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1st Annual International Conference on Sensor Networks and Signal Processing (SNSP)

作者： Feng, Ziang Hu, Guoping Shi, Junpeng Zhou, Hao Air Force Engn Univ Air & Missile Def Coll Xian Shaanxi Peoples R China

ISBN: (纸本)9781538674130

Due to the superiority in target detection, antijamming and parameter estimation of targets, Multiple-input multiple-output (MIMO) radar has attracted wide attention. However, the good performance of direction finding is achieved on the assumption that all the elements of the antenna are in ideal circumstances. In practice, the elements will suffer from some uncertainties caused by electromagnetic environment such as mutual coupling and amplitude and phase error, which will lead to performance decreases or even failure. A novel antenna array geometry for nested MIMO radar, which both lowers the impact of mutual coupling and extends the degrees of freedom (DOF), is discussed. The signal model and structure of mutual coupling matrix are constructed by utilizing the geometry, selection matrices are constructed to maintain the Vandermonde characteristic of the manifold matrix of arrays, and the directions-of-arrival (DOAs) is estimated by utilizing the Spatial Smoothing MUSIC (Multiple Signal Classification) algorithm. Theoretical derivation indicates the superior performance in direction finding and mutual coupling calibration of the proposed method. Simulation results demonstrate the correctness.

关键词： Multiple-input multiple-output (MIMO) radar nested array mutual coupling spatial spectrum direction of arrival spatial smoothing

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SUBSPACE-BASED CO-array PROCESSING FOR nested arrayS WITHOUT EIGENDECOMPOSITION 49

SUBSPACE-BASED CO-ARRAY PROCESSING FOR NESTED ARRAYS WITHOUT...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qui, Xinghao Shang, Zhigang Qiao, Gang Qin, Jixing Li, Xuerui Harbin Engn Univ Coll Underwater Acoust Engn Harbin Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing Peoples R China Harbin Engn Univ HEU Natl Key Lab Underwater Acoust Technol Key Lab Marine Informat Acquisit & Secur HEU Minist Ind & Informat Technol Harbin 150001 Peoples R China Sanya Nanhai Innovat & Dev Base HEU Sanya 572024 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

For the purpose of computational efficiency, we propose two subspace-based methods, but without eigendecomposition, to address the two typical problems in nested array processing, i.e., direction-of-arrival (DOA) estimation and noise elimination. In detail, to estimate DOA parameters, we judiciously arrange the segments extracted from the co-array model and then introduce a novel co-array-based orthogonal propagator method (COPM). Next, we develop a projection-based noise cancellation approach in the co-array domain, improving the relatively poor performance of COPM at low signal-to-noise ratios. Simulations evaluate the proposed algorithms under both overdetermined and underdetermined conditions.

关键词： Co-array model direction-of-arrival estimation nested array orthogonal propagator method.

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DOA estimation in conformal arrays based on the nested array principles

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DIGITAL SIGNAL PROCESSING 2016年 50卷 191-202页

作者： Alinezhad, Pourya Seydnejad, Saeid R. Abbasi-Moghadam, Dariush Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

The nested array structure has attracted great attention recently due to its ability in reducing the number of sensors in an array and at the same time preserving the array performance. While a uniform linear array (ULA) can detect at most N - 1 sources with N sensors, a nested array can provide O(N-2) degrees of freedom with the same number of sensors;allowing us to detect K sources with K > N sensors. Direction of arrival (DOA) estimation in a conformal array is a challenging task. In this article, by breaking the conformal array into smaller sub-arrays and using an interpolation technique, we employ the nested array principles to detect more number of sources than sensors. This comes at the cost of more snapshots and lower resolution, in the DOA estimation of an arbitrarily-shaped conformal array. Each sub-array in the conformal array is selected such that the "shadow effect" which leads to an incomplete steering vector in the DOA estimation algorithm is eliminated. The selected sub-arrays are then transformed to virtual nested arrays where more degrees of freedom can be obtained by applying the MUSIC algorithm for DOA estimation. The application of our proposed method is highlighted by considering a set of comprehensive examples for cylindrical and spherical arrays. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Conformal array nested array DOA estimation MUSIC

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An efficient off-grid DOA estimation approach for nested array signal processing by using sparse Bayesian learning strategies

