Target Backscatter Research Articles

Активно-пасивний імпульсно-шумовий радар діапазону 3мм та результати його попередніх випробувань

The subject of this manuscript is broadband noise signals and systems. The aim of this research is to show the results of a modern active-passive 3-mm waveband system consisting of a noise-pulsed radar and a radiometer. Noise radar systems (NRS) based on broadband signals are characterized by high resolution, accuracy, and information content when performing unambiguous measurements of the range and speed of targets, as well as increased electromagnetic compatibility and noise immunity. These distinctive features of NRS determine the relevance of their construction for practical tasks of short- and medium-range radar. Additional opportunities for ensuring secrecy, reliability of detection of objects, and their tracking are provided by the combination of active and passive location modes in conjunction with advancement to the short-wave part of the millimeter (MM) wave band (WB). The most important characteristic of any pulsed radar, which largely determines its potential for practical application, is the operating frequency, as well as the shape and width of the probing signal spectrum. The main idea of this work is to describe the construction scheme and the results of preliminary tests of the developed active-passive system in the 94 GHz band with a noisy 20–100 ns illumination pulse in the 5 GHz band. The obtained values of the energy potential of the system in the active location mode (-105 dB) and the achieved radiometer sensitivities in the passive mode (0.007K and 0.03K) make it possible to observe ground and air objects at a distance of several kilometres. Noted that the measured parameters of the radar, in the case of processing the received signal by pulse compression methods, make it possible to count on ensuring the resolution of targets in range at the level of 10-15 cm. A multiple (more than an order of magnitude) decrease in the interference fluctuations of the received signal, which is due to the facet nature of the backscattering of targets, has experimentally demonstrated using a noise probing pulse compared to a single-frequency pulse. The methods for further work on the development and practical application of the constructed measuring system are outlined.

SAR Image Quality Assessment: From Sample-Wise to Class-Wise

Target recognition is the core application of radar image interpretation. In recent years, deep learning has become the mainstream solution. However, this family of methods is highly dependent on a great deal of training samples. Limited samples may lead to problems such as underfitting and poor robustness. To solve the problem, numerous generative models have been presented. The generated samples played an important role in target recognition. It is therefore needed to assess the quality of simulated images. However, few studies were performed in the preceding works. To fill the gap, a new evaluation strategy is proposed in this paper. The proposed method is composed of two schemes: the sample-wise assessment and the class-wise one. The simulated images can then be evaluated from two different perspectives. The sample-wise assessment combines the Fisher separability criterion, fuzzy comprehensive evaluation, analytic hierarchy process, and image feature extraction into a unified framework. It is used to evaluate whether the relative intensity of the speckle noise of the SAR image and the target backscattering coefficients are well simulated. Contrarily, the class-wise assessment is designed to compare the application capability of the simulated images holistically. Multiple comparative experiments are performed to verify the proposed method.

Few-Shot PolSAR Ship Detection Based on Polarimetric Features Selection and Improved Contrastive Self-Supervised Learning

Deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection over the past few years. However, the backscattering of manmade targets, including ships, is sensitive to the relative geometry between target orientation and radar line of sight, which makes the diversity of polarimetric and spatial features of ships. The diversity of scattering leads to a relative increase in the scarcity of PolSAR-labeled samples, which are difficult to obtain. To solve the abovementioned issue and extract the polarimetric and spatial features of PolSAR images better, this paper proposes a few-shot PolSAR ship detection method based on the combination of constructed polarimetric input data selection and improved contrastive self-supervised learning (CSSL) pre-training. Specifically, eight polarimetric feature extraction methods are adopted to construct deep learning network input data with polarimetric features. The backbone is pre-trained with un-labeled PolSAR input data through an improved CSSL method without negative samples, which enhances the representation capability by the multi-scale feature fusion module (MFFM) and implements a regularization strategy by the mix-up auxiliary pathway (MUAP). The pre-trained backbone is applied to the downstream ship detection network; only a few labeled samples are used for fine-tuning and the construction method of polarimetric input data with the best detection effect is studied. The comparison and ablation experiment results on the self-established PolSAR ship detection dataset verify the superiority of the proposed method, especially in the case of few-shot learning.

