BSBL‐based multiband fusion ISAR imaging

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

doi:10.1049/joe.2019.0369

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…The signal model is consistent with the model in Ref. [19]. Due to the different initial system phases of the two radars and the different time delays from the target to the radars, the echoes of sub-bands are usually incoherent.…”

Section: Incoherent Phase Between Sub-bandssupporting

confidence: 80%

“…c f 0 y p cos Δθ m Þ to correct the cross-range MTRC [21]. After correcting the MTRC, Equation (7) can be expressed as…”

Section: Fusion Imaging Model Based On Sparse Representationmentioning

confidence: 99%

“…The effect of the first exponential term on the slant‐range MTRC can be negligible, however, the second exponential term may result in slant‐range MTRC for a large target or a long observation time [21]. After translation compensation, the target is regarded as a turntable model rotating at an angular rotation velocity ω , and Δ θ m = ωt m .…”

Section: Multi‐band Isar Fusion High‐resolution Imaging Based On Cgsmmentioning

confidence: 99%

“…Although the effect of cosΔ θ m on the envelope of the range profiles obtained by pulse compression is negligible, the effect on the phase of the scatterers in the range profiles cannot be ignored. The phase compensation term should be constructed as

ϕ_{c o m} = \exp false(j \frac{4 π}{c} f_{0} y_{p} \cos Δ θ_{m} false)

to correct the cross‐range MTRC [21]. After correcting the MTRC, Equation (7) can be expressed as

\begin{matrix} s_{i} (n_{i}, m) = \sum_{p = 1}^{P} a_{p} \cdot \exp (- j \frac{4 π}{c} n_{i} Δ f y_{p}) \\ \cdot \exp (- j \frac{4 π}{c} f_{0} x_{p} ω {t^{'}}_{m}) (i = 1,2) \end{matrix}

…”

Section: Multi‐band Isar Fusion High‐resolution Imaging Based On Cgsmmentioning

confidence: 99%

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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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Section: Incoherent Phase Between Sub-bandssupporting

confidence: 80%

“…c f 0 y p cos Δθ m Þ to correct the cross-range MTRC [21]. After correcting the MTRC, Equation (7) can be expressed as…”

Section: Fusion Imaging Model Based On Sparse Representationmentioning

confidence: 99%

Section: Multi‐band Isar Fusion High‐resolution Imaging Based On Cgsmmentioning

confidence: 99%

ϕ_{c o m} = \exp false(j \frac{4 π}{c} f_{0} y_{p} \cos Δ θ_{m} false)

to correct the cross‐range MTRC [21]. After correcting the MTRC, Equation (7) can be expressed as

\begin{matrix} s_{i} (n_{i}, m) = \sum_{p = 1}^{P} a_{p} \cdot \exp (- j \frac{4 π}{c} n_{i} Δ f y_{p}) \\ \cdot \exp (- j \frac{4 π}{c} f_{0} x_{p} ω {t^{'}}_{m}) (i = 1,2) \end{matrix}

…”

Section: Multi‐band Isar Fusion High‐resolution Imaging Based On Cgsmmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…For example, we can obtain the maximum a posterior (MAP) estimate by methods like expectation-maximum (EM) [22] , support vector regression (SVR) [23] and conventional particle-based VBI [24]. Besides, sparse Bayesian learning (SBL) algorithm can also be used to solve the problem under certain sparse prior assumptions [25], and several algorithms have been proposed to improve the SBL, e.g., [26]- [28]. These algorithms usually require additional assumptions about priors, and it is not easy to achieve a good trade-off between estimation accuracy and computational complexity.…”

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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Broadband multi‐carrier linear frequency modulation signal reception with subcarrier frequency offset deramp processing

Li,

Zhang,

Kong

et al. 2024

IET Radar Sonar & Navi

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In this paper, a broadband multi‐carrier linear frequency modulation (LFM) signal reception method with subcarrier frequency offset deramp processing is proposed and investigated. The proposed frequency offset deramp processing is implemented by mixing the multi‐carrier LFM radar echo with a multi‐carrier LFM reference that has a different subcarrier frequency interval. With this design, the sampling rate of the radar receiver is remarkably reduced and crosstalk‐free separation of different subcarrier signals is easily conducted in the frequency domain. To fuse the multiple subcarriers and fill the frequency gaps, a sparse reconstruction method is employed to obtain the broadband response, which is essential for achieving high range resolution detection. The effectiveness of the proposed method is validated through an experiment in which the reception of an 8‐carrier LFM signal is conducted and a total bandwidth of 6 GHz after multi‐carrier fusion is demonstrated. An inverse synthetic aperture radar imaging experiment is also conducted with the results verifying the good potential of the proposed method in practical applications.

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BSBL‐based multiband fusion ISAR imaging

Cited by 10 publications

References 9 publications

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

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

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

Broadband multi‐carrier linear frequency modulation signal reception with subcarrier frequency offset deramp processing

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