A basic problem in the space-based automatic identification system (AIS) is the low probability of detecting messages because ships' messages arrive at the receiver in the same time slot. In this study, sparse lin...
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A basic problem in the space-based automatic identification system (AIS) is the low probability of detecting messages because ships' messages arrive at the receiver in the same time slot. In this study, sparse linear array optimal beam synthesis (SLA-OBS) technology is proposed to improve the capture ability of AIS messages by forming a narrow beam pattern that points in the direction of the desired AIS messages. To capture the desired signal within the narrow beam pattern, the directions of arrival (DOA) and the number of sources from ships are first estimated. Then, the ideal narrow beam pattern and minimal number of array elements are achieved synchronously with the CPLEX optimal tool. The simulations show that the message detection probability with the proposed method is greater than 95%, even when the situations are very serious, whereas the number of sparselinear antennas is small (no more than six). Copyright (C) 2015 John Wiley & Sons, Ltd.
We propose an imaging algorithm for downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) in the circumstance of cross-track sparse and nonuniform array configuration. Conside...
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We propose an imaging algorithm for downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) in the circumstance of cross-track sparse and nonuniform array configuration. Considering the off-grid effect and the resolution improvement, the algorithm combines pseudo-polar formatting algorithm, reweighed atomic norm minimization (RANM), and a parametric relaxation-based cyclic approach (RELAX) to improve the imaging performance with a reduced number of array antennas. RANM is employed in the cross-track imaging after pseudo-polar formatting the DLSLA 3-D SAR echo signal, then the reconstructed results are refined by RELAX. By taking advantage of the reweighted scheme, RANM can improve the resolution of the atomic norm minimization, and outperforms discretized compressive sensing schemes that suffer from off-grid effect. The simulated and real data experiments of DLSLA 3-D SAR verify the performance of the proposed algorithm. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
In this letter, a dual formulation of atomic norm minimization (ANM) approach is proposed by exploiting the vectorized covariance data of the signals received by the extended virtual array. Compared with the tradition...
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In this letter, a dual formulation of atomic norm minimization (ANM) approach is proposed by exploiting the vectorized covariance data of the signals received by the extended virtual array. Compared with the traditional ANM-based gridless direction of arrival (DOA) estimation algorithms for sparse linear array (SLA), the proposed algorithm not only realizes the parameter continuity and data completion, but also reduces the size of optimization model and the influence of noise on the estimation accuracy, which can significantly improve the performance of the algorithm. In addition, after establishing an ANM model based on complete vectorized covariance data, the corresponding dual problem is formulated to make the subsequent DOA recovery process no longer need the number of sources as a priori information. Numerial simulations demonstrate the superiority of the proposed algorithm over state of the art gridless DOA estimation algorithms.
We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the pea...
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ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066315
We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the peak sidelobe level of the virtual SLA beampattern is low. Prior approaches have focused on optimal sparsearray design, or use of interpolation techniques for filling the holes in the synthesized SLA before applying digital beamforming for angle finding. In this paper, different from previous efforts, we use matrix completion to complete the corresponding virtual uniform lineararray (ULA) before estimating the target angle. In particular, we show that for a small number of targets within the same range-Doppler cell, the Hankel matrix constructed by subarrays of the virtual ULA is low-rank, and thus under certain conditions, can be completed based on the SLA measurements. We derive the coherence properties of the Hankel matrix so that the matrix can be competed via nuclear norm minimization methods. We also demonstrate via examples the effect of various SLA topologies on the identifiability of the Hankel matrix.
This paper presents a hybrid method for the synthesis of sparse but symmetric lineararray. Multiple constraints are taken into account for multiple objectives such as low sidelobe level (SLL), and nulling in specific...
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ISBN:
(纸本)9781538616086
This paper presents a hybrid method for the synthesis of sparse but symmetric lineararray. Multiple constraints are taken into account for multiple objectives such as low sidelobe level (SLL), and nulling in specific directions. Two examples are presented and compared to those obtained by CLPSO, IWO/WDO and MCFO algorithms. Based on the comparisons, the proposed method gives a better suppression onto the sidelobe and interference.
Downward Looking sparse linear array Three Dimensional SAR(DLSLA 3D SAR) is an important form of 3D SAR imaging, which has a widespread application field. Since its practical equivalent phase centers are usually distr...