A Hybrid 3-D-ADI-TDPE/DGTD Method for Multiscale Target Echo Simulation in Large-Scale Complex Environments

In this article, we propose a novel hybrid method for multiscale target echo simulation in large-scale complex environments. The three-dimensional-alternating direction implicit-time domain parabolic equation (3-D-ADI-TDPE) method with refractive index terms and the discontinuous Galerkin time-domain (DGTD) method are combined to solve the propagation of transient electromagnetic waves in large-scale regions and backscattering in multiscale target regions, respectively. At the same time, to realize the connection of two regions and the prediction of target echo, the transfer functions of wave propagation and target backscattering are obtained by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula> transform (ZT). In addition, the target echo process is transformed into a cascade of transfer functions to realize simulation modularization and parallel processing. We also discuss the performance of the proposed method in terms of accuracy, memory consumption, and CPU processing time. Results indicate that the processing time required by the 3-D-ADI-TDPE/DGTD hybrid method is more than 11 times less than that of DGTD alone. Moreover, the ZT improves the flexibility of the hybrid method and provides a new concept for the simulation technology of radio wave propagation.

Detecting Surface Displacement in Kathmandu Valley with Persistent Scatterer Interferometry

Kathmandu Valley has been susceptible to surface displacement due to natural as well as anthropogenic causes since a long time. Previous studies till 2017 suggest that displacement (specially subsidence) with rates of several centimeters per year have occurred in the Kathmandu Valley indicating uncontrolled groundwater withdrawal as the major cause. Owing to the history of surface displacement, this study aims at detecting the nature of land subsidencein Kathmandu for years: 2017 (18th January to 26th December) and 2019 (2nd January to 28th December) based on Persistent Scatterer Interferometry (PSI) technique using Synthetic Aperture Radar (SAR) datasets from Sentinel 1. PSI is able to detect persistently backscattering targets and evaluate respective displacements from the backscattered signal. The results of 2017 and 2019 revealed significant displacement of -100.54mm and -129.19mm along Line Of Sight (LOS) of radar during the study period at Baluwatar and Lazimpat area of Kathmandu district respectively. Similarly, New Baneshwor, Bode and Imadol exhibited a substantial displacement of -88.81mm,-103.55mm, -127.35mm respectively for year 2019.

ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images

Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors.

Scattering and contextual features fusion using a complex multi-scale decomposition for polarimetric SAR image classification

Polarimetric synthetic aperture radar (PolSAR) images contain rich information about back-scattering and physical characteristics of targets. So, they have high ability for discrimination of different land cover classes. The aim of this research is to introduce an efficient method for PolSAR image classification. Extraction of both scattering and contextual features is important for class discrimination. Therefore, the scattering and contextual feature fusion (SCF) method is proposed to fuse the extracted polarimetric and morphological features through applying a complex multi-scale decomposition. The dual tree complex wavelet transform is used to decompose each scattering feature map into its details and approximate components. The contextual feature maps are decomposed in a similar way. Then, details of two kinds of feature maps are fused region by region. This process is also done for the approximation components containing the low frequency information. The result will be a high dimensional fused feature space. The principal discriminant analysis (PDA) is proposed to reduce the data dimensionality with discarding noisy components and increasing the class discrimination. The extracted features are then fed into a simple classifier to obtain the classification map. Three L-band PolSAR images acquired by airborne synthetic aperture radar (AIRSAR) and electronically steered array radar (ESAR) are used for doing experiments. The SCF method shows superior classification results with respect to several state-of-the-art PolSAR classifiers. For example, for the Flevoland dataset containing 15 classes, without applying post processing, the SCF method results in 95.22% overall accuracy compared to 2DCNN with 91.84% and 3DCNN with 93.94% overall accuracy. With applying post processing, the classification results of SCF, 2DCNN and 3DCNN are increased to 99.55%, 98.61% and 99.09%, respectively.

Transmit Code and Receive Filter Design for PMCW Radars in the Presence of One-Bit ADC

Phase-modulated continuous-wave (PMCW) radar is an emerging technology in various civilian applications. Due to the high bandwidth of the PMCW signal, it needs high-speed analog-to-digital converters (ADCs). High-resolution ADCs supporting this high bandwidth are expensive, have a big size on the chip, and consume high power, which are significant challenges for radar. A solution to deal with the aforementioned challenges is to use low-resolution, or in the most extreme case, one-bit ADCs. In this article, we aim at designing the transmit code and the receive filter for the PMCW radar in the presence of one-bit ADC at the receiver side. To this end, we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mean</i> integrated sidelobe level metric, called MISL. MISL generalizes the well-known ISL metric for nonideal ADC usage at the receiver side and takes the effect of noise and target backscattering coefficient into account. For mathematical tractability, we employ the maximum likelihood estimation of the target backscattering coefficient. This leads to casting an optimization problem that does not need knowledge about noise and target characteristics. We utilize the cyclic optimization procedure to optimize the transmit code and the receive filter. The filter design subproblem admits a closed-form solution, whereas we obtain a solution to transmit code design subproblem via the coordinate descent framework. Numerical examples illustrate the superior performance of the proposed method compared to benchmarks that design sequences assuming ideal ADC at the receiver.

