Bambam Kumar

Non-invasive concealed weapon detection and identification using V band millimeter wave imaging radar system

This paper presents a millimeter wave imaging radar system operating at 60 GHz for non invasive and nondestructive concealed weapon detection and identification. In order to mimic, the concealed weapon target, an aluminum toy gun covered with cloth has been used. Different signal processing techniques were applied in order to fully detect and identify the concealed weapon target. Techniques like; discrete convolution, mean and standard deviation based thresholding and canny edge detection were applied and their applicability was tested towards successful hidden target identification. The correct target location and shape identification validates the MMW radars capability towards standoff concealed object detection and identification. Further, the proposed technique can be used with other different kinds of weapon targets for applications requiring personal screening and security check.

Optimization of image processing techniques to detect and reconstruct the image of concealed blade for MMW imaging system

The concealed weapon, like blade, detection and identification is one of the most puzzling task faces by security agency. Researchers have demonstrated MMW imaging systems to detect concealed targets like gun, knife and scissors but detection of small size target like blade with different orientation is still challenging due to resolution limitation of MMW imaging system. The success of small size concealed target detection depends upon scanning step size of imaging system and dielectric property of covering cloths and hidden object. Therefore, resolution enhancement techniques may play a very important role for small size concealed target detection. To perceive such challenges, active V-band MMW radar conjunction with image processing techniques has been demonstrated for detection and identification of concealed blade and obtained two dimensional good quality of images of concealed blade under different cloths at various angle. For this purpose, a critical analysis of various signal and image processing has been carried out and integrated following algorithms like singular value decomposition (SVD) for clutter reduction, discrete wavelet transform (DWT) for resolution enhancement, thresholding for target detection and in last artificial neural network (ANN) based algorithm for rotation invariant target identification. An image processing based methodology has been proposed by which the concealed target like blade can be successfully detected.

DEVELOPMENT OF AN ADAPTIVE APPROACH FOR IDENTIFICATION OF TARGETS (MATCH BOX, POCKET DIARY AND CIGARETTE BOX) UNDER THE CLOTH WITH MMW IMAGING SYSTEM

Non-metallic objects, such as match box and cigarette box, detection and identification are quite an essential task during personal screening from standoff distance to protect the public places like the airport. Although various imaging sensors such as microwave, THz, infrared and MMW with signal processing techniques have been demonstrated by the researchers for concealed weapon detection, it is still a challenging task to detect and identify different types of small size targets such as matchbox, pocket diary and cigarette box simultaneously. Therefore, in this paper, an attempt has been made to develop such an algorithm/methodology by which different types of small targets, such as a matchbox and cigarette box, which is fully or half-filled or empty and pocket diary at different orientations beneath various cloths can be detected and identified with an MMW radar system. For this purpose, an optimal method has been proposed to form an image, and after that, in post processing a novel adaptive approach for detection and identification of considered targets has been proposed. The data were collected by MMW system at V-band (59 GHz–61 GHz). The proposed algorithm/methodology gives a quite satisfactory result.

Non-metallic pipe detection using SF-GPR: A new approach using neural network

Currently, mean subtraction, median removal, singular value decomposition (SVD), Principal component analysis (PCA) and Independent component analysis (ICA) areverypopular approaches to extract the buried target information in presence of clutter and background noise for GPR applications. Clutter and background reduction and detection of low dielectric constant buried object with variable soil conditionsare the challenging tasks in GPR. But available techniques are not able to extract the non-metallic target information, due to low dielectric constant. Therefore, this paper proposes a neural network and statistical mean to standard deviation threshold based approach for subtracting background and for enhancing the detection of low dielectric constant buried object. ANN approach is based on the collection of large amount background data with soil moisture variation. These background data statistically analysed to compute the mean to standard deviation thresholding. After that, motion filter estimate the actual pixel intensity of PVC pipe in linear manner. The results show that the enhanced target detection and background subtraction are achieved directly from proposed trained neural network.

Critical analysis of signal processing techniques for concealed weapon identification with MMW (60 GHz) imaging radar system

Detection of concealed weapons from standoff distance under various fabric sheets is extremely essential for safety as well as security of public and their assets. The success of detection depends upon several factor such as dielectric property of covering cloths, collection of back scattered data, different types of hidden object and their shape as well as size, standoff distance from imaging system and probability of false alarm due to undesirable targets. To focus such problems, in this paper, we have attempted to detect and identify a gun as a concealed target under different cloths. The V-band millimeter wave radar data of different targets covering with various fabric sheets has been taken and several signal post-processing techniques were critically analyzed like, average trace subtraction, intensity transformation, mean and standard deviation based global thresholding and Laplacian of Gaussian (LoG) edge detector and their applicability was tested for successful hidden target identification. The results for concealed target under various fabric sheets were presented. Proposed millimeter wave (MMW) data processing approach produce fine quality of images with fewer false alarms. This technique seems to be having good potential that may be utilized for various practical applications like security as well as safety of human and public property(i.e., airports and buildings etc.).