Sub‐band mutual‐coherence compensation in multiband fusion ISAR imaging

Xiong, Di; Wang, Junling; Zhao, Lizhi; Yuan, Zhengkun; Gao, Meiguo

doi:10.1049/iet-rsn.2018.5400

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…For the determination of model parameters, some subspace decomposition methods can be used such as Estimating Signal Parameters via Rotational Invariance Technique (ESPRIT) [4]- [5], Matrix Pencil algorithm [6]- [8] and RELAX algorithm [2], as well as transform domain methods such as apFFT [9], which are essentially based on spectral search.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Particle-Based Variational Bayesian Inference for Multi-band Radar Sensing

Hu¹,

Liu²,

Wan³

et al. 2022

Preprint

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Multi-band fusion is an important technology to improve the radar sensing performance. In the multi-band radar sensing signal model, the associated likelihood function has oscillation phenomenon, which makes it difficult to obtain highaccuracy parameter estimation. To cope with this challenge, we divide the radar target parameter estimation into two stages of coarse estimation and refined estimation, where the coarse estimation is used to narrow down the search range for the refined estimation, and the refined estimation is based on the Bayesian approach to avoid the convergence to a bad local optimum of the likelihood function. Specifically, in the coarse estimation stage, we employ a root MUSIC algorithm to achieve initial estimation. Then, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm for the Bayesian estimation of the radar target parameters in the refined stage. Unlike the conventional particle-based VBI (PVBI) in which only the probability of each particle is optimized and the per-iteration computational complexity increases exponentially with the number of particles, the proposed SPVBI optimizes both the position and probability of each particle, and it adopts the block SSCA to significantly improve the sampling efficiency by averaging over iterations. As such, it is shown that the proposed SPVBI can achieve a better performance than the conventional PVBI with a much smaller number of particles and per-iteration complexity. Finally, extensive simulations verify the advantage of the proposed algorithm over various baseline algorithms.

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Section: Introductionmentioning

confidence: 99%

Stochastic Particle-Based Variational Bayesian Inference for Multi-band Radar Sensing

Hu¹,

Liu²,

Wan³

et al. 2022

Preprint

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“…The unitary estimation of signal parameter via rotational invariance techniques (U‐ESPRIT) method is proposed to further improve the performance of signal pole estimation in Ref. [11]. This method is feasible when all poles are estimated accurately and matched correctly.…”

Section: Introductionmentioning

confidence: 99%

Coherent compensation and high‐resolution technology of multi‐band inverse synthetic aperture radar fusion imaging

Zhu

Liu

Guo

et al. 2021

IET Radar Sonar & Navi

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Multi-band inverse synthetic aperture radar (ISAR) fusion imaging technology can effectively improve the range resolution without incurring high hardware cost. The coherent phase between sub-bands is a prerequisite to achieve multi-band ISAR fusion imaging. Here, a joint approach of coherent compensation and high-resolution imaging is proposed to compensate the incoherent phase and obtain high-resolution ISAR fusion images. First, an incoherent phase estimation model based on sparse representation is established, and the phase estimation accuracy is improved by a modified coherent dictionary in case of off-grid. Then, a multi-band ISAR fusion imaging model based on sparse representation is established. The complex Gaussian scale mixture priors and the complex Gaussian priors are imposed on the scatterers and noise, respectively. The solution is derived in the complex domain based on the variational Bayesian expectation maximization framework. The proposed method can not only achieve better incoherent phase compensation in the case of off-grid, but also obtain high-quality ISAR fusion images under low signal-to-noise ratio and low bandwidth sampling ratio. Experimental results verify the effectiveness and robustness of the proposed method based on both numerical simulations and real data. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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