Automatic Detection of Low-Backscatter Targets in the Arctic Using Wide Swath Sentinel-1 Imagery

Low backscatter signatures in Synthetic Aperture Radar (SAR) imagery are characteristic to surfaces that are highly smooth and specular reflective of microwave radiation. In the Arctic, these typically represent newly formed sea ice, oil spills, and localized weather phenomena such as low wind or rain cells. The operational monitoring of low backscatter targets can benefit from a stronger integration of freely available SAR imagery from Sentinel-1. We therefore propose a detection method applicable to Sentinel-1 Extra Wide-Swath (EW) SAR scenes. Using intensity values coupled with incidence angle and Noise-Equivalent Sigma Zero (NESZ) information, the image segmentation method is able to detect the low backscatter targets as one segment across sub-swaths. We use the Barents Sea as a test site due to the abundant presence of low backscatter targets with different origins, and of long-term operational monitoring services that help cross-validate our observations. Utilising a large set of scenes acquired in the Barents Sea during the freezing season (November - April), we demonstrate the potential of performing large-scale operational monitoring of local phenomena with low backscatter signatures.

Unsupervised Clustering Method to Discriminate Dense Deception Jamming for Surveillance Radar

With the development of digital radiofrequency memory devices, the deception jamming has become a major jamming type in radar electronic counter countermeasures, especially the dense deception jamming. Recent years, some methods using signal features are proposed for specific deception jamming excision scene. It verifies the feasibility that uses signal features to discriminate deception jamming. However, discriminator training and simulation are indeed hard to achievable since no jamming samples can be obtained in advance. In this letter, we focus on the online dense deception jamming discrimination problem without the offline data training. Since deception jamming signals generated by jammers are definitely different from physical backscattering target echoes on signal features and dense jamming signals generated by one jammer has similar signal fingerprint features, an unsupervised clustering method with heuristic feature extraction is proposed. All discrimination processes are data driven and can be executed online without <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information. To evaluate its effectiveness, the proposed method is applied to a group of experimental data collected from a trial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> -band radar. Results corroborate the proposed method and indicate the direction of future research.

PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.

Acoustic scattering by a finite-length elastic elliptical cylinder in a plane wave.

In this paper, based on the deformed cylinder method, the expression of the far-field acoustic scattering form function of a finite-length elastic elliptical cylinder is obtained. The target strength of elastic aluminum and polymethylmethacrylate (PMMA) elliptical cylinders, with different lengths varying with dimensionless frequency ka, is studied for different incident angles. In addition, the change of the target strength of a PMMA elliptical cylinder with incident angle is explored theoretically and experimentally when the frequency range is 20-40 kHz. Simulation results show that there is almost no change in the peak of the curve of the backscattering target strength versus frequency for a finite-length elastic elliptical cylinder with different lengths. As the length decreases, the target strength gradually decreases. When the theoretical simulation results are compared with the experimental results, it is found that the theoretical predictions are in good agreement with the experimental results. Furthermore, the study will provide theoretical and experimental basis for the prediction and recognition of underwater targets.

음향산란이론모델을 이용한 살오징어(Todarodes pacifica)의 음향후방산란강도 측정

Recently, Japenese common squid, an important fisheries resource, has been decreased in the coastal waters of Korea. The purpose of this study is to estimate the TS of Japanese common squid and to use it as basic data to figure out the exact amount of resources of Japanese common squid. In order to estimate the TS, the total length and mantle length of 26 common squids were measured and photographed. The samples photographed were digitized and applied to the DWBA theoretical model. In the shortest mantle length No. 10 sample, the range of acoustic backscattering target strength for each frequency was measured –101.4 dB∼–55.9 dB at 38 kHz, –91.9 dB∼–57.1 dB at 70 kHz, –99.8 dB∼–56.2 dB at 120 kHz. The acoustic scattering strength range of No. 21 sample with the longest mantle length was measured –98.44 dB∼–55.3 dB at 38 kHz, –84.9 dB∼–53.8 dB at 70 kHz, and –89.5 dB ∼–51.5 dB at 120 kHz. The TS regression equations for common squid at each frequency was as follows; 38kHzSUBMax/SUB = 20logSUB10/SUB(ML) - 71.5, 38 kHzSUBAvg/SUB = 20logSUB10/SUB(ML) – 81.5, 70kHzSUBMax/SUB = 210logSUB10/SUB(ML) – 76.9, 70kHzSUBAvg/SUB. 20logSUB10/SUB(ML) - 82.8, 120kHzSUBMax/SUB = 20logSUB10/SUB(ML) – 75.6, 120kHzSUBAvg/SUB = 20logSUB10/SUB(ML) – 83.0

