Sparsity-based three-dimensional image reconstruction for near-field MIMO radar imaging

Download

index.pdf

Date

2019-01-01

Author

Öktem, Sevinç Figen

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

167
views

145
downloads

Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, airport security, concealed weapon detection, and surveillance. The successful operation of these radar imaging systems highly depends on the quality of the images reconstructed from radar data. Since the underlying scenes can be typically represented sparsely in some transform domain, sparsity priors can effectively regularize the image formation problem and hence enable high-quality reconstructions. In this paper, we develop an efficient three-dimensional image reconstruction method that exploits sparsity in near-field MIMO radar imaging. Sparsity is enforced using total variation regularization, and the reflectivity distribution is reconstructed iteratively without requiring computation with huge matrices. The performance of the developed algorithm is illustrated through numerical simulations. The results demonstrate the effectiveness of the sparsity-based method compared to a classical image reconstruction method in terms of image quality.

Subject Keywords

Electrical and Electronic Engineering, General Computer Science

URI

https://hdl.handle.net/11511/39172

Journal

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES

DOI

https://doi.org/10.3906/elk-1902-85

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Performance evaluation of similarity measures for dense multimodal stereovision Yaman, Mustafa; Kalkan, Sinan (SPIE-Intl Soc Optical Eng, 2016-05-01) Multimodal imaging systems have recently been drawing attention in fields such as medical imaging, remote sensing, and video surveillance systems. In such systems, estimating depth has become possible due to the promising progress of multimodal matching techniques. We perform a systematic performance evaluation of similarity measures frequently used in the literature for dense multimodal stereovision. The evaluated measures include mutual information (MI), sum of squared distances, normalized cross-correlat...
Robust control for line-of-sight stabilization of a two-axis gimbal system Baskin, Mehmet; Leblebicioğlu, Mehmet Kemal (The Scientific and Technological Research Council of Turkey, 2017-01-01) Line-of-sight stabilization against various disturbances is an essential property of gimbaled imaging systems mounted on mobile platforms. In recent years, the importance of target detection from higher distances has increased. This has raised the need for better stabilization performance. For that reason, stabilization loops are designed such that they have higher gains and larger bandwidths. As these are required for good disturbance attenuation, sufficient loop stability is also needed. However, model un...
Deep learning-based reconstruction methods for near-field MIMO radar imaging Manisalı, İrfan; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2022-6-29) Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, airport security, and surveillance. These computational imaging systems reconstruct the three-dimensional scene reflectivity distribution from the radar data. Hence their imaging performance largely depends on the image reconstruction method. The analytical reconstruction methods suffer from either low image quality or high computational cost. In fact, sparsity-bas...
Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big Data Kucukkececi, Cihan; Yazıcı, Adnan (Institute of Electrical and Electronics Engineers (IEEE), 2019-01-01) Wireless Multimedia Sensor Networks (WMSN), for object tracking, have been used as an emerging technology in different application areas, such as health care, surveillance, and traffic control. In surveillance applications, sensor nodes produce data almost in real-time while tracking the objects in a critical area or monitoring border activities. The generated data is generally treated as big data and stored in NoSQL databases. In this paper, we present a new object tracking approach for surveillance applic...
Collaborative mobile target imaging in ultra-wideband wireless radar sensor networks Arık, Muharrem; Akan, Özgür Barış; Department of Electrical and Electronics Engineering (2008) Wireless sensor networks (WSN) have thus far been used for detection and tracking of static and mobile targets for surveillance and security applications. However, detection and tracking do not suffice for a complete satisfaction of these applications and an accurate target classification. To address this need, among various target classification methods, imaging of target yields the most valuable information. Nevertheless, imaging of mobile targets moving over an area requires networked and collaborative d...

Citation Formats

S. F. Öktem, “Sparsity-based three-dimensional image reconstruction for near-field MIMO radar imaging,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, pp. 3282–3295, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39172.