Design of a High-Power Gaussian Pulse Transmitter for Sensing and Imaging of Buried Objects

Alesheikh, Mahdi; Feghhi, Rouhollah; Sabzevari, Fatemeh Modares; Karimov, A. M.; Hossain, Masum; Rambabu, Karumudi

doi:10.1109/jsen.2021.3127136

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…Gaussian pulses have also been used in ground penetrating radar applications with broadband frequency content [52] and are known as impulse radar systems. A high power, ultra-wideband Gaussian pulse transmitter was used for sensing and imaging of buried objects [55]. However, the Gaussian pulses used in ground penetrating radar are typically high frequency and therefore are unsuitable for the seawater environment.…”

Section: Pulse Types Used In Emi Sensingmentioning

confidence: 99%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawaterirradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated datawere generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the spherein 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeablematerials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receivercoil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms tonoise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression,which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models.Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram dataused for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heavisidestep-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects,including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified usingfinite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion ofelectromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without theuse of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallicspheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paperpresents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

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Section: Pulse Types Used In Emi Sensingmentioning

confidence: 99%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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“…These antennas offer high efficiency and independent optimization of Sigma and Delta modes. However, these antennas are not suitable for low-profile and small-volume structures, which are often required in surveillance systems because of their size and weight limitations [6].…”

Section: Introductionmentioning

confidence: 99%

A Compact Monopulse Antenna Array With Suppressed Mutual Coupling Using Broadband Schiffman Phase Shifters

Alamdar,

Mohammadpour-Aghdam,

Khalili

2024

IEEE Access

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This study presents the design, fabrication, and measurement of a compact microstrip monopulse antenna array for surveillance applications. The design process and limitations of the planar antenna structure are discussed, including the stacking of two radiating elements as a parasitic loaded patch antenna to increase bandwidth and suppress mutual coupling. A distributed feeding network for 8×16 elements with an integrated monopulse comparator, was designed and optimized, with the comparator placed in a separate layer to prevent spurious radiation. The operating frequency is 15.5-18 GHz, and the antenna exhibited a bandwidth of approximately 15%. The measurement results for the fabricated antenna array align with the simulations and design goals, with sidelobe levels of the beam better than 17 dB in both the E-and H-plane, and a null depth in the Delta pattern of less than -30 dB. The peak gain at the operating frequency is 22.3 dB and the total efficiency of the antenna array is approximately 55%.

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“…5, No. generators based on Marx transistor, a Vivaldi antenna, and a balun [11]. Moreover, a transmitter unit which is a transverse electromagnetic flared (TEM) horn antenna with a pulse shaping circuit consisting of a crystal oscillator with an oscillation frequency of 10 MHz and an amplitude in the range of ±1 V has been developed for imaging of water pipelines [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

Jahara Baun,

Genevie Janairo,

Concepcion II

et al. 2024

IJCDS

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Real-time monitoring of output electrical parameters of the transmitted signals in a capacitive resistivity underground imaging system is necessary because these are significant in the calculation of underground resistivity, however, machine learning has not yet been applied in this application to improve the accuracy of measurement. This study aims to develop and select the best prediction models that can be implemented for a digital measuring unit suitable for capacitive resistivity underground imaging. Three deep neural network models namely Elman recurrent neural network (ERNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were explored to build prediction models for the current and voltage of the transmitter circuit. The prediction models' performance was assessed using mean squared error (MSE), which is reduced to its absolute lowest value. The result shows that the best-trained models for current and voltage prediction are the ERNN models with configurations of 900-600-500 hidden neurons network with training MSE of 9.82 X 10 -9 and the configured 1300-1000-900 hidden neurons with training MSE of 0.465, respectively. With the help of the prediction models, it would be possible to measure current and voltage output accurately, allowing simultaneous data acquisition while avoiding the need for a separate measuring device.

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Design of a High-Power Gaussian Pulse Transmitter for Sensing and Imaging of Buried Objects

Cited by 12 publications

References 39 publications

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

A Compact Monopulse Antenna Array With Suppressed Mutual Coupling Using Broadband Schiffman Phase Shifters

Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

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