Rapid free‐vibration analysis with model reduction based on coherent nodal clusters

SummaryModal expansion is a workhorse used in many engineering analysis algorithms. One example is the coupled boundary element‐finite element computation of the backscattering target strength of underwater elastic objects. To obtain the modal basis, a free‐vibration (generalized eigenvalue) problem needs to be solved, which tends to be expensive when there are many basis vectors to compute. In the above‐mentioned backscattering example it could be many hundreds or thousands. Excellent algorithms exist to solve the free‐vibration problem, and most use some form of the Rayleigh‐Ritz (RR) procedure. The key to an efficient RR application is a low‐cost transformation into a reduced basis. In this work, a novel, inexpensive a priori transformation is constructed for solid‐mechanics finite element models based on the notion of coherent nodal clusters. The inexpensive RR procedure then leads to significant speedups of the computation of an approximate solution to the free vibration problem.

An improved land cover classification using polarization signatures for PALSAR 2 data

Land cover classification in mixed land cover scenarios is challenging with PolSAR data. Polarimetric decomposition techniques are most popular methods for PolSAR data classification in recent times. These techniques focus on identification of dominant scattering phenomena and hence result in sub-optimal classification in mixed land cover scenarios. Alternatively, polarization signatures (PSs) are good illustrations of SAR target responses as they depict a detailed physical information from target backscatter. Researchers have successfully utilized SAR PSs for land cover (LC) classification. Some reports suggested utilizing correlation between observed PSs and standard target PSs as features for LC classification. This paper presents a study on improved utilization of PSs for optimal LC classification in mixed class scenarios. First, PS based SAR features are derived using fully polarimetric SAR data. The features represent a degree of similarity between observed and standard PSs. The derived features are termed as polarization signatures correlation features or PSCFs. The novel PSCFs are analyzed, evaluated and compared with decomposition based features for the purpose of LC classification. Classification performance indicators highlight potential of PSCFs for mixed LC classification problems. Therefore, further an adaptive and optimal LC class boundary estimation approach for LC classification is proposed and developed. Observed PSs and reference LC class PS statistics are used to build empirical models between classification performance indicators and LC class boundaries. The empirical models are optimized using the evolutionary genetic algorithm to maximize classification performance. A decision tree is constructed based on the optimal class boundaries to prepare LC classification. The proposed classification approach is compared with some recent popular classifiers and comparison suggests that the proposed approach provides satisfactory results for mixed LC classification scenarios.

Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range

Radiometric calibration adjusts the measured pixel intensity to a physical property, the radar cross section (RCS). After calibration, this relationship is defined over the entire backscatter range: from low image power (near noise) up to high reflections (below saturation). Based on a proper radiometric calibration, the measured radar backscatter for the Sentinel-1 synthetic aperture radar (SAR) satellite constellation is validated over a wide backscatter range using different target types. Therefore, the RCS derived from point targets and radar brightness from distributed targets are compared between Sentinel-1A (S-1A) and Sentinel-1B (S-1B) acquisitions over the same observation area for regions where a stable target backscatter is expected for a certain period of time. Low differences (in the order of 0.3 dB) are found between S-1A and S-1B for high and medium backscatter derived from point targets or rainforest regions, but higher differences for low backscattering regions like ice areas and lakes. For comparing radar brightness containing low backscatter targets, an accurate derived noise level has to be taken into account. In addition to the measured lower noise equivalent beta zero (NEBZ) level, higher transmit power was detected for S-1B compared to S-1A. The higher antenna gain of S-1B leads finally to a higher sensitivity for low backscattering areas of S-1B compared to S-1A and explains the found differences.

PolSAR target scattering interpretation in rotation domain: theory and application

The backscattering of radar targets is sensitive to the relative geometry between targets' orientations and the radar. This target scattering diversity makes mechanism interpretation and quantitative application difficult. This study reviews a newly established theory for target scattering mechanism interpretation in the rotation domain. It contains uniform polarimetric matrix rotation theory and the interpretation tool of polarimetric coherence pattern. The core idea of the proposed method is to expand targets' information obtained at a specific orientation to the full rotation domain, which can provide fundamentals for the deep mining and utilisation of target scattering information. On this basis, typical applications with the derived new polarimetric features are introduced and the effectiveness of these features is demonstrated.

