Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
Wideband sparse spectral estimation is generally formulated as a multi-dictionary/multi-measurement (MD/MM)\nproblem which can be solved by using group sparsity techniques. In this paper, the MD/MM problem is reformulated\nas a single sparse indicative vector (SIV) recovery problem at the cost of introducing an additional system error. Thus,\nthe number of unknowns is reduced greatly. We show that the system error can be neglected under certain\nconditions. We then present a new subband information fusion (SIF) method to estimate the SIV by jointly utilizing all\nthe frequency bins. With orthogonal matching pursuit (OMP) leveraging the binary property of SIV�s components, we\ndevelop a SIF-OMP algorithm to reconstruct the SIV. The numerical simulations demonstrate the performance of the\nproposed method....
A coprime array is capable of achieving more degrees-of-freedom for direction-of-arrival\n(DOA) estimation than a uniform linear array when utilizing the same number of sensors. However,\nexisting algorithms exploiting coprime array usually adopt predefined spatial sampling grids for\noptimization problem design or include spectrum peak search process for DOA estimation, resulting\nin the contradiction between estimation performance and computational complexity. To address this\nproblem, we introduce the Estimation of Signal Parameters via Rotational Invariance Techniques\n(ESPRIT) to the coprime coarray domain, and propose a novel coarray ESPRIT-based DOA estimation\nalgorithm to efficiently retrieve the off-grid DOAs. Specifically, the coprime coarray statistics are\nderived according to the received signals from a coprime array to ensure the degrees-of-freedom\n(DOF) superiority, where a pair of shift invariant uniform linear subarrays is extracted. The rotational\ninvariance of the signal subspaces corresponding to the underlying subarrays is then investigated\nbased on the coprime coarray covariance matrix, and the incorporation of ESPRIT in the coarray\ndomain makes it feasible to formulate the closed-form solution for DOA estimation. Theoretical\nanalyses and simulation results verify the efficiency and the effectiveness of the proposed DOA\nestimation algorithm....
The minimum power distortionless response beamformer has a good interference rejection capability, but the\ndesired signal will be suppressed if signal steering vector or data covariance matrix is not precise. The worst-case\nperformance optimization-based robust adaptive beamformer (WCB) has been developed to solve this problem.\nHowever, the solution of WCB cannot be expressed in a closed form, and its performance is affected by a prior\nparameter, which is the steering vector error norm bound of the desired signal. In this paper, we derive an\napproximate diagonal loading expression of WCB. This expression reveals a feedback loop relationship between\nsteering vector and weight vector. Then, a novel robust adaptive beamformer is developed based on the iterative\nimplementation of this feedback loop. Theoretical analysis indicates that as the iterative step increases, the\nperformance of the proposed beamformer gets better and the iteration converges. Furthermore, the proposed\nbeamformer does not subject to the steering vector error norm bound constraint. Simulation examples show that the\nproposed beamformer has better performance than some classical and similar beamformers....
This paper focuses on robust transceiver design for throughput enhancement on the interference channel (IC),\nunder imperfect channel state information (CSI). In this paper, two algorithms are proposed to improve the\nthroughput of the multi-input multi-output (MIMO) IC. Each transmitter and receiver has, respectively, M and N\nantennas and IC operates in a time division duplex mode. In the first proposed algorithm, each transceiver adjusts\nits filter to maximize the expected value of signal-to-interference-plus-noise ratio (SINR). On the other hand, the\nsecond algorithm tries to minimize the variances of the SINRs to hedge against the variability due to CSI error.\nTaylor expansion is exploited to approximate the effect of CSI imperfection on mean and variance. The proposed\nrobust algorithms utilize the reciprocity of wireless networks to optimize the estimated statistical properties in two\ndifferent working modes. Monte Carlo simulations are employed to investigate sum rate performance of the\nproposed algorithms and the advantage of incorporating variation minimization into the transceiver design....
Range cell migration has a serious impact on the precision of image formation, especially for high-resolution and large-scale\nimaging. To get the full resolution and high quality of the image, the range cell migration correction in the azimuth time domain\nmust be considered. For tackling this problem, this paper presents a novel and efficient range cell migration correction method\nbased on curve fitting and signal processing. By emulating a curve to approximate the range-compressed echo of a strong point,\nthe range location indexes of the strong point along the azimuth direction can be obtained under the least squares criterion.The\nmerits of the proposed method are twofold: (1) the proposed method is robust to the uncertainty of system parameters (strong\ntolerance) under real flights and (2) the generalization of the proposed method is better and can be easily adapted to different\nsynthetic aperture radar (SAR) modes (e.g., monostatic and bistatic). The experimental results on real remote sensing data from\nboth themonostatic and the bistatic SAR demonstrate the effectiveness.The regressed distance curve is completely coincident with\nthe trajectory of strong points of the echo. Finally, the imaging focus results also validate the efficiency of the proposed method....
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