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SIGNAL PROCESSING 2016年 128卷 110-122页

作者： Yang, Jie Liao, Guisheng Li, Jun Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

In this paper, we address the problem of sparsity-inducing direction-of-arrival (DOA) estimation in the context of array covariance vectors when a recently proposed nested array structure is utilized. The proposed approach is based on a Sparse Bayesian learning (SBL) perspective while the off-grid effects inherent in traditional sparse recovery algorithms are taken into account simultaneously. Specifically, the notable advantages of the developed approach can be attributed to two aspects. First, through a linear transformation, the unknown noise variance can be eliminated effectively and the estimation error between the actual and sample covariance matrix can be normalized to an identity matrix, thereby facilitating a noise-free sparse representation. Second, a computationally tractable polynomial rooting procedure is introduced to calculate the discretized sampling grid error one by one based on the reconstructed spatial power spectrum, thus this novel algorithm can maintain high estimation accuracy even under a coarse sampling grid. Extensive simulation results finally demonstrate the superiority of the proposed approach in terms of DOA estimation precision and computational efficiency over off-the-shelves techniques. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Direction-of-arrival (DOA) estimation nested array Sparse Bayesian learning (SBL) Off-grid effects Polynomial rooting

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nested array for Antenna Deployment in Massive MIMO with Spectrum Efficiency 5

Nested Array for Antenna Deployment in Massive MIMO with Spe...

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5th IEEE International Conference on Mobile Services (MS)

作者： Liang, Qilian Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

ISBN: (纸本)9781509026258

The nested array was firstly introduced to perform array processing with increased degrees of freedom using much fewer physical sensors In this paper, we apply nested array to antenna deployment in massive MIMO, which can tremendously reduce the number of antennas, and achieve the same performance. Besides, this deployment for massive MIMO has also spectrum efficiency. As a case study, we demonstrate that real-world LTE/LTE-A mobile service data traffic is quite fluctuating, and adaptive bandwidth allocation should be performed to increase spectrum efficiency.

关键词： Massive MIMO LTE/LTE-A Mobile Service Data nested array

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DOA estimation with synthetic nested array using moving sensor

DOA estimation with synthetic nested array using moving sens...

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2017 2nd International Conference on Machinery,Electronics and Control Simulation(MECS 2017)

作者： Zhan Shi Xiaofei Zhang You Sun College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics State Key Laboratory of Millimeter Waves Southeast University National Mobile Communications Research Laboratory Southeast University

The synthetic aperture methods are widely used to extend array aperture by exploiting platform motion. In this paper, a synthetic nested array using moving sensor for DOA estimation is proposed. With the moving sensor, we can construct a longer nested array which is equivalent to the real array. Then traditional DOA estimation algorithms for nested array can also be applied, such as spatial smooth MUSIC algorithm. By extending the array aperture and exploiting synthetic nested array, the DOA estimation performance can be improved. In the ideal case, the performance of synthetic and real array is the same which is vilified by performance analysis and simulations.

关键词： Moving sensor nested array Synthetic array

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Direction of arrival estimation of non-Gaussian signals for nested arrays: Applying fourth-order difference co-array and the successive method

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ETRI JOURNAL 2021年第5期43卷 869-880页

作者： Ye, Changbo Chen, Weiyang Zhu, Beizuo Tang, Leiming Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Dynam Cognit Syst Electromagnet Spectrum Coll Elect & Informat Engn Nanjing Peoples R China

Herein, we estimate the direction of arrival (DOA) of non-Gaussian signals for nested arrays (NAs) by implementing the fourth-order difference co-array (FODC) and successive methods. In particular, considering the property of the fourth-order cumulant (FOC), we first construct the FODC of the NA, which can obtain O(N-4) virtual elements using N physical sensors, whereas conventional FOC methods can only obtain O(N-2) virtual elements. In addition, the closed-form expression of FODC is presented to verify the enhanced degrees of freedom (DOFs). Subsequently, we exploit the vectorized FOC (VFOC) matrix to match the FODC of the NA. Notably, the VFOC matrix is a single snapshot vector, and the initial DOA estimates can be obtained via the discrete Fourier transform method under the underdetermined correlation matrix condition, which utilizes the complete DOFs of the FODC. Finally, fine estimates are obtained through the spatial smoothing-Capon method with partial spectrum searching. Numerical simulation verifies the effectiveness and superiority of the proposed method.

