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Two-dimensional zero-attraction projection (2DZAP) algorithm for single snapshot direction of arrival (DOA) estimation is proposed in this paper. Compared with the traditional DOA estimation, the proposed 2D-ZAP algorithm can estimate DOA exactly by ℓ-norm with the same number of sensors, although each sensor samples the signal only one time. In addition, 2D-ZAP algorithm can reduce the noise interference...
One-dimensional (1D) compressive sensing is often adopted in traditional sparse target detection of pulse Doppler radar. However, the 1D sparse target detection problem often requires large computational memory due to large measurement matrix. To solve this problem, this paper proposes a two-dimensional iterative hard thresholding algorithm (2D-IHT) for CS-based pulse Doppler radar to directly detect...
Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions...
Adaptive sparse system identification (ASIDE) techniques have been successfully applied in many applications, such as sparse channel estimation and radar target detection. Normalized least mean fourth (NLMF)-type algorithms are considered as one of the stable ASIDE techniques even at low signal-to-noise ratio (SNR). However, the convergence capability of sparse NLMF algorithms is severely decreased...
Underdetermined inverse sparse signal reconstruction problems in the presence of non-Gaussian noise interference are often encountered in high-mobility wireless communications and signal processing. These problems can be solved by finding the minimizer of a suitable objective function which consists of a data-fitting term and a regularization term with different mixed-norms. Based on the Gaussian-noise...
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing (CS) in many applications such as Radar imaging and sparse channel estimation. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted...
To estimate multiple-input multiple-output (MIMO) channels, invariable step-size normalized least mean square (ISSNLMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model due to broadband signal transmission, such sparsity can be exploited by adaptive sparse channel estimation (ASCE) methods using sparse ISS-NLMS algorithms...
Cluster-sparse channels often exist in frequency-selective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is caused by a few reflectors near the receiver. Conventional sparse channel estimation methods have been proposed for general sparse channel model which without considering the potential cluster-sparse structure information...
Accurate channel estimation is essential for broadband wireless communications. Adaptive sparse channel estimation schemes based on normalized least mean square (NLMS) have been proposed to exploit channel sparsity for improved performance. However, their performance bound as derived in this paper indicates that the invariable step size (ISS) usually used for iteration in these schemes would lead...
Accurate channel estimation problem is one of the key technical issues in broadband wireless communications. Standard normalized least mean fourth (NLMF) algorithm was applied to adaptive channel estimation (ACE). Since the channel is often described by sparse channel model, such sparsity could be exploited and then estimation performance could be improved by adaptive sparse channel estimation (ASCE)...
Accurate channel state information (CSI) is required for coherent detection in time-variant multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) modulation. One of low-complexity and stable adaptive channel estimation (ACE) approaches is the normalized least mean square (NLMS)-based ACE. However, it cannot exploit the inherent sparsity...
Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) and Lp - norm sparse LMS (LP-LMS), have also been proposed. To take full advantage...
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