Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar

Wang, Zetao; Xie, Wu; Duan, Keqing; Wang, Yongliang

doi:10.1016/j.sigpro.2016.06.023

Cited by 65 publications

(69 citation statements)

References 28 publications

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“…where • p denotes the l p -norm of a vector, ε represents the noise error tolerance. Since the optimization problem of (13) is NP-hard, numerous SR algorithms have been applied to solve this problem, such as focal underdetermined system solution (FOCUSS) algorithm [28], [49], orthogonal matching pursuit (OMP) algorithm [33], [50], iterative adaptive approach (IAA) [51], [52] and SBL algorithm [34], [53]- [55]. Therefore, the CNCM can be calculated by using the estimated angle-Doppler profileγ , i.e.,…”

Section: Sr-stap Formulationmentioning

confidence: 99%

“…As aforementioned, the SBL algorithm can achieve improved performance without parameter adjustment, but the convergence procedure of SBL is very slow and the computational burden is quite heavy. In [34], the FCSBL approach is derived to improve the convergence of SBL. It is validated that FCSBL can obtain the steady-state performance with 20 iterations, which is much smaller than SBL whose required number of iterations is more than 400.…”

Section: Adaptive Fcsbl and Adaptive Mfcsblmentioning

confidence: 99%

“…According to [34], the initial derivation of FCSBL is to cast the signal model (20) into the following formulation, that is,…”

Section: ) Review Of the Fcsblmentioning

confidence: 99%

“…In general, according to the suppression way of clutter, these SR-STAP methods can be divided into two categories: whitened SR-STAP and nulled SR-STAP. In the whitened SR-STAP, the clutter angle-Doppler profile is first recovered by utilizing the SR algorithm, and then the CCM is calculated with the STAP over-complete dictionary and the obtained clutter angle-Doppler profile [27]- [34]. In [28], a D3 SR-STAP approach is proposed to estimate the clutter high-resolution space-time spectrum by only using the test cell.…”

Section: Introductionmentioning

confidence: 99%

“…In [33], the clutter spectrum is achieved by designing the RD STAP over-complete dictionary. In [34], the fast converging SBL (FCSBL) algorithm in single measurement vector (SMV) and the MMV cases MFCSBL algorithm are proposed, which uses a much smaller number of iterations to achieve the comparable clutter suppression performance. In nulled SR-STAP, the clutter subspace is first obtained by using SR algorithms, and then the STAP weight vector is calculated by projecting the data of CUT into the subspace orthogonal to the clutter subspace [35].…”

Section: Introductionmentioning

confidence: 99%

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Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

Guo

Zhang

et al. 2019

IEEE Access

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Sparse-recovery-based space-time adaptive processing (STAP) methods can exhibit superior clutter suppression performance with limited training data. However, the clutter suppression performance seriously degrades when the mutual coupling is present in the STAP array elements. In this paper, a sparse Bayesian learning (SBL)-based STAP method against the mutual coupling by using the middle subarray is proposed. Specifically, the mutual coupling matrix (MCM) of the STAP uniform linear array is approximately described as a banded symmetric Toeplitz matrix since the effect of mutual coupling between two array elements is inversely related to their distance and can be negligible when their distance is larger than a few wavelengths. Utilizing this specific structure of the MCM, a mutual coupling mitigation method is developed for STAP by rearranging the received snapshots with the designed spatial-temporal selection matrix. Then, to efficiently recover the clutter angle-Doppler profile, the modified fast converging SBL (FCSBL) named adaptive FCSBL algorithm and its multiple measurement vector case adaptive MFCSBL algorithm are developed with only partial hyper-parameters being updated in a single iteration. The proposed method not only achieves the superior clutter suppression performance in the presence of unknown mutual coupling, but also has low computational load. The simulation results are implemented to validate the effectiveness of the proposed methods. INDEX TERMS Space-time adaptive processing, mutual coupling matrix, spatial-temporal selection matrix, sparse Bayesian learning.

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Section: Sr-stap Formulationmentioning

confidence: 99%

Section: Adaptive Fcsbl and Adaptive Mfcsblmentioning

confidence: 99%

“…According to [34], the initial derivation of FCSBL is to cast the signal model (20) into the following formulation, that is,…”

Section: ) Review Of the Fcsblmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

Guo

Zhang

et al. 2019

IEEE Access

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Recent Advances in Self‐Powered Tribo‐/Piezoelectric Energy Harvesters: All‐In‐One Package for Future Smart Technologies

Karan

Maiti

Lee

et al. 2020

Adv Funct Materials

147

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Electric devices has become a necessity in modern smart societies. The energy research community is continually exploring new solutions that can be employed as potential and reliable alternatives to satisfy future energy requirements in this context. Triboelectric/piezoelectric nanogenerators (TNGs/PNGs) are considered promising renewable energy harvesting technologies. TNGs/PNGs are gaining considerable research attention because of their advantages such as ease of fabrication, cost effectiveness, flexibility, lightweightness, long-term-durability, and excellent output performance. The TNGs/PNGs have enormous potential in harvesting energy from water waves (blue energy), airflow (wind energy), sound frequency (acoustic energy), vibrations/mechanical motions (such as body motions activities/sleeping), and in vivo body motions. These technologies are thus an "all-in-one package," which is a unique selling point. The advantage of this technology is that all sources are natural and abundant. This paper provides a review of the potential use of PNGs/TNGs and includes a description of the rise of nanogenerators, their underlying mechanisms, different materials used, various models, possible applications, and future prospects. Stored energy can be used to develop smart/informative technologies (as ultrasensitive sensors) and self-powered biomedical science. Owing to their considerable effects, these harvesting technologies are expected to dominate the future smart world.

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Localisation of Persons by Means of Impulse-Radar Sensors: Advanced Methods

Wagner

Mazurek

Morawski

2022

Health Information Science

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Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar

Cited by 65 publications

References 28 publications

Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

Recent Advances in Self‐Powered Tribo‐/Piezoelectric Energy Harvesters: All‐In‐One Package for Future Smart Technologies

Localisation of Persons by Means of Impulse-Radar Sensors: Advanced Methods

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