Classification Of Buried Objects Research Articles

Numerical Simulation of the Buried Object Detection Based on Underwater Plasma Acoustic Source

Detection and classification of buried objects is of great importance in underwater counterterrorism and archaeology. To penetrate the sediment, a low frequency intensive acoustic source is needed. Underwater plasma acoustic source (UPAS) with high voltage discharge has the advantage of adjustable pulse length, high source level output and no pollution to the environment, which can satisfy these needs. In this paper, we introduced the UPAS, including its basic mechanism, structure and pressure output. Then we build up an elastic wave propagation model, solved it with finite difference and staggered grid methods, and combined with certain source and boundary condition, we simulated and analyzed the pressure wave propagation in time domain with an aluminum cylinder buried in sediment, from the results we validated the effectiveness of UPAS in the application of buried object detection.

Mine Identification and Classification by Mobile Sensor Network Using Magnetic Anomaly

In this paper, a new method is proposed to identify and classify the data obtained by the sensor network (SN) for the detection of mines. This method is used for the identification of antitank and antipersonnel mines and classification of buried objects within a target region. In this paper, a mobile SN is used to detect mines and some other objects buried and creating magnetic anomaly in and around the region where they are found, with the behavior of the individual sensors swarming onto the area under which a mine or any other object is buried. The process of collecting data by the SN and modeling it mathematically are explained in detail. The SN is modeled as a fictitious two-dimensional spatial impulse sampler. This paper is motivated by clearing the territories of mine fields to open them to agriculture. It is very important because, currently, in some countries, very fertile territories around the borders are covered by buried mines. The approach is basically based on magnetic anomaly measurements, which directly tackles the subregions corresponding to buried objects whether they represent objects that are separately located or occluded by other objects. It is based on a new developed method that is called “the back-most object detection and identification algorithm.” This method is fully automatic, and there is no human intervention throughout the process. In this paper, classification of objects is based on their well-known shapes and dimensions. Therefore, there is no need for sophisticated learning algorithms to achieve classification. The experimental results are given both for detection and identification of a single mine and classification of a number of mines and any other objects that have a potential of giving false alarms in a target region.

Sonar transmit and receiver design for detection of underwater objects in nonstationary environments.

In undersea environments, particularly shallow water, the sonar backscatter from objects of interest can be subject to propagation effects such as dispersion, attenuation, and multi‐path, which can confound detection and classification. Detection and classification of buried objects are further complicated by dramatic changes in the backscatter due to the sediment layer. These propagation and environmental effects can induce time‐varying (or nonstationary) characteristics in the received sonar signal. In addition, the object itself can induce nonstationarities, such as the inherent dispersion characteristics of some elastic objects. The processing and analysis of such signals for detection and classification can be enhanced by applying time‐varying methods to the received signal, such as time‐frequency analysis and linear time‐varying filters. Detection can also be enhanced by designing a transmit waveform to optimize some metric, such as received signal‐to‐interference/noise power. In this talk, we explo...

Spheroidal Mode Approach for the Characterization of Metallic Objects Using Electromagnetic Induction

We propose a spheroidal mode approach to characterize the electromagnetic induction (EMI) response of buried objects, assumed to be much more conductive than their environment. Both the excitation and the response are formulated as the linear superpositions of basic spheroidal modes. The scattering coefficients characterize objects, regardless of their geometrical complexity and material inhomogeneity, due to the orthogonality of the spheroidal modes. The ill-conditioning encountered in retrieving the scattering coefficients is dealt with by mode truncation and Tikhonov regularization. The approach is tested for both simulated and measured data, and the retrieval results show encouragingly that only few excitation and response modes effectively represent the EMI response of the objects. The proposed approach is therefore promising in the detection and classification of buried objects

