A novel application of artificial neural network for recognition of target behind the wall

Singh, Akhilendra Pratap; Dwivedi, Shridhar; Jain, P. K.

doi:10.1002/mop.32020

Cited by 5 publications

(14 citation statements)

References 21 publications

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“…C-scan provides valuable information about the complete shape of a target in terms of width and height. 2-D images (30x25) are generated from 3D C-scan data by taking the slice at particular range bin that is decided by the range profile of targets 26 . The obtained images have been normalised and they are called raw images 30 .…”

Section: Experimental Set Up and Data Collectionmentioning

confidence: 99%

“…Frequency range from 1 GHz to 3 GHz (BW=2 GHz) chosen for shape identification of the target. The range and cross-range resolution of SFCW radar are very important parameters to distinguish between two closely spaced targets in down range and lateral directions, respectively 26,29 . The range resolution is (Δy=C/2PΔf=7.5 cm), where C is speed of EM wave, N is the number of frequency points and Δf is the frequency step size.…”

Section: Experimental Set Up and Data Collectionmentioning

confidence: 99%

“…3 D C-scan data has been collected for the metallic, wooden, tiles and empty scene target of different shapes and size in different orientation and converted in 2D (M×N) from 3D C-scan data by taking the slice at particular range bin that is decided by the range profile of targets 26 . A total of 120 samples are captured by through-wall radar imaging system.…”

Section: Data Organisation For Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The known wall parameters are used for velocity correction so that focused image can be obtained 29 . The average subtraction method is used as background subtraction to remove the clutter as well as the noise of image 26 . Mathematically, X NFREE may be given by following equation:…”

Section: Data Organisation For Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Although, the most important disadvantage of these methods is that, it can only find the presence or absence of targets. The shape of targets has been detected by using artificial neural network 26 . In this paper, authors used a binary image for training the neural network.…”

Section: Introductionmentioning

confidence: 99%

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Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

et al. 2021

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In Through-wall Imaging (TWI) system, shape-based identification of the hidden target behind the wall made of any dielectric material like brick, cement, concrete, dry plywood, plastic and Teflon, etc. is one of the most challenging tasks. However, it is very important to understand that the performance of TWI systems is limited by the presence of clutter due to the wall and also transmitted frequency range. Therefore, the quality of obtained image is blurred and very difficult to identify the shape of targets. In the present paper, a shape-based image identification technique with the help of a neural network and curve-fitting approach is proposed to overcome the limitation of existing techniques. A real time experimental analysis of TWI has been carried out using the TWI radar system to collect and process the data, with and without targets. The collected data is trained by a neural network for shape identification of targets behind the wall in any orientation and then threshold by a curve-fitting method for smoothing the background. The neural network has been used to train the noisy data i.e. raw data and noise free data i.e. pre-processed data. The shape of hidden targets is identified by using the curve fitting method with the help of trained neural network data and real time data. The results obtained by the developed technique are promising for target identification at any orientation.

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Section: Experimental Set Up and Data Collectionmentioning

confidence: 99%

Section: Experimental Set Up and Data Collectionmentioning

confidence: 99%

Section: Data Organisation For Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Data Organisation For Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

et al. 2021

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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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Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

2022

Mobile Information Systems

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In order to study the pitch evaluation of Matouqin chamber music performance based on artificial neural network, this paper puts forward the relevant theories in the fields of human ear auditory perception system, auditory psychology, music theory knowledge, and pattern recognition. This paper extracts the auditory image features of chords and then establishes a sparse representation classifier model for chord recognition and classification. Scale-invariant feature transformation (SIFT) and spatial pyramid matching (SPM) are used to extract the detailed features of chord auditory images. The experimental results show that the highest correct recognition rate of the chord recognition algorithm based on the auditory image proposed in this paper is 76.2%, which is 20.4% higher than that of MFCC feature based on human auditory characteristics.

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A novel application of artificial neural network for recognition of target behind the wall

Cited by 5 publications

References 21 publications

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

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

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

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