Concurrent mapping and localization using sidescan sonar

This paper describes and evaluates a concurrent mapping and localization (CML) algorithm suitable for localizing an autonomous underwater vehicle. The proposed CML algorithm uses a sidescan sonar to sense the environment. The returns from the sonar are used to detect landmarks in the vehicle's vicinity. These landmarks are used, in conjunction with a vehicle model, by the CML algorithm to concurrently build an absolute map of the environment and to localize the vehicle in absolute coordinates. As the vehicle moves forward, the areas covered by a forward-look sonar overlap, whereas little or no overlap occurs when using sidescan sonar. It has been demonstrated that numerous reobservations by a forward-look sonar of the landmarks can be used to perform CML. Multipass missions, such as sets of parallel and regularly spaced linear tracks, allow a few reobservations of each landmark with sidescan sonar. An evaluation of the CML algorithm using sidescan sonar is made on this type of trajectory. The estimated trajectory provided by the CML algorithm shows significant jerks in the positions and heading brought about by the corrections that occur when a landmark is reobserved. Thus, this trajectory is not useful to mosaic the sea bed. This paper proposes the implementation of an optimal smoother on the CML solution. A forward stochastic map is used in conjunction with a backward Rauch-Tung-Striebel filter to provide the smoothed trajectory. This paper presents simulation and real results and shows that the smoothed CML solution helps to produce a more accurate navigation solution and a smooth navigation trajectory. This paper also shows that the qualitative value of the mosaics produced using CML is far superior to those that do not use it.     

Passive sonar ML estimator for ship propeller speed

The optimal estimator in the maximum-likelihood sense fur the propeller speed of a ship, using underwater radiated cavitation noise generated by the propeller blades, is derived. From the result the number of blades on the propeller can also be derived. Results obtained for real sonar data using a digital implementation of the estimator will be presented     

基于声呐图像的深海采矿车组合定位导航算法

实现高精度的定位导航是深海采矿车完成海底工作任务的基础条件。在采矿车行进过程中,声呐设备生成的图像信息能够反映海底场景的变化,从而体现采矿车本身的运动,由此建立了一种声呐图像里程计,并将其与轮式里程计和USBL测量数据相结合提出了一种深海采矿车组合定位导航算法。首先对多波束前视声呐图像进行预处理,然后使用Canny算法进行特征检测并对特征点云进行配准,再结合声呐成像原理构建了声呐图像里程计运动模型,最后通过轮式里程计运动模型推导预测方程、声呐图像里程计运动模型和USBL测量数据推导更新方程,利用EKF（extended Kalman filter）算法实现基于多传感器融合的定位与姿态估计。海试数据验证了该组合定位算法能实现轮式里程计、声呐里程计和超短基线在速度、位置、艏向角估计、定位速率的精度互补,具有一定的有效性和精确性,该算法为深海采矿车的定位与导航算法研发提供了参考。     

海洋石油管线探测多波束剖面声纳设计

针对海洋石油管线探测多波束剖面声纳的高速、大容量的实时信号处理任务,以及声纳系统进一步扩展的需求,设计并实现了一种基于IP网络互连的、以并行DSP为处理节点的多波束剖面声纳系统.该系统应用于海底石油管线探测与定位,系统中的每个处理节点与数据采集转换部分采用TCP/IP网络连接,可以通过物理上添加一个或多个处理节点成倍的提高系统的信号处理能力,而在软件上无须大的改动.通过电联调与水槽试验,验证了系统的可行性和稳定性,该设计为高速水声阵列信号实时处理系统提供了一种全新的解决思路.     

Mariana Trough by multi-beam sonar

The Mariana Trough on the eastern part of the Philippine plate is a product of backare extension. Fine-grained bathymetry shows the trough to have a diffuse spreading center and to be spreading obliquely, thus refuting several previous theories. Unzipping of the trough from south to north has occurred for the past 6 m.y. This is reflected in spreading lineations perpendicular to the tectonic spreading fabric. The growth history of the trough is shown since 48 m.y. BP including speculation as to its continued eastward extension.     

