotlight mode. It is shown that both arrays allow important signal-to-reverberation gains, enhancing the detection of sub-bottom echoes. A new, environmentally adaptive, matched filter which further improves the signal to reverberation ratio while allowing discrimination between proud and buried targets is presented and validated experimentally. The use of resonant scattering for target classification of buried objects is discussed, in the particular case of spherical shells.     

A method for estimating the physical and acoustic properties of the sea bed using chirp sonar data

This paper proposes a method, based on the Biot model, for estimating the physical and acoustic properties of surficial ocean sediments from normal incidence reflection data acquired by a chirp sonar. The inversion method estimates sediment porosity from reflection coefficient measurements and, using the estimated porosity and the measured change in fast wave attenuation with frequency, estimates the permeability of the top sediment layer. The spectral ratio of echoes from the interface at the base of the upper sediment layer and from the sediment-water interface provides a measure of the change in attenuation with frequency. Given the porosity and permeability estimates, the Kozeny-Carman equation provides the mean grain size and the inversion method yields the acoustic properties of top sediment layer. The inversion technique is tested using chirp sonar data collected at the 1999 Sediment Acoustics Experiment (SAX-99) site. Remote estimates of porosity, grain size, and permeability agree with direct measurements of those properties.     

Side-scan sonar image matching

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     

Object detection and tracking method of AUV based on acoustic vision

This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.     

An automatic approach to the detection and extraction of mine features in sidescan sonar

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.     

Bayesian Sonar Detection Performance Prediction With Source Position Uncertainty Using SWellEx-96 Vertical Array Data

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.     

Humpback whale song or humpback whale sonar? A reply to Au et al

Au et al.'s arguments against the hypothesis that humpback whale songs function as long-range sonar are based on questionable assumptions rather than on empirical data. Like other echolocating mammals (e.g., bats), singing humpback whales: 1) localize targets in the absence of visual information; 2) possess a highly innervated peripheral auditory system; and 3) modulate the temporal and spectral features of their sounds based on environmental conditions. The sonar equation is inadequate for determining whether humpback whale songs generate detectable echoes from other whales because it does not account for temporal variables that can strongly affect the detectability of echoes. In particular, the sonar equation ignores the fact that much of the noise encountered by singing humpback whales is spectrally and temporally predictable, and that audition in mammals is a dynamic and plastic process. Experiments are needed to test the hypothesis that singing humpback whales listen for and respond to echoes generated by their songs     

Detection of objects on the sea bottom using backscatteringcharacteristics dependent on the observation point

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     

Active learning for detection of mine-like objects in side-scan sonar imagery

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.     

一种联合单波束测深的侧扫声呐海底线提取方法

论述了侧扫声呐的成像原理,针对目前常用侧扫声呐系统数据采集的相关特性,结合实际工程应用,提出了一种联合单波束测深的侧扫声呐海底线提取新方法,以便在数据后处理时进行快速准确地斜距改正。利用实测数据验证了该方法的有效性,并与成熟商用软件的提取结果和阈值法提取结果进行对比分析,结果表明该方法具有一定的可行性和优势。