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IEEE SENSORS LETTERS 2022年第11期6卷 1页

作者： Zhao, Jun Gui, Renzhou Dong, Xudong Tongji Univ Coll Elect & Informat Engn Shanghai 201802 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Jiangsu Peoples R China

Traditional DOA tracking algorithms have poor performance when considering source time-varying and impulsive noise interference. To address these issues, an improved multisource multi-Bernoulli (IMSMB) particle filtering algorithm based on phased fractional lower order moment is proposed. In the IMSMB algorithm, an unscented transform method is used to operate the surviving particles, which increases the diversity of resampling particles. Moreover, the proposed method is extended to nested array to improve tracking accuracy. Simulation results verify the superiority of the proposed method compared to the existing DOA tracking algorithm.

关键词： Radar tracking Signal processing algorithms Direction-of-arrival estimation Pediatrics Trajectory Estimation array signal processing Sensor systems sensor applications direction-of-arrival (DOA) tracking impulsive noise nested array phased fractional lower order moment (PFLOM)

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A novel DOA estimation algorithm using directional antennas in cylindrical conformal arrays

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Defence Technology（防务技术） 2021年第3期17卷 1042-1051页

作者： Xiao-feng Gao Ping Li Xin-hong Hao Guo-lin Li Zhi-jie Kong Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing100081China China Academy of Launch Vehicle Technology Beijing100076China

In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix(CCM) of the subarrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays,and the unitary estimation of the signal parameters via rotational invariance techniques(ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio(SNR) environment.

关键词： Direction of arrival(DOA) Conformal antenna array Interpolation technique nested array

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Maximizing the Number of Spatial Nulls with Minimum Sensors

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DEFENCE SCIENCE JOURNAL 2022年第4期72卷 638-646页

作者： Remadevi, M. Anjana, S. Sureshkumar, N. Rajesh, R. DRDO Naval Phys & Oceanog Lab Kochi 682021 Kerala India Rajagiri Sch Engn & Technol Kochi 682039 Kerala India

In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level.

关键词： Uniform line array Acoustic vector sensor nested array Spatial nulls Conventional and MVDR beamformer

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Sparse nested Cylindrical Sensor Networks for Internet of Mission Critical Things

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IEEE INTERNET OF THINGS JOURNAL 2018年第5期5卷 3353-3360页

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

In this paper, two new structures of sparse cylindrical sensor network are proposed, which are 1-D nested cylindrical sensor network (1-D NCSN) and 2-D NCSN. Considering the development of Internet of Mission Critical Things, numerous of devices need to be interconnected and sharing information. Sparse structures of sensor networks will not only reduce the cost of deployment, but also decrease the quantity of data. The reason of choosing cylindrical sensor network is that cylindrical array has been widely used in wireless communication and underwater target detection. According to the characteristic of cylindrical array, the 1-D and 2-D nested array is extended to build the 1-D and 2-D sparse cylindrical sensor networks, respectively. Comparing with the 1-D NCSN, the 2-D structure could save more elements, while it is hard to derive the beampattern expression of the 2-D NCSN. Simulation results show that the 1-D NCSN has a higher resolution than the uniform cylindrical sensor network (UCSN). In addition, through augmented matrix MUSIC approach, 2-D NCSN could detect all targets in the range, even if the number of targets is larger than the number of real elements in the network. However, both the UCSN and the 1-D NCSN could not find all the sources.

关键词： Direction-of-arrival (DOA) estimation Internet of Things (IoT) nested array sparse cylindrical sensor network

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Integrated Sensing and Communication-Assisted User State Refinement for OTFS Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第2期23卷 922-936页

作者： Shi, Yunmei Huang, Yi Tongji Univ Dept Informat & Commun Engn Shanghai 201804 Peoples R China

Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.

关键词： Orthogonal time frequency space (OTFS) integrated sensing and communication (ISAC) user state refinement hybrid digital-analog architecture nested array

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Calibrating nested Sensor arrays With Model Errors

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2015年第11期63卷 4739-4748页

作者： Han, Keyong Yang, Peng Nehorai, Arye Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA

We consider the problem of direction of arrival (DOA) estimation based on a nonuniform linear nested array, which is known to provide O(N-2) degrees of freedom (DOFs) using only N sensors. Both subspace-based and sparsity-based algorithms require certain modeling assumptions, e.g., exactly known array geometry, including sensor gain and phase. In practice, however, the actual sensor gain and phase are often perturbed from their nominal values, which disrupts the existing DOA estimation algorithms. In this paper, we investigate the self-calibration problem for perturbed nested arrays, proposing corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares (STLS) is used to deal with the phase error issue. In addition, we provide the Cramer-Rao bound (CRB) to analyze the robustness of the estimation performance of the proposed approaches. Furthermore, we extend the calibration strategies to general nonuniform linear arrays. Numerical examples are provided to verify the effectiveness of the proposed strategies.

