Browsing by Author "Muqaibel, Ali"

Now showing 1 - 6 of 6
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    Aspect dependent efficient multipath ghost suppression in TWRI with compressive sensing
    (Cambridge University Press and the European Microwave Association, 2017) Muqaibel, Ali; Abdalla, Abdi T; Alkhodary, Mohammad; AlDharrab, Suhail
    In through-the-wall radar imaging, multipath propagation can create ghost targets, which can adversely affect the image reconstruction process. However, unlike genuine targets, ghost positions are aspect-dependent, which means their position changes with the transceiver location. This paper proposes efficient ghost suppression methods exploiting aspect dependence feature under compressive sensing framework. This paper proposes a generalized signal model that accommodates for the reflections of the front-wall and target-to-target interactions, making the scheme more practical, yet the knowledge of the location of reflecting geometry is not a requirement as in most of the recent literatures. In addition, the sensing matrix is greatly reduced making the methods more attractive. Moreover, this paper investigates the influence of array configurations by examining two antenna array configurations: multimonostatic, and single-view bistatic configurations. Results based on synthesized data and real experiment show that the proposed method can greatly suppress multipath ghosts and hence increase signal-to-clutter ratio.
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    Extended Targets Modelling and Block Agnostic Sparse Reconstruction in Through-the-Wall Radar Imaging: A Different Perspective
    (Academic Publication Council-Kuwait University, 2019) Abdalla, Abdi T; Alkhodary, Mohammad; Muqaibel, Ali
    A common target model in through-the-wall radar (TWRI) imaging literature obeys the point target (PT) assumption in which a target is hypothesized to occupy a single pixel. Unlike PTs, the received signal reflected from extended target (ET) is an integration of the scattered signals from various parts of the same target. For high resolution images, a generalized model is needed to encompass the ETs. In this paper, we suggest a different but realistic ET reconstruction approach based on agnostic block sparsity. The algorithm does not impose any assumption on the length, number, or the distribution of the blocks. Results based on MATLAB simulation and experimental data show the effectiveness of the proposed reconstruction approach. The applications of the suggested approach are found in civil, rescue, surveillance, and security enforcement sectors, where an accurate tracking of large targets behind walls is vital.
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    Indoor target localization using marginal antenna with virtual radars support
    (2017) Muqaibel, Ali; Abdalla, Abdi T; Alkhodary, Mohammad
    In urban target localization, the presence of walls creates virtual radars (VRs), which can be exploited to aid in localization process. The fact that multipath changes with the radar locations, which are referred to as aspect dependence property, enable us to find a radar location, which reduces wall uncertainties. This paper proposes single-antenna target localization in an enclosed structure taking advantage of VRs. Using ultra-wideband signals, we can resolve the target returns and estimate the correct location by solving monostatic loci at real and VR locations. Simulation results show that the method can precisely and accurately localize the target for a wide range of timing errors
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    Multipath Ghost in Through the Wall Radar Imaging: Challenges and Solutions
    (Wiley Online Library, 2018) Abdalla, Abdi T; Alkhodary, Mohammad; Muqaibel, Ali
    In through-the-wall radar imaging (TWRI), the presence of front and side walls causes multipath propagation, which creates fake targets called multipath ghosts. They populate the scene and reduce the probability of correct target detection, classification, and localization. In modern TWRI, specular multipath exploitation has received considerable attention for reducing the effects of multipath ghosts. However, this exploitation is challenged by the requirements of the reflecting geometry, which is not always available. Currently, the demand for a high radar image resolution dictates the use of a large aperture and wide bandwidth. This results in a large amount of data. To tackle this problem, compressive sensing (CS) is applied to TWRI. With CS, only a fraction of the data are used to produce a high-quality image, provided that the scene is sparse. However, owing to multipath ghosts, the scene sparsity is highly deteriorated; hence, the performance of the CS algorithms is compromised. This paper presents and discusses the adverse effects of multipath ghosts in TWRI. It describes the physical formation of ghosts, their challenges, and existing suppression techniques.
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    Path-loss Compensation in Through-the-wall Radar Imaging
    (IEEE, 2016) Alahmed, Ahmed; Alafif, Omar; Abdalla, Abdi T; Muqaibel, Ali
    In through-the-wall radar imaging (TWRI), pathloss can result in misleading targets. The fact that distant targets experience more path-Ioss than near targets is an essential feature of propagating signals. This paper proposes a path-Ioss compensator, which reverts the unavoidable loss in power by using different path-Ioss models. This will be effectively done by developing and incorporating the path-Ioss compensator matrix. The signal model is generalized so that it includes the front wall reflections. Many research papers in the field do not account for the differences in the propagation losses of the paths due to its complexity and mutable behavior, wh ich can bring great difficulties to establish distance path-Ioss model. Simulation and experimental results are presented to show how the proposed approach can efficiently compensate for far targets and thus enhancing the signal-to-c1utter ratio and reducing the normalized mean square error
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    Through-the-Wall Radar Imaging Exploiting Pythagorean Coprime-Based Synthetic Apertures with Sparse Reconstruction
    (2017) Muqaibel, Ali; Abdalla, Abdi T; Alkhodary, Mohammad; Alawsh, Saleh
    Through-the-wall radar imaging (TWRI) is receiving a considerable attention recently due to its diverse applications. One of the impinging challenges is the multipath propagation from the surrounding environment and even targets themselves. Multipath propagation produces ghost targets which populate the scene and not only create confusion with genuine targets but deteriorate the performance of compressive sensing (CS) algorithms. Unlike genuine targets, ghost locations are aspect dependent. Successful exploitation of this feature is dictated by the subarray selection modality. Up to this far, random multiple subarrays selection is the practice in exploiting aspect dependence. This paper suggests new subarray configurations based on Pythagorean triple which is made of pairwise coprime numbers that can enhance ghost suppression process and improve image resolution. The sensing matrices of the proposed subarrays are developed and analyzed. The paper investigates the effectiveness of generating the images from all the elements in the array as opposed to generating the images by processing designed subarrays individually and then combining the results. This comparison is done in view of multipath ghost suppression exploiting aspect dependence feature. Results based on synthesized data and electromagnetic propagation simulator show the effectiveness of the proposed arrays.