Development of an Adaptive Approach for Identification of Targets (Match Box, Pocket Diary and Cigarette Box) Under the Cloth With MMW Imaging System

Kumar, Bambam; Upadhyay, Rohit; Singh, and Dharmendra

doi:10.2528/pierb17040804

Cited by 5 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reflections from the low dielectric targets are generally weak compared to clutter from the interior and exterior of the wall, making detection of such targets difficult. Efficient clutter reduction technique is required to remove the clutter from the B-scan image to detect low dielectric targets [23]. After pre-processing, different commonly used clutter reduction techniques are implemented and the results of this are discussed here.…”

Section: Clutter Reduction Techniquesmentioning

confidence: 99%

Entropy-Based Low-Rank Approximation for Contrast Dielectric Target Detection with Through Wall Imaging System

2019

Self Cite

View full text Add to dashboard Cite

In through wall imaging, clutter plays an important role in the detection of objects behind the wall. In the literature, extensive studies have been carried out to eliminate clutter in the case of targets with the same dielectric. Existing clutter reduction techniques, such as the sub-space approach, differential approach, entropy-based time gating, etc., are able to detect a single target or two targets with the same dielectric behind the wall. In a real-time scenario, it is not necessary that targets with the same dielectric will be present behind the wall. Very few studies are available for the detection of targets with different dielectrics; here we termed it "contrast target detection" in the same scene. Recently, low-rank approximation (LRA) was proposed to reduce random noise in the data. In this paper, a novel method based on entropy thresholding for low-rank approximation is introduced for contrast target detection. It was observed that our proposed method gives satisfactory results.The major contribution to the clutter in TWI is due to reflections from the wall. Different wall removal techniques are proposed in the literature, such as the sub-space projection approach that is used in [8] for wall removal. SVD frequently using the sub-space projection approach for clutter reduction, and it has previously been used to enhance the signal-to-clutter ratio for the application of ground penetrating radar (GPR) [9] and TWI in [10]. In SVD, Eigen-images of the B-scan are determined and used to identify wall clutter and target subspace. In [11], it is stated that the first two Eigen-values correspond to the wall and target, respectively, but [8] shows that wall clutter is spread along with the high dimensional subspace and weak wall singular components interleave with target subcarriers. Recently, the empirical low-rank approximation method was proposed in [12] for seismic data, where all Eigen-values corresponding to the noise subspace were considered to identify the weak signals. If we consider all the Eigen-values along with the Eigen-values corresponding to the signal, then noise is also get added in the signal.Compared to other imaging systems such as GPR and biomedical, TWI has to deal with more severe problems like changes in the propagation environment and sensor positioning [13,14]. Another problem in TWI is the propagation medium, where multiple unknowns and either homogenous or non-homogenous walls are involved [15]. In a real-time scenario, it may be possible that targets with different dielectrics will be present behind the wall. It is challenging to detect low dielectric targets such as wood (≈4) in the presence of metal (≈∞) behind the wall because in TWI images, low dielectric targets are obscured in the presence of high noise. We termed the target detection and imaging problem in which targets with different dielectrics are present as "contrast target detection" and "contrast target imaging", respectively. The contribution of this paper is that first we propose a novel method to detect contrast ...

show abstract

Section: Clutter Reduction Techniquesmentioning

confidence: 99%

Entropy-Based Low-Rank Approximation for Contrast Dielectric Target Detection with Through Wall Imaging System

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The threshold value is calculated as T h = mean + standard deviation (15) The thresholded 2D TWRI shape of considered targets using each imaging algorithm is shown in Figures 4(a)-(p) along with the reference shape of the target. The number of target pixels of reference target shape has been obtained based on a priori information of target size, location, and size of pixel [18][19][20]. As per experimental results with various target samples, a considerable difference between the output images of algorithms from the focusing point of view is observed.…”

Section: Performance Of Imaging Algorithmsmentioning

confidence: 99%

“…Therefore to increase data, synthetic data of three common shapes of various sizes and orientations of target are generated using Boolean values as given in Appendix A [18,19]. The synthetic target shape has been obtained based on a priori information of target size, location, and size of pixel [20]. For example, synthetic data of rectangular shape of sizes (50 × 30), (45 × 25) cm, (55 × 35) cm, (60 × 40) cm, and (65 × 45) at orientations 0, 30, 60, 90, 120, 150, and 180 degrees have been generated.…”

Section: Development Of Model For Shape Identification Of Targetmentioning

confidence: 99%

Analysis of the Imaging Algorithms for Shape Detection and Shape Identification of a Target Using Through-the-Wall Imaging System

Singh¹,

Dwivedi²,

Jain³

2019

PIER B

View full text Add to dashboard Cite

Through-the-Wall Imaging systems are a promising method for on-line applications, especially in disaster areas, where victims are buried under collapsed walls. These applications require such systems to identify the shape of the target. The foremost step while performing the task of shape recognition of stationary targets behind a wall is to first detect the target position, its approximate shape and size, and then, subsequent processing of these images with the use of signal processing techniques for the shape recognition of targets. For determining highly accurate information about target location and its approximate shape, a high-resolution image of the target is required. In literature, various imaging algorithms have been reported, some of which are back projection, delay sum, and frequencywavenumber imaging algorithm. However, the use of these algorithms for shape detection of the target has not been explored so far. Therefore, it becomes essential to explore the use of these algorithms on TWI data to select an effective imaging algorithm for detecting approximate shape and size of the target. For this purpose, an experiment has been performed. The performances of these imaging algorithms have been analyzed and evaluated. The detected target images do not correspond to the actual shape and size of targets; therefore, a novel methodology using an artificial neural network has been presented for predicting the actual shape of the target. From the experimental data, the retrieved result of shape has been found in good agreement with the target original shape.

show abstract

“…where, p(x) is n th order polynomial, 1 2 1 , , ., n p p p + ……… are its coefficients and x is input to polynomial. The coefficients of polynomial are obtained by the curve fitting approach on the basis of coefficient of determination (R 2 ) values which are greater than 0.9 [31][32] .…”

Section: Threshold Decisionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Development of an Adaptive Approach for Identification of Targets (Match Box, Pocket Diary and Cigarette Box) Under the Cloth With MMW Imaging System

Cited by 5 publications

References 26 publications

Entropy-Based Low-Rank Approximation for Contrast Dielectric Target Detection with Through Wall Imaging System

Entropy-Based Low-Rank Approximation for Contrast Dielectric Target Detection with Through Wall Imaging System

Analysis of the Imaging Algorithms for Shape Detection and Shape Identification of a Target Using Through-the-Wall Imaging System

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

Contact Info

Product

Resources

About