Mashud Hyder

Direction-of-Arrival Estimation Using a Mixed

A set of vectors is called jointly sparse when its elements share a common sparsity pattern. We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as the problem of recovering a joint-sparse representation. We consider both narrowband and broadband scenarios. We propose to minimize a mixed ℓ2,0 norm approximation to deal with the joint-sparse recovery problem. Our algorithm can resolve closely spaced and highly correlated sources using a small number of noisy snapshots. Furthermore, the number of sources need not be known a priori. In addition, our algorithm can handle more sources than other state-of-the-art algorithms. For the broadband DOA estimation problem, our algorithm allows relaxing the half-wavelength spacing restriction, which leads to a significant improvement in the resolution limit.

\ell _{2,0}

We address the problem of finding a set of sparse signals that have nonzero coefficients in the same locations from a set of their compressed measurements. A mixed lscr2,0 norm optimization approach is considered. A cost function appropriate to the joint-sparse problem is developed, and an algorithm is derived. Compared to other convex relaxation based techniques, the results obtained by the proposed method show a clear improvement in both noiseless and noisy environments.

Norm Approximation

ℓ⁰ norm based signal recovery is attractive in compressed sensing as it can facilitate exact recovery of sparse signal with very high probability. Unfortunately, direct ℓ⁰ norm minimization problem is NP-hard. This paper describes an approximate ℓ⁰ norm algorithm for sparse representation which preserves most of the advantages of ℓ⁰ norm. The algorithm shows attractive convergence properties, and provides remarkable performance improvement in noisy environment compared to other popular algorithms. The sparse representation algorithm presented is capable of very fast signal recovery, thereby reducing retrieval latency when handling

A Robust Algorithm for Joint-Sparse Recovery

We pose the range-Doppler imaging problem as a two-dimensional sparse signal recovery problem with an overcomplete basis. The resulting optimization problem can be solved using both ℓ0 and ℓ1 norm minimization algorithms. Algorithm performance and estimation quality are illustrated using artificial data set, where targets are close to each other and target SNR is low. We show that accurate target location is achieved with high resolution. In particular, compared to other state-of-art algorithms, the proposed approach is shown to achieve more robustness in noisy environment with limited data.

An approximate L0 norm minimization algorithm for compressed sensing

The problem of detection of multiple targets using a bistatic MIMO radar system is posed as a joint sparse signal recovery problem. We explore the potential of MIMO systems to locate targets in noisy environment with limited number of time samples, while the transmitted signals are not necessarily mutually orthogonal. Explicit enforcement of a joint sparse representation is motivated by a desire to obtain a sharp estimate of the targets that exhibits robustness in noisy environment. Numerical experiments demonstrate that the proposed strategy outperforms existing algorithms.

Range-Doppler imaging via sparse representation

We consider the problem of estimating the line spectrum of a signal from finitely many time domain samples. We present a gridless algorithm for solving the total variation minimization approach associated with this problem. Unlike the related previous results, our method does not require the sampling instants to lie on an uniform grid. The resulting algorithm is a semidefinite program, structurally similar to some of the existing methods. One key observation made in our analysis also allows us to develop a gridless version of the SPICE algorithm. The simulation results demonstrate the superiority of these in performance compared to other related methods.

A joint sparse signal representation perspective for target detection using bistatic MIMO radar system

This paper presents a new approach, known as symmetry axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the axis of symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.

Frequency Estimation From Arbitrary Time Samples

Initial uplink synchronization is an integral part of wireless communication systems. It enables the base station (BS) to detect new subscriber stations (SS) willing to commence communication. It also enables the BS to estimate the uplink channel parameters of these SSs. Accurate estimation of channel parameters is crucial, as they ensure the uplink signals from all the SSs arrive at the BS synchronously at similar power levels. However, this detection and estimation problem turns out to be very challenging when multiple users initiate the synchronization procedure at the same time. We address this issue by exploiting the underlying sparsity of the estimation problem. We propose a fast sparse signal recovery approach that shows a clear improvement in detection and estimation performance compared to other state-of-the-art methods. The proposed method can be integrated into any OFDM based standard.

SYMMETRY AXIS BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING

Abstract We present a grid-less version of the L1-SVD algorithm for direction of arrival estimation. The resulting semidefinite programming approach is a globally convergent, fully parametric method capable of working with two dimensional arrays with any arbitrary sensor configurations. It is computationally efficient, and shows improved performance when compared with other popular alternatives. The analysis also allows us to formulate the SPICE algorithm in gridless manner.

A Sparse Recovery Method for Initial Uplink Synchronization in OFDMA Systems

A Zadoff–Chu (ZC) sequence is uncorrelated with a non-zero cyclically shifted version of itself. However, this alone is insufficient to mitigate inter-code interference in LTE initial uplink synchronization. The performance of the state-of-the-art algorithms vary widely depending on the specific ZC sequences employed. We develop a systematic procedure to choose the ZC sequences that yield the optimum performance. It turns out that the procedure for ZC code selection in LTE standard is suboptimal when the carrier frequency offset is not small.