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OrbisResearch.com is a leading market research reseller which offers research report on “2017 Market Research Report on Global Ground Penetrating Radar (GPR) Industry”.The report provides information on products, services, trends, top companies, verticals, countries globally.Texas, USA - April 24, 2017 /MarketersMedia/ — The Global Ground Penetrating Radar (GPR) Market Research Report 2017 is a professional and in-depth study on the current state of the Ground Penetrating Radar (GPR) market. Annual estimates and forecasts are provided for the period 2017 through 2022. Also, a six-year historic analysis is provided for these markets. The Ground Penetrating Radar (GPR) industry was 135.20 million USD in 2016 and is projected to reach USD 210.74 million USD by 2022, at a CAGR of 7.68% between 2016 and 2022. The report provides a basic overview of the Ground Penetrating Radar (GPR) industry including definition, classification, application and industry chain structure. Then, the report focuses on global major leading industry players with information such as company profiles, product picture and specifications, sales, market share and contact information. What’s more, the Ground Penetrating Radar (GPR) industry development trends and marketing channels are analyzed. This report studies Ground Penetrating Radar (GPR) focuses on top manufacturers in global market, with production, price, revenue and market share for each manufacturer, covering GSSI MALA IDS GeoRadar GEOTECH SSI US Radar Utsi Electronics Chemring Group Radiodetection Japan Radio Co ChinaGPR Kedian Reed Market segment by regions, this report splits global into several key regions, with production, industry, revenue, market share and growth rate of Ground Penetrating Radar (GPR) in these regions, like USA Global Others Split by product types, with production, revenue, price, market share and growth rate of each type, can be divided into Handheld Ground Penetrating Radar Cart Based Ground Penetrating Radar Get a PDF Sample of Market Report at: http://www.orbisresearch.com/contacts/request-sample/246180 Split by applications, this report focuses on industry, market share and growth rate of Ground Penetrating Radar (GPR) in each application, can be divided into Transport and Road Inspection Municipal Inspection Disaster Inspection Archeology Others Finally, the feasibility of new investment projects is assessed, and overall research conclusions are offered. In a word, the report provides major statistics on the state of the Ground Penetrating Radar (GPR) industry and is a valuable source of guidance and direction for companies and individuals interested in the market. Checkout some Major Points from TOC: Chapter One: Ground Penetrating Radar (GPR) Market Overview 1 1.1 Product Overview and Scope of Ground Penetrating Radar (GPR) 1 1.2 Ground Penetrating Radar (GPR) Segment by Types 5 1.2.1 Global Production Market Share of Ground Penetrating Radar (GPR) by Types in 2016 5 1.2.2 Handheld Ground Penetrating Radar (GPR) 6 1.2.3 Cart Based Ground Penetrating Radar (GPR) 6 1.3 Ground Penetrating Radar (GPR) Segment by Applications 8 1.3.1 Ground Penetrating Radar (GPR) Consumption Market Share by Applications in 2016 8 1.3.2 Transport and Road Inspection 9 1.3.3 Municipal and Environmental Protection 9 1.3.4 Disaster Prevention and Migration 10 1.3.5 Archeology 10 1.4 Ground Penetrating Radar (GPR) Market by Regions 11 1.4.1 North America Status and Prospect (2012-2022F) 11 1.4.2 China Status and Prospect (2012-2022F) 12 1.4.3 Europe Status and Prospect (2012-2022F) 13 1.4.4 Japan Status and Prospect (2012-2022F) 14 1.4.5 Korea Status and Prospect (2012-2022F) 15 1.4.6 Taiwan Status and Prospect (2012-2022F) 16 1.4.7 India Status and Prospect (2012-2022F) 17 1.4.8 ROW Status and Prospect (2012-2022F) 18 1.5 Global Market Size of Ground Penetrating Radar (GPR) (2012-2022F) 19 Chapter Two: Global Ground Penetrating Radar (GPR) Market Competition by Manufacturers 21 2.1 Global Ground Penetrating Radar (GPR) Production and Share by Manufacturers (2016 and 2017E) 21 2.2 Global Ground Penetrating Radar (GPR) Revenue and Share by Manufacturers (2016 and 2017E) 23 2.3 Global Ground Penetrating Radar (GPR) Average Price by Manufacturers (2016 and 2017E) 26 2.4 Manufacturers Ground Penetrating Radar (GPR) Manufacturing Base Distribution, Sales Area, Product Types 28 2.5 Ground Penetrating Radar (GPR) Market Competitive Situation and Trends 29 2.5.1 Ground Penetrating Radar (GPR) Market Concentration Rate 29 2.5.2 Ground Penetrating Radar (GPR) Market Share of Top 3 and Top 5 Manufacturers 31 2.5.3 Mergers & Acquisitions, Expansion 32 Chapter Three: Global Ground Penetrating Radar (GPR) Production, Revenue (Value) by Regions (2012-2017E) 33 3.1 Global Ground Penetrating Radar (GPR) Production and Market Share by Regions (2012-2017E) 33 3.2 Global Ground Penetrating Radar (GPR) Revenue (Value) and Market Share by Regions (2012-2017E) 35 3.3 Global Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 37 3.4 North America Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 38 3.5 Europe Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 38 3.6 China Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 39 3.7 Japan Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 39 3.8 Korea Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 40 3.9 Taiwan Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 40 3.10 India Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 41 3.11 ROW Ground Penetrating Radar (GPR) Production, Revenue, Price and Gross Margin (2012-2017E) 41 About Us: Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customised reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialisation. This helps our clients to map their needs and we produce the perfect required market research study for our clients. Contact Info:Name: Hector CostelloEmail: sales@orbisresearch.comOrganization: Orbis ResearchAddress: 4144N Central Expressway, Suite 600, Dallas, Texas – 75204, U.S.APhone: +1 (214) 884-6817Source URL: http://marketersmedia.com/2017-global-ground-penetrating-radar-gpr-industry-by-size-share-segments-trends-development-estimates-and-forecasts/189457For more information, please visit http://www.orbisresearch.com/reports/index/2017-market-research-report-on-global-ground-penetrating-radar-gpr-industrySource: MarketersMediaRelease ID: 189457