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     

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.     

基于三维声纳点云的水下沉井地形探测技术

为了解决水下沉井地形监测难题,给出了一种基于三维扫描声纳的地形监测方法以及地形点云的高精度快速处理方法.首先,结合沉井特点,给出了一种悬挂式三维扫描声纳扫描沉井水下地形方法;然后,结合沉井形状及点云特点,给出由降采样点云抽稀法、基于欧式距离的区域自生长算法的点云去噪方法和基于K-means++聚类算法的沉井水下地形分割...     

联合YOLOv4和迁移学习的侧扫声纳图像沉船检测

为解决侧扫声纳(SSS)图像沉船检测中样本不足、代表性弱等带来的检测精度低的问题,提出了一种联合YOLOv4和迁移学习的SSS图像沉船检测方法。首先,基于SSS成像机理实现了SSS沉船图像样本扩增,解决样本少而无法开展沉船检测模型构建的难题;然后,利用迁移学习,将公共数据集上学习到的权重和沉船通用性特征引入YOLOv4网络,构建高性能沉船检测模型。试验表明,构建的沉船检测模型取得了85.5%的类平均精度(mAP),将传统方法的检测精度提升了7.7%,在少样本情况下实现了沉船的高精度检测。     

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.     

Acoustic models and sonar systems

The basic types of acoustic models are reviewed. These include ray models, spectral integral models, normal mode models, parabolic equation modeling, and 3-D acoustic modeling. Their application to conventional sonar simulation problems is demonstrated. Examples of their use in more advanced signal processing applications are presented     

Spatial processing of signals received by platform mounted sonar

Acoustic signals received by platform mounted sonar arrays can be spatially processed to enhance the detection of targets in the presence of both ambient and platform generated (self) noise. Ambient noise in the ocean, such as that due to distant shipping or biological choruses, are known to be spatially correlated. The platform generated noise will be of near-field origin and may not be received by all elements in the array. In this paper we investigate the performance of the minimum variance distortionless response (MVDR) beamformer and the recently introduced Fourier integral method (FIM) and compare their performances with the conventional beamformer. Real passive sonar data, obtained from a platform mounted sparse linear array of hydrophones, is used to study the performance of the beamformers in a typical sonar environment. It is shown that in the absence of self noise, when the array is accurately calibrated the MVDR beamformer will perform very well, but when sensor gain or phase errors are present the performance of the MVDR beamformer is degraded. Further, the MVDR beamformer is unable to reject the self noise which is not "seen" by the entire array. FIM however seems to perform well and a modified version of FIM, which we call weighted FIM (WFIM), is shown to perform better and is at worst comparable to a well calibrated MVDR beamformer     

Measurement of seabed topography by multibeam sonar using CFFT

A precise ocean bottom map for ocean surveying and dredging is desired. Especially in dredging, it is essential to know the seabed topography in real time without being affected by scatterers (for example floating sand and mud) in the seawater during work. To meet these requirements, the multi-narrow-beam sonar system (MBSS) has been developed. The MBSS forms beams with the use of the complex fast Fourier transform (CFFT) algorithm. In the MBSS, arithmetic mean processing is employed to eliminate echo from scatterers and the measurement error due to the oblique incident angle is reduced by peak value detection processing. By using these processes, an ocean bottom map can be accurately obtained. It is both theoretically and experimentally shown that the distribution of echo intensity from scatterers is approximated by the Rayleigh probability density function. The arithmetic mean of four to eight successively received echoes from scatterers reduces the variance of the echo intensity distribution by 6 to 12 dB.     