Inversion for acoustic parameters of plastic polymer target in water

The high-precision molding capability of complex surfaces and structures makes three-dimensional (3D) printing technology more widely used in underwater acoustic models and structural molding. The plastic polymer material, as the main material in the field of 3D printing, possesses the acoustic parameters that are directly related to the acoustic properties of 3D printed underwater acoustic models and structures. Based on the Rayleigh normal series solution for the acoustic scattering of underwater target and the mechanism analysis of the low-frequency resonance which is associated with subsonic Rayleigh waves on the solid plastic polymer spheres, the sensitivity characteristics of the resonance frequency and amplitude to the wave velocity and attenuation coefficient are obtained at low frequencies. It is shown that the transverse wave velocity and the transverse wave attenuation coefficient both have high inversion accuracy at low values of &lt;i&gt;ka&lt;/i&gt;, where &lt;i&gt;a&lt;/i&gt; is the radius of elastic sphere, as they are quite sensitive to the backscattering resonance frequency and amplitude, respectively. In the frequency band of interest, the backscattering resonance frequency is almost independent of attenuation coefficient. Considering the inversion accuracy, the longitudinal wave velocity and transverse wave velocity are inverted by the resonance frequency separately. Based on these characteristics, the cyclic search method is used to establish an acoustic parameter inversion method for plastic polymer materials, in which the frequency and amplitude of backscattering resonance peak are used as cost functions. Finally, the backscattering acoustic scattering experiment on a solid typical plastic polymer PMMA (methyl methacrylate-acrylic) sphere is conducted in the tank. The experimental results about the backscattering target strength varying with the frequency are in good agreement with the simulation results in a frequency range of 5–20 kHz. The simulation parameters such as the longitudinal wave velocity, transverse wave velocity and attenuation coefficient are obtained by the previously established inversion method. Therefore, the acoustic parameter inversion method provides reliable acoustic parameters for 3D printed underwater acoustic model and structure performance prediction for plastic polymer materials.

ON THE ESTIMATION OF POLARIMETRIC PARAMETERS FOR OIL SLICK FEATURE DETECTION FROM HYBRID POL AND DERIVED PSEUDO QUAD POL SAR DATA

Abstract. Oil spills in oceans have a significant long term effect on the marine ecosystem and are of prime concern for maritime economy. In order to locate and estimate the oil spread area and for quantitative damage assessment, it is required to continually monitor the affected area on the sea and its surroundings and space based remote sensing makes this technically viable. Synthetic Aperture Radar SAR with its high sensitivity to target dielectric constant, look angle and polarization-dependent target backscatter has become a potential tool for oil-spill observation and maritime monitoring. From conventional single-channel SAR (single-pol, HH or VV) to multi-channel SAR – (Dual/Quad-polarization) and more recently compact polarimetric (Hybrid/Slant Linear) SAR systems have been widely used for oil-spill detection in the seas. Various polarimetric features have been proposed to classify oil spills using full, dual and compact polarimetric SAR. RISAT-1 is a C-band SAR with Circular Transmit and Linear Receive (CTLR) hybrid polarimetric imaging capability.This study is aimed at the polarimetric processing of RISAT-1 hybrid pol single look complex (SLC) data for derivation of the decisive polarimetric parameters which can be used to identify oil spills in oceans and their discrimination from look-alike signatures. In order to understand ocean–oil spill signatures from full-quad pol SAR, pseudo-quad pol covariance matrix is constructed from RISAT-1 hybrid pol using polarimetric scattering models .Then polarimetric processing is carried out over pseudo-quad pol data for oil slick detection. In-house developed software is used for carrying out the above oil-spill study.

Laboratory simulation of backscattering by a hemisphere in a boundary wave-guide with an external source

While there have been various investigations of backscattering when a source and a target are situated in the same wave-guide, the situation when the source is external to the wave-guide is less well explored. For example, a target buried in a mud layer over sand [K. L. Williams, J. Acoust. Soc. Am. 140, EL504 (2016)], the mud layer may act as a wave-guide. The target signatures when the source is in the water column above the mud are modified. A laboratory experiment has been performed to isolate wave-guide effects on target backscattering, using a simplified wave-guide in water. The wave-guide is defined between a free water surface and a thin partially transparent acrylic plate placed below the water surface. A soft spherical target is half-exposed at the free-surface and source-receiver is below the plate. A geometric method-of-images model gives the timing arrivals of wave-guide echoes from an infinite number of back-scattering paths that undergo multiple reflections in the wave-guide and gives the frequency response of the wave-guide by summation of the contributions. This model gives a closed-form result in the far-field limit, and is compared to the experimental results in the time and frequency domains. [Work supported by ONR.]