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25 pages, 14985 KiB

Open AccessArticle

High-Speed Target HRRP Reconstruction Based on Fast Mean-Field Sparse Bayesian Unrolled Network

by Hang Dong, Fengzhou Dai and Juan Zhang

Remote Sens. 2025, 17(1), 8; https://doi.org/10.3390/rs17010008 - 24 Dec 2024

Cited by 1 | Viewed by 574

The rapid and accurate reconstruction of the high-resolution range profiles (HRRPs) of high-speed targets from incomplete wideband radar echoes is a critical component in space target recognition tasks (STRTs). However, state-of-the-art HRRP reconstruction algorithms based on sparse Bayesian learning (SBL) are computationally expensive and require the manual selection of prior scale parameters. To address these challenges, this paper proposes a model-driven deep network based on fast mean-field SBL (FMFSBL-Net) for the HRRP reconstruction of high-speed targets under missing data conditions. Specifically, we integrate a precise velocity compensation and HRRP reconstruction into the mean-field SBL framework, which introduces a unified SBL objective function and a mean-field variational family to avoid matrix inversion operations. To reduce the performance loss caused by mismatched prior scale parameters, we unfold the limited FMFSBL iterative process into a deep network, learning the optimal global prior scale parameters through training. Additionally, we introduce a sparsity-enhanced loss function to improve the quality and noise robustness of HRRPs. In addition, simulation and measurement experimental results show that the proposed FMFSBL-Net has a superior reconstruction performance and computational efficiency compared to FMFSBL and existing state-of-the-art SBL framework type algorithms. Full article

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15 pages, 953 KiB

Open AccessTechnical Note

Micro-Motion Parameter Extraction for Ballistic Missile with Wideband Radar Using Improved Ensemble EMD Method

by Nannan Zhu, Jun Hu, Shiyou Xu, Wenzhen Wu, Yunfan Zhang and Zengping Chen

Remote Sens. 2021, 13(17), 3545; https://doi.org/10.3390/rs13173545 - 6 Sep 2021

Cited by 19 | Viewed by 3522

Abstract

Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength. Full article

(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)

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16 pages, 8346 KiB

Open AccessArticle

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 2618

Abstract

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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