Alqadah H.F.,US Radar | Scholnik D.P.,US Radar
2017 IEEE Radar Conference, RadarConf 2017 | Year: 2017

Some recent works on direction of arrival (DOA) estimation using sparse arrays have centered on a difference co-array approach, where DOA estimation is performed with respect to the source variances rather than the source complex amplitudes. This formulation advertises big gains in the available degrees of freedom (DOF) which makes it of interest to sparse arrays. Here we seek to quantitatively compare four super-resolution DOA estimation methods as applied to the difference co-array formulation. Our study assesses performance in terms of stability with respect to the number of data snapshots used and signal-to-noise ratio (SNR). While none of the four methods presented here are able to fully exploit the DOF offered by the difference co-array, we do find that two of the four methods however do exhibit significantly more stable performance in terms of snapshots and SNR. © 2017 IEEE.


Alqadah H.F.,US Radar
Proceedings of the 2016 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016 | Year: 2016

Recent research concerning qualitative inverse scattering approaches, and in particular the Linear Sampling Method (LSM), has revealed an interesting connection to interior Eigenmodes of scatterer(s) embedded in a known background. These modes have some desirable properties that are amenable for automatic target recognition (ATR) applications; chiefly scale, translation, and rotation invariance. In this work we apply a space-frequency regularized LSM to extract and exploit this connection for generating resonant based features. We establish initial proof-of-concept of the proposed approach using numerically generated multi-static data. © 2016 IEEE.


Alqadah H.F.,US Radar
Proceedings of the 2016 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016 | Year: 2016

We consider the problem of imaging small current sources embedded in an underwater medium by means of a novel compressive Near-field Electromagnetic Holography (NEH) method. Near-field measurements of the electromagnetic field are represented by a finite combination of electric and magnetic dipoles distributed over a source surface. We investigate a co-array formulation of the resulting linear model by means of a Khatri-Rao product of the forward projection operators. We illustrate that such a formulation can dramatically increase the degrees of freedom which can be leveraged through a compressive inversion method. We illustrate proof of concept of the proposed approach using planar magnetic array measurements taken in an earth field simulator (EFS). The data presented in this paper was graciously obtained under permission from DRDC Atlantic. © 2016 IEEE.


