Fuzzy technology implemented in sonar systems

Applications of fuzzy algorithms in the domain of sonar systems that have been implemented in the latest generation of Atlas sonar equipment are introduced. The aim is to provide an insight into the solution of problems from an application-oriented point of view. Classification of DEMON spectra and automatic target tracking by means of fuzzy systems are introduced and demonstrated with examples. A short introduction to fuzzy technology is given to support the understanding of the algorithms presented     

Receiver depth selection for passive sonar systems

Since tactical acoustic systems such as towed arrays can be deployed at various depths, the authors address the question of what depth is optimal. This question is considered principally from the point of view of optimum propagation conditions, employing two deep-water scenarios representing summer and winter conditions in the western Mediterranean. Two simple rules-of-thumb are derived from these results: first, if the source depth is known, then the best receiver depth is either the source depth or the conjugate depth (where the sound speed is the same as that at the source). Second, if the source depth is unknown, then a receiver depth where the ocean sound speed is as low as possible is optimal. These two rules are qualified with a few disclaimers. In the first place they are derived under the assumption of a range-invariant environment. In addition, a definition of optimality requires numerous assumptions that may not always be appropriate. Both these guidelines and their domain of applicability are discussed     

New towed-array shape-estimation scheme for real-time sonar systems

In real-time towed-array systems, performance degradation of array gain occurs when a line array that is not straight is assumed straight in the beamforming process. In this paper, a new method is proposed for array shape estimation. The novelty of this method is that it accounts for the variations in the tow ship's speed, which are typical during course alterations. The procedure consists of two steps. First, we solve for the tow-point induced motion in the time domain based on the constraints from the tow-point compass-sensor readings and from a discretized Paidoussis equation. At each time instance, the shape estimate is solved from a linear system of equations. We also show that this solution is equivalent to a previous frequency-domain solution while the new approach is much simpler. In the second step, we use the tail compass-sensor data to adjust the overall array shape. By noting that variations in the ship speed lead to a distortion in the normalized time axis, we first register the predicted tail displacement with the tail sensor readings along the time axis. Then, distortions in the estimated array shape over its length can be compensated accordingly. We also model a slow-changing bias between sensor zeros and remove systematic sensor errors. The effectiveness of the new algorithm is demonstrated with simulations and real sea-trial data.     

Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems

This paper examines the potential for remote classification of seafloor terrains using a combination of quantitative acoustic backscatter measurements and high resolution bathymetry derived from two classes of sonar systems currently used by the marine research community: multibeam echo-sounders and bathymetric sidescans sonar systems. The high-resolution bathymetry is important, not only to determine the topography of the area surveyed, but to provide accurate bottom slope corrections needed to convert the arrival angles of the seafloor echoes received by the sonars into true angles of incidence. An angular dependence of seafloor acoustic backscatter can then be derived for each region surveyed, making it possible to construct maps of acoustic backscattering strength in geographic coordinates over the areas of interest. Such maps, when combined with the high-resolution bathymetric maps normally compiled from the data output by the above sonar systems, could be very effective tools to quantify bottom types on a regional basis, and to develop automatic seafloor classification routines.     

海底沉积物物理参数的声学反演模式

声学反演方程是声学探测沉积物物理参数的基础方程。基于声学理论和统计理论的声速反演沉积物物理参数的两种模式,运用南海海底沉积物声学物理数据验证、比较了两种反演模式,以反演孔隙度、含水量为例,得出基于双参数经验方程反演模式的适用性较强,但精度有待于提高;基于声速理论的反演模式反演南海海底沉积物物理参数有待于进一步完善。     

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     

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     

Parametric array Doppler sonar

A Doppler sonar technique capable of measuring ground speed and distance traveled at extreme ocean depths is described. The technique uses nonlinear acoustic effects to create the narrow beams required. Performance estimates are calculated and parameters for a test system chosen. Sonar Pond test results of transmitter beam patterns and source levels are given. The sea trials conducted in July and August of 1975, aboard the R. V. Sperry Star off New Providence Island, are described. Evaluation of data taken in water depths to 11 000 ft is made and substantial agreement with original estimates is demonstrated. The feasibility of an accurate, all ocean capability, dead reckoning navigator is thus confirmed.     

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.     

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     

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.     

Sidescan sonar image processing techniques

A four-step processing sequence is described to produce image mosaics from the various segments of a sidescanned acoustic imaging survey of a given seafloor area. Starting with data consisting for each ping of acoustic backscatter levels versus horizontal range across-track, median prefiltering is used first to reduce the influence of outliers on subsequent linear processes. Artifacts that are clearly unrelated to the backscattering properties of the seafloor are then isolated on a ping by ping basis through a spectral analysis that relies on a decomposition using Chebyshev polynomials to filter the low spatial frequency components of the image. Contrast enhancement is then achieved through an original implementation of the classical gray level histogram equalization technique by balancing local versus global histogram contributions. Pixels are mapped on a geographic grid taking due account of the geometry of the measurement and of the spacing between pings to minimize along-track smearing of features. Examples of results obtained with these processing techniques are given for SeaMARC II data recorded during a complete survey of Fieberling Guyot (32°.5 N, 128° W)     

Techniques for sonar target identification

We describe bistatic and mostly monostatic techniques that are useful for target classification by means of active sonar. The echoes returned by any submerged elastic body, exemplified here by a wolfram carbide (WC) sphere, contain features caused by the poles of the scattering amplitude of the problem. We have found these poles and shown that they naturally split into two large sets from which one can separately extract shape or material composition information. The composition information seems easier to determine than details about the shape. Together these sets of poles unambiguously characterize any scatterer.     

Multibeam sonar backscatter data processing

Multibeam sonar systems now routinely record seafloor backscatter data, which are processed into backscatter mosaics and angular responses, both of which can assist in identifying seafloor types and morphology. Those data products are obtained from the multibeam sonar raw data files through a sequence of data processing stages that follows a basic plan, but the implementation of which varies greatly between sonar systems and software. In this article, we provide a comprehensive review of this backscatter data processing chain, with a focus on the variability in the possible implementation of each processing stage. Our objective for undertaking this task is twofold: (1) to provide an overview of backscatter data processing for the consideration of the general user and (2) to provide suggestions to multibeam sonar manufacturers, software providers and the operators of these systems and software for eventually reducing the lack of control, uncertainty and variability associated with current data processing implementations and the resulting backscatter data products. One such suggestion is the adoption of a nomenclature for increasingly refined levels of processing, akin to the nomenclature adopted for satellite remote-sensing data deliverables.     

Passive sonar detection and localization by matched velocityfiltering

This paper presents a new bearings-only method of detecting and tracking low signal-to-noise ratio (SNR) wideband targets on a constant course and velocity trajectory. A track-before-detect strategy based on matched velocity filtering is adopted using spatial images constructed from a sequence of power bearing map (PBM) estimates accumulated during a track. To lower the threshold SNR for detection, a discrete bank of matched velocity filters integrates the PBM images over a range of hypothesized trajectories, such an approach eliminates the need to estimate the number of targets since signal detection is determined by comparing the output of each matched filter (MF) to a decision threshold. The distribution of the MF output is derived based on a single point target in diffuse noise assumption. Receiver operating characteristic curves show a definite detection gain under low SNR conditions for matched velocity filtering (track-before-detect) over detection from a single PBM     

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.