Subcritical scattering from buried elastic shells

Buried objects have been largely undetectable by traditional high-frequency sonars due to their insignificant bottom penetration. Further, even a high grazing angle sonar approach is vastly limited by the coverage rate dictated by the finite water depth, making the detection and classification of buried objects using low frequency, subcritical sonar an interesting alternative. On the other hand, such a concept would require classification clues different from the traditional high-resolution imaging and shadows to maintain low false alarm rates. A potential alternative, even for buried targets, is classification based on the acoustic signatures of man-made elastic targets. However, the elastic responses of buried and proud targets are significantly different. The objective of this work is to identify, analyze, and explain some of the effects of the sediment and the proximity of the seabed interface on the scattering of sound from completely and partially buried elastic shells. The analysis was performed using focused array processing of data from the GOATS98 experiment carried out jointly by MIT and SACLANTCEN, and a new hybrid modeling capability combining a virtual source-or wave-field superposition-approach with an exact spectral integral representation of the Green's functions for a stratified ocean waveguide, incorporating all multiple scattering between the object and the seabed. Among the principal results is the demonstration of the significant role of structural circumferential waves in converting incident, evanescent waves into backscattered body waves, emanating to the receivers at supercritical grazing angles, in effect making the target appear closer to the sonar than predicted by traditional ray theory.

AUVs as integrated, adaptive acoustic sensors for ocean exploration

Autonomous underwater vehicles (AUV) are rapidly being transitioned into operational systems for national defense, offshore exploration, and ocean science. AUVs provide excellent sensor platform control, allowing for, e.g., accurate acoustic mapping of seabeds not easily reached by conventional platforms, such as the deep ocean. However, the full potential of the robotic platforms is far from exhausted by such applications. Thus, for example, most seabed-mapping applications use imaging sonar technology, the data volume of which cannot be transmitted back to the operators in real time due to the severe bandwidth limitation of the acoustic communication. The sampling patterns are therefore in general being preprogramed and the data are being stored for postmission analysis. This procedure is therefore associated with indiscriminate distribution of the sampling throughout the area of interest, irrespective of whether features of interest are present or not. However, today’s computing technology allows for a significant amount of signal processing and analysis to be performed on the platforms, where the results may then be used for real-time adaptive sampling to optimally concentrate the sampling in area of interest, and compress the results to a few parameters which may be transmitted back to the operators. Such adaptive sensing concepts combining environmental acoustics, signal processing, and robotics are currently being developed for concurrent detection, localization, and classification of buried objects, with application to littoral mine countermeasures, deep ocean seabed characterization, and marine archeology. [Work supported by ONR and NATO Undersea Research Center.]

Automated buried object classification with a broadband autonomous underwater vehicle sonar system

The classification of buried objects in the littoral ocean is a challenging task for the underwater community. The high attenuation rate of the seabed imposes the use of a low frequency sonar system, which leads to an increased aperture length requirement to achieve high resolution. A simple classification algorithm is proposed in which a broadband source insonifies the target area while multiple receiver vehicles process the target scattering strengths over various bistatic angles to reconstruct a planar cut of the scattered field of the target. The advantages of this technique are the low rate of information exchange between vehicles, the simplicity of the processing algorithm, and less demanding processing requirements compared to the image-based techniques. The algorithm is based on multistatic measurements of the target spatial acoustic signatures, which permits a parameterization of the identification problem to a decreased number of degrees of freedom, with the consequence of decreased processing and implementation difficulty. The use of a broadband sonar system splits the processing in parallel tasks, leading to an overall classification improvement. The GOATS experiment series is used to demonstrate the classification performance of the algorithm for various targets and conditions. The results compare favorably with the state-of-the-art imaging techniques.

Sub-critical insonification of buried elastic shells

In a shallow water environment a high frequency high grazing angle mine-hunting sonar approach is vastly limited by the coverage rate, making the detection and classification of buried objects using subcritical grazing incidence an attractive alternative. One of the central issues in mine countermeasurements regarding the physics of scattering from spherical shells is the isolation and the analysis of the resonant excitations of the system distinguishing the manmade elastic targets from rocks or other clutter. Burial of an elastic target in the seabed results in a variety of modifications to the scattered response caused by different physical mechanisms, geometric constrains, and intrinsic sediment properties. The aim of this research is to identify, analyze, and explain the fundamental effects of the sediment and the proximity of the seabed interface on the scattering of sound from elastic spherical shells insonified using low frequencies at subcritical incident angles. A new, comprehensive understanding of the goats98 experimental data was obtained distinguishing the effects of the acoustics environment from the resonant signature of a buried elastic target. To achieve this and to further investigate the more intricate details of the scattering process, a numerically improved, OASES-3D modeling framework was used. [Work supported by ONR.]

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.