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.     

A behavior-based approach to adaptive feature detection andfollowing with autonomous underwater vehicles

This paper presents a technique for adaptively tracking bathymetric contours using an autonomous underwater vehicle (AUV) equipped with a single altimeter sonar. An adaptive feature mapping behavior is developed to address the problem of how to locate and track features of unknown extent in an environment where a priori information is unavailable. This behavior is implemented as part of the layered control architecture used by the AUV Odyssey II. The new adaptive feature mapping behavior builds on previous work in layered control by incorporating planning and mapping capabilities that allow the vehicle to alter its trajectory online in response to sensor data in order to track contour features. New waypoints are selected by evaluating the expected utility of visiting a given location balanced against the expected cost of traveling to a particular cell. The technique is developed assuming sensor input in the form of a single, narrow-beam altimeter sensor attached to a non-holonomic, dynamically controlled survey-class AUV such as the Odyssey II. Simulations of the Charles River basin which have been constructed from real bathymetry data are used as test missions. The 7-m contour line of a prominent trench in the river serves as the target feature. The adaptive contour following behavior tracks the contour despite navigation error and environmental disturbances, supplying the capability of autonomously detecting and following distinctive bathymetric features using a point sensor. This behavior provides a foundation for future research in tracking of dynamic features in the water-column and for concurrent mapping and localization over natural terrain using a point sensor     

A model for numerical simulation of nonstationary sonar reverberation using linear spectral prediction

An innovative approach to the numerical generation of nonstationery reverberation time series is presented and demonstrated. The computer simulated reverberation time series are of high quality, in that they are accurate representations of those which would result from an actual sonar system (transmit/receive and horizontal/ vertical beampatterns; pulse type, shape, length, and power; frequency and sampling rate), platform (speed and depth), and environment (wind speed and direction, backscattering strengths, and propagation loss). Volume, surface, and/or bottom reverberation as seen by a multiple beam sonar on a moving platform is generated. The approach utilizes recent developments in linear spectral prediction research in which the spectra of stochastic processes are modeled as rational functions and algorithms are used to efficiently compute optimal estimates of coefficients which specify the spectra. A two-fold sequence is formulated; first, the expected reverberation spectra for all beams are predicted and, second, the stochastic time series are generated from the expected spectra. The expected spectra are predicted using a numerical implementation, referred to as the REVSPEC (reverberation spectrum) model, of a general formulation of Faure, Ol'shevskii, and Middleton. Given the spectra, the Levinson-Durbin method is used to solve the Yule-Walker equations of the autoregressive formulation of linear spectral prediction. The numerical implementation of the approach, referred to as the REVSIM (reverberation simulation) model, produces nonstationary coherent multiple-beam reverberation time series. The formulation of the REVSIM model is presented and typical results given. A comparison is made between the simulation outputs of the REVSIM model and those of the REVGEN (reverberation generator) model, a standard well-accepted time series simulation model, to demonstrate the validity of the new approach.     

Geometrical aspects of a sonar-based navigation system for underwater vehicles

A novel self-contained navigation system has been devised for underwater vehicles operating in and around offshore installations. This system matches data from a sector-scanning sonar device to a computer model of the installation. The paper begins by highlighting the existing approaches to subsea navigation before outlining the main features of the proposed system. It then concentrates on a key component of this system which is a method for calculating the position and heading of an underwater vehicle navigating in the vincinity of tubular steel structures. An iterative solution method is presented which incorporates six degree of freedom vehicle motions and this is verified in a series of laboratory experiments with various arrangements of structural members and using a commercial sonar device. The key features, applications and performance of this method are discussed. The main conclusion is that the proposed method for calculating the position and heading of an underwater vehicle contributes towards achieving an accurate and reliable subsea navigation capability.     

