Wall parameters estimation based onsupport vector regression for through wall radar sensing

Chen, Xi; Chen, Weidong

doi:10.1186/s13634-015-0244-8

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…Machine learning based methods have been shown to produce quick and precise results but are less reliable as only simulation-based validations are presented. [10][11][12][13][14] Moreover, these models are effective for one and two targets but are inapplicable with multiple targets. A comparison between different machine learning methods is described in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies have explored methods for estimating wall parameters using machine learning. Machine learning based methods have been shown to produce quick and precise results but are less reliable as only simulation‐based validations are presented 10–14 . Moreover, these models are effective for one and two targets but are inapplicable with multiple targets.…”

Section: Introductionmentioning

confidence: 99%

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A novel technique for wall parameter estimation for through wall imaging systems

Singh

2023

Micro & Optical Tech Letters

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Accurate estimating of wall parameters is crucial for precisely locating targets and obtaining well‐focused through wall radar images. Recently, machine learning has been introduced for quick and precise estimation of wall parameters. One of the significant concerns with this approach is the generation of training and testing data, which require the fabrication of walls with different permittivity, thickness, and conductivity. Creating walls with varying permittivity, thickness, and conductivity can be difficult and expensive. Therefore, a cost‐effective method for estimating wall parameters is proposed in this paper. The proposed method uses an electromagnetic approach to train the artificial neural network to retrieve the relative permittivity and thickness of the wall. The proposed estimation approach has the advantages of high precision and low computational complexity. The performance of the proposed approach is validated on simulated and experimental data. The applicability of the proposed method has been shown with measured data from two real building walls. It is shown that the proposed method retrieved wall parameters with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel technique for wall parameter estimation for through wall imaging systems

Singh

2023

Micro & Optical Tech Letters

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“…To overcome these challenges, diferent methods and techniques are employed [6][7][8][9]. Te TWR challenge can also be addressed by machine learning algorithms [10,11].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network

Ghorbani

Soleimani

2022

International Journal of Antennas and Propagation

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The purpose of this paper is to present a deep learning model that simultaneously estimates targets and wall parameters in through-the-wall radar (TWR). As a result of the complexity of the environments in which through-the-wall radars operate, TWR faces many challenges. The propagation of radar signals through walls is further delayed and attenuated than in free space. Therefore, the targets are less able to be detected and the images of the targets are distorted and defocused as a consequence. To address the above challenges, two modes are considered in this work: single targets and two targets. In both cases, permittivity and wall thickness are considered, along with the target’s center in two dimensions and the permittivity of targets. Therefore, in the case of a single target, we estimate five values, whereas in the case of two targets, we estimate eight values simultaneously, each representing the mentioned parameters. As a result of using deep neural networks to solve the task of target locating problem in TWR, the model has a better chance of learning and increased accuracy if it involves more parameters (such as wall parameters and permittivity of the wall) in the target location problem. In this way, the accuracy of target locating improved when two wall parameters were considered in problem. A deep neural network model was used to estimate wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets with 99% accuracy in single-target and two-target modes.

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“…This method provides quick and precise estimates, but it requires the need for measurement to be performed by removing the wall to eliminate unwanted received signal other than the wall so that received signal contains information about walls only, which limits its usefulness in practice. The fourth is a machine learning-based method in which an SVM-based regression model is created to build a relationship between scattered field and wall parameters [8][9][10]. Deep learning models have also been introduced for simultaneous estimation of wall parameters and target location [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A robust and efficient wall parameter estimation approach for through wall radar

Singh

2022

Int. J. Microw. Wireless Technol.

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In through-wall radar system, the wall parameters, including permittivity, and wall thickness are of crucial importance for locating targets precisely. Recently, to obtain a quick and accurate estimation of wall parameters, an approach based on machine learning was introduced. However, these approaches are less reliable as only simulations results are presented. One of the major concerns with machine learning-based approaches is the generation of training and testing data which require fabrication of wall with different permittivity, thickness, and conductivity. Creating walls with different permittivity, thickness, and conductivity can really be challenging and expensive. Therefore, an effort has been made in this paper to establish a cost-effective and robust machine learning-based wall parameter estimation process with the usage of transmission line method and artificial neural network. The implementation and efficacy of proposed approach have been demonstrated through simulation and experimental results. The proposed approach quickly and accurately predicted the wall relative permittivity and thickness of real building wall. The merit of proposed approach is that it is less complex and computational efficient as it can extract wall parameters from only one measurement and therefore can be used in conjunction with any commercial through-wall radar systems.

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Wall parameters estimation based onsupport vector regression for through wall radar sensing

Cited by 4 publications

References 35 publications

A novel technique for wall parameter estimation for through wall imaging systems

A novel technique for wall parameter estimation for through wall imaging systems

Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network

A robust and efficient wall parameter estimation approach for through wall radar

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