Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

Zhang

EURASIP J. Adv. Signal Process.

2016

In this paper, we propose a robust direction-of-arrival (DOA) estimation algorithm based on group sparse reconstruction algorithm utilizing signals observed at multiple frequencies. The group sparse reconstruction scheme for DOA estimation is solved through the complex multitask Bayesian compressive sensing algorithm by exploiting the group sparse property of the received multi-frequency signals. Then, we propose a robust reconstruction algorithm in the presence of distorted signals. In particular, we consider a problem where the observed data in some frequencies are distorted due to, e.g., interference contamination. In this case, the residual error will follow the impulsive Gaussian mixture distribution instead of the Gaussian distribution due to the fact that some of the estimation errors significantly depart from the mean value of the estimation error distribution. Thus, the minimum least square restriction used in the conventional sparse reconstruction algorithm may lead to a failed reconstruction result. By exploiting the maximum correntropy criterion which is inherently insensitive to the impulsive noise, a weighting vector is derived to automatically mitigate the effect of the distorted narrowband signals, and a robust group compressive sensing approach is developed to achieve reliable DOA estimation. The robustness and effectiveness of the proposed algorithm are verified using simulation results.

Section: Cmt-bcs Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust group compressive sensing for DOA estimation with partially distorted observations

Zhang

EURASIP J. Adv. Signal Process.

2016

“…In addition, to apply CS algorithms, the sampling matrix must satisfy the restricted isometry property (RIP) for guaranteeing reliable estimators. Such a condition cannot be easily verified because it results computational demanding [10,11].…”

Section: Introductionmentioning

confidence: 99%

Efficient Bayesian compressed sensing-based channel estimation techniques for massive MIMO-OFDM systems

Al-Salihi

Nakhai

2017

J Wireless Com Network

Efficient and highly accurate channel state information (CSI) at the base station (BS) is essential to achieve the potential benefits of massive multiple input multiple output (MIMO) systems. However, the achievable accuracy that is attainable is limited in practice due to the problem of pilot contamination. It has recently been shown that compressed sensing (CS) techniques can address the pilot contamination problem. However, CS-based channel estimation requires prior knowledge of channel sparsity to achieve optimum performance, also the conventional CS techniques show poor recovery performance for low signal to noise ratio (SNR). To overcome these shortages, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems using Bayesian compressed sensing (BCS) based on prior knowledge of statistical information regarding channel sparsity. Furthermore, by utilizing the common sparsity feature inherent in the massive MIMO system channel, we extend the proposed Bayesian algorithm to a multi-task (MT) version, so the developed MT-BCS can obtain better performance results than the single task version. Several computer simulation based experiments are performed to confirm that the proposed methods can reconstruct the original channel coefficient more effectively when compared to the conventional channel estimator in terms of estimation accuracy.

“…The most successful one is L1-SVD [5], which exploits the L 1 norm to reconstruct sparse signals and applies singular value decomposition (SVD) to reduce computational complexity. In [6][7][8], the Bayesian compressive sensing (BCS) framework is successfully applied in DOA estimation. In [9], a algorithm called L1-SRACV is presented for DOA estimation, based on sparse representation of array covariance vectors.…”

Section: Introductionmentioning

confidence: 99%

Off-Grid Doa Estimation Based on Sparse Representation and Rife Algorithm

Xu¹,

Chen²,

Gao³

2017

PIER M

Abstract-In this paper, off-grid DOA estimation based on sparse representation and Rife algorithm is presented to improve performance when the sparse signal directions are not on the predefined angular grids. The algorithm is divided into two steps. Firstly, the real-valued sparse representation of array covariance vector (RV-SRACV) algorithm is used to do off-grid DOA estimation, and it does not need to estimate the noise power. Secondly, Rife algorithm is used to correct the DOA estimation, and after that the DOA can be accurately estimated. The effectiveness and superior performance of the proposed algorithm are demonstrated in the simulation results.