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     

TOBI image processing-the state of the art

TOBI (Towed Ocean Bottom Instrument) is a deep-tow sidescan sonar vehicle from which sidescan sonar data are now routinely collected and archived. This paper describes the algorithms developed for detailed processing of TOBI data. Sonar imagery has a characteristic set of processing challenges and these are addressed. TOBI provides a very large sonar dataset, and to limit the difficulties of handling and processing these data, the raw data are subjected to a data reduction technique prior to further processing. Slant-range correction is improved by editing vehicle altitude data using a median filter. Noise on TOBI imagery can appear in two main forms; speckle noise and line dropouts. Speckle noise is removed by a small median difference kernel and line dropouts are removed using a ratio of two box-car filters, each with appropriate thresholding techniques. Precise geocoding of the imagery requires an accurate estimate of vehicle location, and a method of calculation is presented. Two optional processing algorithms are also; presented; deblurring of imagery to improve along-track resolution at far range, and the suppression of a surface reflection return which may occur when TOBI is operated in relatively shallow water. Several of the techniques presented can be transcribed and modified to suit other datasets     

Active impulsive echo discrimination in shallow water by mappingtarget physics-derived features to classifiers

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     

Curved shape reconstruction using multiple hypothesis tracking

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     

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q/spl I.bar/learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q/spl I.bar/learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs.     

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.     

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。     

Pulse-length-tolerant features and detectors for sector-scan sonar imagery

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.     

Underwater vehicle obstacle avoidance and path planning using amulti-beam forward looking sonar

This paper describes a new framework for segmentation of sonar images, tracking of underwater objects and motion estimation. This framework is applied to the design of an obstacle avoidance and path planning system for underwater vehicles based on a multi-beam forward looking sonar sensor. The real-time data flow (acoustic images) at the input of the system is first segmented and relevant features are extracted. We also take advantage of the real-time data stream to track the obstacles in following frames to obtain their dynamic characteristics. This allows us to optimize the preprocessing phases in segmenting only the relevant part of the images. Once the static (size and shape) as well as dynamic characteristics (velocity, acceleration,…) of the obstacles have been computed, we create a representation of the vehicle's workspace based on these features. This representation uses constructive solid geometry (CSG) to create a convex set of obstacles defining the workspace. The tracking takes also into account obstacles which are no longer in the field of view of the sonar in the path planning phase. A well-proven nonlinear search (sequential quadratic programming) is then employed, where obstacles are expressed as constraints in the search space. This approach is less affected by local minima than classical methods using potential fields. The proposed system is not only capable of obstacle avoidance but also of path planning in complex environments which include fast moving obstacles. Results obtained on real sonar data are shown and discussed. Possible applications to sonar servoing and real-time motion estimation are also discussed     

A generic swath‐mapping data format

There is a pressing need for standardization of data derived from bathy‐metric swath‐mapping systems. Currently several dozen multibeam and sidescan sonar data formats exist within the oceanographic community, and more can be expected as new systems are developed. Without some standardization of swath‐mapping data formats, the capability for use and integration of data from different systems will be severely compromised.

This paper presents a strategy for organizing swath bathymetry data in a logical modular fashion that will allow data from all current swath bathymetric sonar systems to be stored and accessed in a common fashion. We have chosen the approach of defining compact efficient modules for each logically independent portion of a data record and storing it in a manner that is portable between diverse computer architectures and operating systems. This approach is extensible to accommodate new types of data. Although specifically developed for swath bathymetry, this format is also capable of supporting digital sidescan data and other types of swath data.     

Automatic Registration of TOBI Side-Scan Sonar and Multi-Beam Bathymetry Images for Improved Data Fusion

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.     

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     

Optimal measurement strategies for target tracking by a biomimetic underwater vehicle

This study presents a novel navigation and control system allowing a biomimetic-autonomous underwater vehicle (BAUV) to track a target. A Bayesian approach using an extended Kalman filter and combined localization and environmental mapping by a BAUV are implemented. This strategy selects the best sensor measurement by choosing one of several forward-looking directions. The body of the BAUV moves in a cyclical pattern; thus, an inexpensive echo sounder can be installed on the BAUV head to detect environmental features without the need for expensive scanning devices. The localization and environmental mapping problem is then transformed into a non-linear two-point boundary value problem. Optimal policies are to maintain the accuracy of predicted states and to approach minimal observation cost by solving the control problem. A line-of-sight guidance law is utilized that drives the BAUV to the target. An approach that controls the motion of the body/caudal fin and pectoral fins of the BAUV is utilized for target tracking. Estimation, measurement, and control processes are integrated to form a working system. Experiments using a test bed BAUV confirm the effectiveness of the proposed approach.     

Sonar tracking of multiple targets using joint probabilistic data association

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.