Blunt S.D.,University of Kansas | Chan T.,Johns Hopkins University | Gerlach K.,US Radar
IEEE Transactions on Aerospace and Electronic Systems | Year: 2011

A new approach for spatial direction-of-arrival (DOA) estimation, denoted as re-iterative superresolution (RISR), is developed based upon a recursive implementation of the minimum mean-square error (MMSE) framework. This recursive strategy alternates between updating an MMSE filter bank according to the previous receive spatial power distribution and then subsequently applying the new filter bank to the received data snapshots to obtain a new estimate of the receive spatial power distribution. Benefits of this approach include robustness to coherent sources such as can occur in multipath environments, operation with very low sample support to enable "tracking" of sources with rapidly changing DOA (e.g., bistatic pulse chasing), intrinsic determination of model order, and robustness to array modeling errors by exploiting approximate knowledge of array calibration tolerances. From an implementation perspective RISR belongs to a class of recursive algorithms that includes Interior Point methods, the minimum-normbased FoCal underdetermined system solver (FOCUSS) algorithm, and the iterative reweighted least squares (IRLS) algorithm. However, the structure of RISR also enables the natural inclusion of spatial noise covariance information as well as a mechanism to account for array modeling errors which are known to induce degradation for existing superresolution methods. The inclusion of the latter is also found to facilitate an adaptive form of regularization that establishes a feasible (given model uncertainties) dynamic range for source estimates. © 2011 IEEE.


Chen R.C.,US Radar
IEEE National Radar Conference - Proceedings | Year: 2010

Optimal waveforms for minimum mean square error range profile estimation are investigated. An idealized measurement and waveform adaptation process is developed that yields optimal scene and range specific waveforms. This process is idealized in that during each cycle of the process, a large number of dwells are required. As part of our method, a modified version of the Adaptive Pulse Compression (APC) estimation method is used to estimate the range profile after each dwell cycle. The proposed method is analogous to the APC method in that it yields a set of range specific optimal waveforms, while the APC method yields a set of range specific optimal pulse compression filters. In certain scenarios, the measurement and waveform adaptation process yields range profile estimates that are significantly better than those derived by the APC method alone. © 2010 IEEE.


Raj R.G.,US Radar
IEEE National Radar Conference - Proceedings | Year: 2014

We apply our recently developed concept of mutual exclusivity [1] in the context of discriminative coding, to the problem of learning dictionary for representing signals drawn from N classes in a way that optimizes their discriminability. We first briefly review our mutual-exclusivity concept and then deploy it a simple discriminative dictionary learning algorithm that directly generalizes the well-known KSVD algorithm which is addressed for the traditional problem of signal coding. We demonstrate performance improvements over traditional KSVD based feature extraction schemes and conclude by describing avenues for future research. © 2014 IEEE.


Raj R.G.,US Radar | Chen V.C.,US Radar | Lipps R.,US Radar
IET Signal Processing | Year: 2010

The authors develop methods for the time-frequency (TF) analysis of human gait radar signals. In particular the authors demonstrate how knowledge of different motion classes can be obtained via a Markov chain model of state transitions based on the TF envelope structure associated with the motion sequence being analysed. The class-conditional knowledge thus obtained allows us to effectively extract the motion curves associated with different body parts via a non-parametric partial tracking algorithm that is coupled with an optimum Gaussian g-Snake modelling of the TF structure. The optimum segmentation of the TF structure into different half-cycles as well as the determination of the initial Doppler control points (corresponding to each half-cycle) is facilitated by a dynamic programming algorithm wherein the associated cost function involves a mean-square minimisation of the best quadratic fit to each segment together with a sparsity prior that enables us to control the smoothness of the approximation space in which the time series being analysed is effectively projected. Finally, the authors describe some of the limitations of our approach and point out future research directions that can overcome some of the difficulties associated with the complex interaction between the inherently non-linear dynamics of human gait motion and radar systems. © 2010 The Institution of Engineering and Technology.


A Ground Penetrating Radar (GPR) system makes use of digital circuitry for synchronizing the sampling of a received radar signal with a transmitted radar signal. The digital synchronization achieves improved waveform reproduction and greater receiver sensitivity. Furthermore, the system employs digital circuitry to control the gain of a receiver amplifier. The digitally controlled gain makes it possible to accurately calibrate the amplitude of received radar signals with great precision while achieving good dynamic range.


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