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Downward Looking sparse linear array Three Dimensional SAR(DLSLA 3D SAR) is an important form of 3D SAR imaging, which has a widespread application field. Since its practical equivalent phase centers are usually distributed sparsely and nonuniformly, traditional 3D SAR algorithms suffer from low resolution and high sidelobes in cross-track dimension. To deal with this problem, this paper introduces a method based on back-projection and convex optimization to achieve 3D high accuracy imaging reconstruction. Compared with traditional SAR algorithms, the proposed method sufficiently utilizes the sparsity of the 3D SAR imaging scene and can achieve lower sidelobes and higher resolution in cross-track dimension. In the simulated experiments, the reconstructed results of both simple and complex imaging scene verify that the proposed method outperforms 3D back-projection algorithm and shows satisfying cross-track dimensional resolution and good robustness to noise.
We present the design of steerable sparse linear array (SLA) for 2D forward looking sonar imaging system in this paper. The mathematical model includes constraints for multiple steering directions in the Field Of View...
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ISBN:
(纸本)9781538641880
We present the design of steerable sparse linear array (SLA) for 2D forward looking sonar imaging system in this paper. The mathematical model includes constraints for multiple steering directions in the Field Of View (FOV) and this model is based on the Compressive Sensing (CS) theory. The sparsity in this model is introduced by considering oversampled linear aperture (i.e. densely populated aperture by sensors). This model defines the Main Lobe Width (MLW) constraints indirectly by matching the beam pattern of the synthesized SLA with the desired beam pattern in the main lobe region for all steering direction. The Side Lobe Peak (SLP) constraints in this model are defined directly for all steering directions. This optimization problem is a disciplined convex program and its equivalent formulation is a second order cone programming problem. The SLA synthesized using this method has separate weights for each steering directions. The SLA synthesized using this method is compared with the SLA designed using Multiple beam, Measurement Vectors (MMV) CS method. The performance of the synthesized SLAs is compared in terms of reduction of sensors with half wavelength spacing Uniform lineararray (ULA) and MLW % error. We present the design results of forward looking sonar imaging system for frequencies 37.5 kHz and 75 kHz having angular resolution 6.4 degrees and 3.2 degrees respectively, with 11 and 19 beams in FOV.
This work proposes a new fitness function for sparse linear array design using the Point Spread Function (PSF), where the energy and entropy are combined. The Arithmetic Optimization Algorithm is used as a search mech...
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ISBN:
(纸本)9781665443593
This work proposes a new fitness function for sparse linear array design using the Point Spread Function (PSF), where the energy and entropy are combined. The Arithmetic Optimization Algorithm is used as a search mechanism, and a new strategy to find sparselinear configurations is proposed. This new strategy eliminates the necessity of penalization functions to control the number of elements in sparsearrays. The algorithm is used to find two sparse linear arrays, one using a fitness function based on the radiation pattern, and the other with the proposed fitness function. Comparing the results, the configuration found using the proposed fitness function presented better lateral resolution and contrast.
Recently one-bit quantization has been widely used in field of signal processing because of its advantages of low cost, low power consumption and high sampling rate. This paper studies the problem of estimating the di...
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sparse linear arrays (SLAs) provide a high number of degrees of freedom (DOF) and reduce the mutual coupling effect between sensors of the array. Many design methods for SLAs have been proposed in the last two decades...
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sparse linear arrays (SLAs) provide a high number of degrees of freedom (DOF) and reduce the mutual coupling effect between sensors of the array. Many design methods for SLAs have been proposed in the last two decades but most of the existing SLA design methods fail to achieve minimum number of sensors for a desired DOF. In this paper, a design method is proposed which utilizes the branch and bound (B & B) optimization algorithm to give a SLA with the desired DOF and minimum number of physical sensors. The proposed SLA design method is obtained by generalizing the minimum sensor array (MSA) design method which is recently proposed in the literature. This generalized method provides the same DOF as the MSA method but with a smaller number of physical sensors. In addition, a version of this design method is proposed which provides more robustness against mutual coupling effect between the sensors of the array. Simulation results demonstrate the superiority of the performance of the SLAs obtained with the proposed method over the arrays obtained with the MSA method and also over the other existing SLAs.
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