An RCB-Like Steering Vector Estimation Method Based on Interference Matrix Reduction

Zhang, Pan; Yang, Zhiwei; Liao, Guisheng; Jing, Gang; Ma, Teng

doi:10.1109/taes.2020.3028484

Cited by 17 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following analysis is performed on (41). If the desired signals or jamming are incident on the array at different ranges and angles, the SVs corresponding to any two signals are approximately orthogonal or completely orthogonal.…”

Section: R E Y(t)y H (T) = Armentioning

confidence: 99%

“…As the accuracy of the Capon spatial spectrum in [31] deteriorates dramatically when coherent signals exist, a low complexity RAB based on the maximum entropy power spectrum is introduced in [40], but it ignores the jamming SV distortion when dealing with arbitrary mismatches. In [41], a novel desired signal SV optimization approach is proposed by designing a desired signal power estimator with uncertainty set constraints, which is more robust to look observation error and array geometry error. The above-mentioned RAB involves three strategies in reconstructing the IPNCM.…”

Section: Introductionmentioning

confidence: 99%

“…The above-mentioned RAB involves three strategies in reconstructing the IPNCM. The first is to remove the desired signal from the SCM [37,41]. The second approach is to reconstruct the IPNCM by integrating the Capon power spectrum over an angular domain containing the jamming [31,33,36].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Beamforming Based on Covariance Matrix Reconstruction in FDA-MIMO Radar to Suppress Deceptive Jamming

Wan

Zhang

2022

Sensors

View full text Add to dashboard Cite

Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit–receive frequency domain by utilizing additional degrees of freedom (DOFs) in the range dimension. However, when there are target SV mismatch, covariance matrix estimation error and target contamination, the jamming suppression performance degrades severely. In this paper, a robust adaptive beamforming algorithm for anti-jammer application based on covariance matrix reconstruction is proposed in FDA-MIMO radar. In this method, the residual noise is further determined by using the spatial power spectrum estimation approach, which results in improved estimation accuracy of the signal covariance matrix and the desired target SV. The jamming SV is obtained from vectors in the intersection of two subspaces (namely, the signal-jamming subspace derived from the sample covariance matrix (SCM) and the jamming subspace generated from the jamming covariance matrix) by an alternating projection algorithm. Furthermore, the jamming power is obtained by exploiting the orthogonality between the different SVs. With the obtained parameters of target and jamming, the optimal adaptive beamformer weight vector is calculated. Simulation results demonstrate that the proposed algorithm can cope with the mainbeam deceptive jamming suppression under various model mismatches and has excellent performance over a wide range of signal-to-noise ratios (SNRs).

show abstract

Section: R E Y(t)y H (T) = Armentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Beamforming Based on Covariance Matrix Reconstruction in FDA-MIMO Radar to Suppress Deceptive Jamming

Wan

Zhang

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Since traditional adaptive beamforming approaches have weak immunity against even a small mismatch between the presumed and actual signal steering vectors, many robust adaptive beamforming (RAB) techniques have been developed and substantial progress has been made in this area in recent years (see e.g. [2]- [7] and references therein). The mismatch may result from look direction errors, imperfect array calibration, distorted antenna shape, and other reasons [2].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets

Huang¹,

Hao²,

Vorobyov³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interferenceplus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer.

show abstract

“…The beamformer in [48] utilizes subspace orthogonality to reconstruct the NPICM. In [49], a design of SOI power estimator to formulate the SV optimization problem with an uncertainty set constraint is proposed which is different from the conventional Capon estimator in which the desired signal SV is optimized with the Capon power estimator.…”

mentioning

confidence: 99%

Study of Robust Adaptive Beamforming Based on Low-Complexity DFT Spatial Sampling

Mohammadzadeh,

Nascimento,

de Lamare

et al. 2021

Preprint

View full text Add to dashboard Cite

In this paper, a novel and robust algorithm is proposed for adaptive beamforming based on the idea of reconstructing the autocorrelation sequence (ACS) of a random process from a set of measured data. This is obtained from the first column and the first row of the sample covariance matrix (SCM) after averaging along its diagonals. Then, the power spectrum of the correlation sequence is estimated using the discrete Fourier transform (DFT). The DFT coefficients corresponding to the angles within the noise-plus-interference region are used to reconstruct the noise-plus-interference covariance matrix (NPICM), while the desired signal covariance matrix (DSCM) is estimated by identifying and removing the noise-plusinterference component from the SCM. In particular, the spatial power spectrum of the estimated received signal is utilized to compute the correlation sequence corresponding to the noiseplus-interference in which the dominant DFT coefficient of the noise-plus-interference is captured. A key advantage of the proposed adaptive beamforming is that only little prior information is required. Specifically, an imprecise knowledge of the array geometry and of the angular sectors in which the interferences are located is needed. Simulation results demonstrate that compared with previous reconstruction-based beamformers, the proposed approach can achieve better overall performance in the case of multiple mismatches over a very large range of input signal-tonoise ratios.

show abstract

An RCB-Like Steering Vector Estimation Method Based on Interference Matrix Reduction

Cited by 17 publications

References 38 publications

Robust Beamforming Based on Covariance Matrix Reconstruction in FDA-MIMO Radar to Suppress Deceptive Jamming

Robust Beamforming Based on Covariance Matrix Reconstruction in FDA-MIMO Radar to Suppress Deceptive Jamming

Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets

Study of Robust Adaptive Beamforming Based on Low-Complexity DFT Spatial Sampling

Contact Info

Product

Resources

About