Biomimetic sonar locates and recognizes objects

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     

Aerial and acoustic marine mammal detection and localization on navy ranges

Acoustic monitoring and aerial visual surveys of marine mammal activity were conducted simultaneously at the Navy's Pacific Missile Range Facility near Kauai, HI, during times of both high- and low-whale density from February 2002 to March 2003. Specifically, recordings from the range's 24 broadband hydrophones were made during 11 of 16 "in-season" and during six of ten "off-season" aerial surveys. Basic acoustic detections consisted primarily of humpback whale calls and sperm-whale clicks, and those two species were also reported in the visual surveys. The relative number of acoustic detections roughly corresponded with the visual survey results throughout the year. The same acoustic data were also provided to a passive-acoustic-localization algorithm based on acoustic propagation models which generated estimates of sperm-whale movement through the range. The acoustic localizations are in close proximity in space and time to the visual observations of sperm whales. Verification of the model-based localization algorithm's accuracy was demonstrated in a controlled-source experiment at the Navy's Atlantic Undersea Test and Evaluation Center (AUTEC) range in the Bahamas where the recordings of sperm-whale clicks were broadcast and successfully tracked. The localization accuracy of the model-based technique and traditional hyperbolic techniques is compared. These results raise the possibility of using existing Navy assets to detect and track marine mammals, particularly during times when visual sighting conditions are not favorable, in efforts to minimize their exposure to underwater sound.     

Oceanic incoherent Doppler sonar spectral analysis by conventional and finite-parameter modeling methods

The spectral structure of oceanic incoherent Doppler sonar data is analyzed with Fourier and two finite-parameter modeling methods (Spectral Moment Estimation (SME) and AutoRegressive (AR)). Fourier spectral analysis, applied to long-range segments of echo return data, validates a theoretical point scattering model. It is demonstrated that Fourier spectral techniques, applied to data from incoherent sonar systems, cannot be used to probe oceanic spectral and velocity fields on the scales that frequently are important in oceanographic applications. The finite-parameter methods model the spectral structure on closely spaced range intervals. The SME method calculates the first moment of the Doppler spectrum at each range interval and the AR method provides spectral estimates at these same intervals. Trade-offs in estimating scatterer radial velocity using the various algorithms are considered. It is shown that a frequency-integrated AR technique has velocity estimation performance comparable to the SME method. In addition, the AR technique reveals that several regions of data possess asymmetric spectral structure. The implications of this spectral structure for oceanic velocity estimates are discussed.     

Megaplume bubble process visualization by 3D multibeam sonar mapping

MultiBeam echosounder data were collected during a surface-ship survey of the 22/4b well site in the North Sea in September 2011 using a Teledyne-Reson 7125. Modern multibeam echosounders are instrumental in providing detection and accurate localization of weak to strong bubble plumes. Two survey profiles effectively insonified the bubble plumes rising from the main crater at the well site, providing snapshot data of bubble plume processes. Additionally, three profiles insonified bubble plumes rising from, in, and to the south of a secondary crater, 1.2 km southeast of the main crater. Data processing included a simple algorithm that muted mislocated echoes from incomplete sidelobe suppression. The data processing produced a Cartesian volume of echo intensity from the water column and seabed.Plume geometry was analyzed to investigate a number of important large-scale plume processes, including plume bubble detrainment due to currents and stratification, downwelling flows, sea surface interaction, plume heterogeneity, and other fluid transport processes. The data showed strong upwelling flows, with bubble vertical motions generally much faster than currents. One important finding was that megaplumes create intrusions above the general thermocline, in part because their extensive upwelling flow lifts the thermocline locally. As a result, the intrusion layer deposits dissolved gases in the upper wave-mixed layer of the water column where it is not isolated from the atmosphere, unlike dissolved gases in the lower water column.The analysis shows that high fidelity multibeam echosounder data can provide a wealth of remote sensing information on bubble plume characteristics and processes, with important applications, including blowout monitoring and response, better understanding of megaplumes such as used in lake destratification, and improved characterization of natural seep emission processes.     

Shallow water sonar conditions and radar backscatter

Sonar and radar signals are scattered by small-scale roughness elements of the sea surface. The characteristics of the roughness depend on wind, sea state, and other parameters. The dynamic behavior of this roughness is related to radar backscatter. This provides a basis for oceanographic radar remote sensing and links radar backscatter to sea surface sonar conditions. Radar, sonar, and roughness experiments have been conducted at the research platform NORDSEE. The results are combined and provide a method to estimate shallow water sonar conditions from the radar backscatter cross section