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2-D Joint Sparse Reconstruction and Micro-Motion Parameter Estimation for Ballistic Target Based on Compressive Sensing

by Jiaqi Wei, Shuai Shao, Lei Zhang and Hongwei Liu

Sensors 2020, 20(16), 4382; https://doi.org/10.3390/s20164382 - 5 Aug 2020

Viewed by 2619

The sparse frequency band (SFB) signal presents a serious challenge to traditional wideband micro-motion curve extraction algorithms. This paper proposes a novel two-dimension (2-D) joint sparse reconstruction and micro-motion parameter estimation (2D-JSR-MPE) algorithm based on compressive sensing (CS) theory. In this technique, the 2D-JSR signal model and the micro-motion parameter dictionary are established based on the segmented SFB echo signal, in which the idea of piecewise effectively reduces the model complexity of ballistic target. With the accommodation of the CS theory, the 2D-JSR-MPE of the echo signal is transformed into solving a sparsity-driven optimization problem. Via an improved orthogonal matching pursuit (OMP) algorithm, the high-resolution range profiles (HRRP) can be reconstructed accurately, and the precise micro-motion curves can be simultaneously extracted on phase accuracy. The employment of 2-D joint processing can effectively avoid the interference of the sparse reconstruction error caused by cascaded operation in the subsequent micro-motion parameter estimation. The proposed algorithm benefits from the anti-jamming characteristic of the SFB signal and 2-D joint processing, thus remarkably enhancing its accuracy, robustness and practicality. Extensive experimental results are provided to verify the effectiveness and robustness of the proposed algorithm. Full article

(This article belongs to the Section Physical Sensors)

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14 pages, 1407 KiB

Open AccessArticle

Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation

by Lu Chen, Daping Bi and Jifei Pan

Sensors 2018, 18(10), 3553; https://doi.org/10.3390/s18103553 - 19 Oct 2018

Cited by 9 | Viewed by 2904

Abstract

To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with

M

sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of

M^{2}

signal sources. To solve the problem of direction finding [...] Read more.

To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with

M

sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of

M^{2}

signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise. A sparse Bayesian learning algorithm is used to estimate one dimension angle. By a joint covariance matrix, another dimension angle is estimated, and the estimated angles from two dimensions can be automatically paired. The simulation results show that the number of DOA signals that can be estimated by the proposed two-parallel nested arrays is much larger than the number of sensors. The proposed two-dimensional DOA estimation algorithm has excellent estimation performance. Full article

(This article belongs to the Section Physical Sensors)

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