A Three-Dimensional Enhanced Imaging Method on Human Body for Ultra-Wideband Multiple-Input Multiple-Output Radar

Abstract

High-resolution three-dimensional (3D) images can be acquired by the planar Multiple-Input Multiple-Output (MIMO) array radar making future work like detection and tracking easier. However, regarding portability and to save the costs of radar system, MIMO radar array adopts sparse type with limited number of antennas, so the imaging performance of a MIMO radar system is limited. In this paper, the 3D back projection imaging algorithm is verified by the experimental results of planar MIMO array for human body and an enhanced radar imaging method is proposed. The Lucy-Richardson (LR) algorithm based on deconvolution that is normally used for optical images is applied in radar images. Since the LR algorithm can amplify the noise level in a noise-contaminated system, a regularization method based on the Total Variation constraint is further incorporated in the LR algorithm to suppress the ill-posed characteristics. The proposed method shows a higher image Signal-to-Noise Ratio, a faster rate of convergence, a higher structure similarity and a smaller relative error compared to some similar methods. In the meantime, it also reduces the loss of image information after image enhancement and improves the radar image quality (get less grating lobe and clearer human limbs). The proposed method overcomes the disadvantages mentioned above and is verified by simulation experiment and real data measurement.

DOA Estimation of Strictly Noncircular Sources in Wireless Sensor Array Network via Block Sparse Representation

“…It gets well utilized in many fields, such as radar, medical diagnosis, sources location, etc. [2], [3]. It's evident that much work has been done on DOA estimation, accompanied by a series of corresponding high-resolution estimation algorithms [4]- [7].…”

“…As the l-th sub-block of Uθ 1w and Uθ 2w , Uθ l 1w and Uθ l 2w are equal to the (Fl − F + 1)th to (Fl)th rows of Uθ 1w and Uθ 2w , respectively. Then, two new column vectors are introduced for conveniently expressing, that are t 1 = [tθ1 1 , tθ2 1 , . .…”

Efficient Large Sparse Arrays Synthesis by Means of Smooth Re-Weighted L1 Minimization

“…Antenna arrays are one of the most important technologies that allows responding to the challenges of the close future communication systems, particularly in the 5G and IoT frameworks [1][2][3][4]. In this perspective, the use of sparse, non-regular, arrays is particularly appealing, since it has been shown that a sparse array is often capable of achieving the same performance of regular lattice ones, with a much lower number of radiating elements, and a strong reduction of the issue of grating lobes.…”

Weighted Block Sparse Recovery Algorithm for High Resolution DOA Estimation with Unknown Mutual Coupling

“…Meanwhile, inspired by the development of multiple-input multiple-output (MIMO) communication technology, MIMO radar has recently been proposed to obtain more degrees of freedom and high resolution for DOA estimation [4]. It has become a hot spot in the fields of autopilot, target location, and medicine [5][6][7]. One problem we must consider is that with a fixed array size, as the number of antennas is increased (or the distance between array elements is decreased), this inevitably brings about mutual coupling effect in the received data.…”