关键词： Direction of arrival discrete Fourier transform fourth-order difference co-array nested array non-Gaussian signals

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Real-Valued Khatri-Rao Subspace Approaches on the ULA and a New nested array

Real-Valued Khatri-Rao Subspace Approaches on the ULA and a ...

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IEEE International Conference on Digital Signal Processing (DSP)

作者： Duan, Huiping Tuo, Tiantian Yin, Yunjie Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781509041657

In underdetermined direction-of-arrival (DOA) estimation using the covariance-based signal models, the computational complexity turns into a noticeable issue because of the high dimension of the virtual array manifold. In this paper, real-valued Khatri-Rao (KR) approaches are developed on the uniform linear array (ULA) and the nested array. The complexities of subspace decomposition and spectral search are reduced compared with the complex-valued KR approach. By designing a special transformation matrix, the influence of the noise is removed in the mean time while the data is transformed from the complex domain to the real domain. Deploying the sensors with nonuniform spacings can raise the degree of freedom (DOF) and hence help detect more sources in the underdetermined situation. To increase the DOF further, a new nested array geometry is designed. The real-valued denoising KR approach developed on the new nested array can resolve more sources with reduced complexities. The performance improvement is demonstrated by numerical studies.

关键词： underdetermined direction-of-arrival (DOA) estimation Khatri-Rao product real-valued nested array degree of freedom (DOF)

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Multi-Target Direction Finding in MIMO Radar Exploiting nested array 12

Multi-Target Direction Finding in MIMO Radar Exploiting Nest...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Qi, Han Jiang, Hong Zhou, Erning Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

ISBN: (纸本)9781509040933

In this paper, a novel multi-target direction finding approach is proposed for multiple input multiple output (MIMO) radar exploiting nested array. In our MIMO radar model, the transmit antennas are widely spaced to enable transmit diversity, while the receiver of the MIMO radar is configured with a 2-level nested array to enable direction finding. The nested array is an emerging non-uniform linear array (NLA) which can contain a large number of virtual sensors in MIMO radar system. When multiple targets are closely located in spatial domain, the proposed approach can significantly enhance the resolution and estimation accuracy relative to the conventional method with uniform linear array (ULA). In the proposed approach, no peaking searching is needed. Notably, it fits the situation that the number of targets is equal or even larger than that of the receive sensors, which is unavailable for the conventional MIMO radar system. The simulations validate the performance of the proposed approach.

关键词： MIMO radar direction finding nested array virtual aperture transmit diversity

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2D DOA and polarization estimation via synthetic nested dual-polarized array with scalable aperture in the presence of unknown nonuniform noise

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DIGITAL SIGNAL PROCESSING 2025年 158卷

作者： Yang, Yunlong Shan, Mengru Jiang, Guojun Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

For effectively enhancing the performance of two-dimensional direction-of-arrival and polarization estimation in practice, we propose a synthetic nested array with scalable aperture (SNASA), and then present a low-complexity estimation method, in unknown nonuniform noise environment. There is a systematic procedure to provide the numbers and positions of antennas for three subarrays of the SNASA, and then the estimation method based on one-dimensional sparse reconstruction can be used only once to achieve multi-parameter estimation. Compared with the existing nested polarization sensitive arrays (PSAs) with estimation methods, the proposed technique has some advantages: (a) The degrees of freedom of the SNASA is two times larger than that of the existing PSAs, due to the shared subarray of the former;(b) The physical aperture of the SNASA can be uniformly scaled while always keeping the ULA-based difference coarray, which enhances its flexibility applied for various platforms;(c) Both the nonuniform noise mitigation and the spatial information cooperated with polarized domain are automatically achieved by the block-sparse signal reconstruction, which avoids the extra complexity and the performance loss of the existing techniques for multi-parameter estimation case. The Cram & eacute;r-Rao bound and its existence condition are given in the presence of nonuniform noise. Numerical simulations are conducted to show the superior performance of the proposed technique.

关键词： nested array Scalable aperture Direction-of-arrival estimation Polarization estimation Unknown nonuniform noise

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