Object Recognition in High-Resolution Indoor THz SAR Mapped Environment

Batra, Aman; Sheikh, Fawad; Khaliel, Maher; Wiemeler, Michael; Göhringer, Diana; Kaiser, Thomas

doi:10.3390/s22103762

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…The recognition of tissue burns has been investigated using CNN which proved to be more robust than existing algorithms in [110]. Recognition of indoor objects has been addressed using THz based synthetic aperture radar and machine learning algorithm for localization and detection of the target object [111]. Deep neural network models have also been explored for automated classification of glucose [112] and materials based on THz TDS [113].…”

Section: B Classification Detection and Identificationmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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Following the recent progress in the development of Terahertz (THz) generation and detection, THz technology is being widely used to characterize test sample properties in various applications including nondestructive testing, security inspection and medical applications. In this paper, we have presented a broad review of the recent usage of artificial intelligence (AI) particularly, deep learning techniques in various THz sensing, imaging, and spectroscopic applications with emphasis on their implementation for medical imaging of cancerous cells. Initially, the fundamentals principles and techniques for THz generation and detection, imaging and spectroscopy are introduced. Subsequently, a brief overview of AI -machine learning and deep learning techniques is summarized, and their performance is compared. Further, the usage of deep learning algorithms in various THz applications is reported, with focus on metamaterials design and classification, detection, reconstruction, segmentation, parameter extraction and denoising tasks. Moreover, we also report the metrics used to evaluate the performance of deep learning models and finally, the existing research challenges in the application of deep learning in THz cancer imaging applications are identified and possible solutions are suggested through emerging trends. With the continuous increase of acquired THz data -sensing, spectral and imaging, artificial intelligence has emerged as a dominant paradigm for embedded data extraction, understanding, perception, decision making and analysis. Towards this end, the integration of state-of-the-art machine learning techniques such as deep learning with THz applications enable detailed computational and theoretical analysis for better validation and verification than modelling techniques that precede the era of machine learning. The study will facilitate the large-scale clinical applications of deep learning enabled THz imaging systems for the development of smart and connected next generation healthcare systems as well as provide a roadmap for future research direction.

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Section: B Classification Detection and Identificationmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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“…The component of high-frequency vibration along the line of sight (LOS) is the main factor affecting the signal phase according to (6). Hence, the calculation can be simplified by setting C to 1 without affecting the accuracy of the result [23].…”

Section: Thz-sar High-frequency Vibration Error Model and Effectmentioning

confidence: 99%

“…Compared with conventional SAR operating in microwaves bands, THz-SAR offers high imaging resolution, good penetrability, and high frame rate imaging capability, giving it potential applications in many fields, such as video SAR, security checks, indoor object recognition, detecting micro-motion targets and stealth targets, etc. [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Estimation of High-Frequency Vibration Parameters for Airborne Terahertz SAR Using Chirplet Decomposition and LS Sequential Estimators

Hao

Sun

et al. 2022

Remote Sensing

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Due to the short wavelength of the terahertz wave, airborne terahertz synthetic aperture radar (THz-SAR) suffers from echo phase errors caused by the high-frequency vibration of the platform. These errors will result in defocusing and the emergence of ghost targets, which will degrade the quality of the image. Therefore, it is necessary to compensate for phase errors in order to bring the image into focus. This paper proposes a multi-component high-frequency vibration parameter estimation method based on chirplet decomposition and least squares (LS) sequential estimators, which differs from other methods that can only be applied to simple harmonic vibrations. In particular, we first obtain the instantaneous chirp rate (ICR) of the signal by chirplet decomposition. Then, we employ the LS sequential estimators in conjunction with separable regression technique (SRT) to estimate vibration parameters. The estimated parameters are subsequently used to re-establish the ICR components for each vibration component and these parameters are further re-estimated to improve their accuracy. Based on the estimated parameters, phase compensation functions can be constructed to suppress the defocusing and ghost targets in airborne THz-SAR imaging. Simulated results on point targets and distributed imaging scenes demonstrate that the proposed method is accurate and reliable even at low signal-to-noise ratios (SNRs).

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“…Even though the THz spectrum is limited by sensing range caused by free space path loss and high atmospheric attenuation, however it exhibits great potential to enable imaging applications in the short range or near-field, especially in the indoor environments which could be the basis of more applications for example, household 2 robotics, health monitoring, building fire detection, etc. [7], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

Gezimati

Singh

2023

IEEE Photonics J.

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High resolution imaging technique will play an essential role in the future smart imaging applications to provide potential solutions for accurate detection, monitoring and classification of the target. The potential of synthetic aperture radar (SAR) systems to acquire high resolution images has opened new frontiers and led to its exploration in new applications. The conventional microwave radar imaging suffers limited range resolution in the sub-millimeter wave range and optical imaging methods are constrained by the diffraction limit. In the terahertz (THz) regime of the spectrum, the SAR systems could be operated in all the weather, all the time and are capable of penetrating through clouds, smoke, dust etc. to achieve high resolution images beyond the diffraction limit. The curved SAR presents a SAR system created by circularly encircling the target scene. The Circular SAR and Spiral SAR which are based on a circular and cylindrical spiral scanning trajectory, respectively are the scanning methods used for curved SAR. In this paper, we have explored a new toolbox that enables the rapid development of near-field THz-SAR imaging systems and generation of large near-field THz imaging scenarios with the goal to support and facilitate data driven research such as deep learning in the THz community through synthetically generated SAR images as well as its potential for THz based high resolution and 2D/3D near-field imaging application. Initially, the potential advantages of THz-SAR over Microwave SAR and optical imaging methods are presented. Further, a frequency modulated continuous wave (FMCW) radar testbed is simulated in multi-input multi-output (MIMO) configuration for the target frequency band. Moreover, the circular and cylindrical SAR scanning methods are explored for near-field imaging of point targets to obtain 2D and 3D THz-SAR images, respectively. Also, the radar images are reconstructed using the Back Propagation and Polar Formatting Algorithms, focusing on short range applications like indoor environments. Finally, the SAR performance evaluation metrics are reported and a roadmap for future work is presented.

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Object Recognition in High-Resolution Indoor THz SAR Mapped Environment

Cited by 6 publications

References 27 publications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Estimation of High-Frequency Vibration Parameters for Airborne Terahertz SAR Using Chirplet Decomposition and LS Sequential Estimators

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

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