A Novel Multiband Fusion Method Based on a Modified RELAX Algorithm for High-Resolution and Anti-Non-Gaussian Colored Clutter Microwave Imaging

Jiang, Wen; Liu, Jianwei; Yang, Jiyao; Zhang, Xiangpeng; Li, Wangzhe

doi:10.1109/tgrs.2021.3109724

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…However, whether high resolution can be achieved will depend on the accuracy of the data reconstruction. Therefore, for the SPVBI algorithm and Baseline 3 and 4, we compare the root-mean-square error (RMSE) of data reconstruction to indirectly show the resolution performance 2 .…”

Section: Resolution Performancementioning

confidence: 99%

“…The existing multi-band radar sensing methods can be divided into three categories [2], i.e., spectral estimation method, sparse signal reconstruction method, and probabilistic inference method.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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: Resolution Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%