关键词： Calibration direction of arrival (DOA) estimation model error nested array nonuniform linear array sparse total least squares (STL) Toeplitz

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3D nested distributed massive MIMO: Modeling and performance analysis

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AD HOC NETWORKS 2017年 58卷 6-12页

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

The Large Scale Multiple-input-multiple-output (MIMO) system, also called Massive MIMO in past researches, are considering the azimuth angle only. However, in a 3D distributed antenna system, the elevation angle cannot be ignored. nested array as a two dimensional arrays was firstly proposed to perform array processing with increased degree of freedom, using less number of sensors at the same time. This paper introduces a novel 3D MIMO antenna deployment based on nested co-array. As the difference co arrays are invariance, in the 3D nested distributed MIMO system, we are able to calculate the covariance matrix of channels. Based on this inference, we model a 3D nested distributed MIMO system and analyze its performance with achievable sum rate. By applying the nested deployment, our proposed method could achieve a Massive MIMO with less number of physical antennas. Numerical results also provided to support our model. (C) 2016 Elsevier B.V. All rights reserved.

关键词： 3D Massive MIMO Difference co-array nested array

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Real-Valued Sparse Bayesian Learning for DOA Estimation With Arbitrary Linear arrays

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 4977-4990页

作者： Dai, Jisheng So, Hing Cheung Jiangsu Univ Dept Elect Engn Zhenjiang 212013 Jiangsu Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Sparse Bayesian learning (SBL) has become a popular approach for direction-of-arrival (DOA) estimation, but its computational complexity for Bayesian inference is quite high because calculating inverse of a large complex matrix per iteration is required. It is known that the computational load can be reduced by transforming the complex-valued problem into a real-valued one. However, the commonly used real-valued transformation works for uniform linear arrays (ULAs) only. In this paper, we propose a new real-valued transformation for DOA estimation with arbitrary linear arrays by exploiting the virtual steering of linear arrays. Then, we introduce an alternating optimization algorithm based on the variational Bayesian inference (VBI) methodology to iteratively obtain a stationary solution to the real-valued sparse representation problem. Because of utilizing the additional real-valued structure, the VBI scheme can achieve a better performance in terms of both estimation accuracy and computational complexity. Moreover, we embed the generalized approximate message passing (GAMP) into the VBI-based method for further complexity reduction. Although there may be a performance loss for the GAMP variant, simulation results reveal its substantial performance improvement over existing methods.

关键词： Direction-of-arrival estimation Estimation Bayes methods Covariance matrices array signal processing Optimization Minimization Direction-of-arrival (DOA) estimation real-valued transformation sparse Bayesian learning (SBL) variational Bayesian inference (VBI) nested array off-grid

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Polarization-Angle-Frequency Estimation With Linear nested Vector Sensors

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IEEE ACCESS 2018年 6卷 36916-36926页

作者： Han, Xiaodong Shu, Ting He, Jin Yu, Wenxian Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China

This paper considers the problem of multiple dimensional parameter estimation of radar signals using a linear nested vector sensor array. We propose a computationally efficient polarization-angle-frequency estimation algorithm based on spatial-temporal nested sampling. Radar cross-sections diversity in multiple coherent processing intervals is exploited to construct a virtual polarization-spatial-temporal manifold with extended degrees of freedom. Then, a computational efficient method without eigen-decomposition is derived to estimate Khatri-Rao signal subspace. Automatically paired polarization, azimuth-elevation angles, and doppler frequency estimates are finally obtained by exploiting the idea of the estimation of signal parameters via rotational invariance techniques algorithm. The effectiveness of the proposed method is verified through numerical examples.

关键词： Angle and frequency estimation polarization estimation pulsed Doppler nested sampling nested array degree of freedom

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Reduced-Complexity Maximum Likelihood Direction-of-Arrival Estimation Based on Spatial Aliasing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第24期62卷 6568-6581页

作者： Shin, Jong-Woo Lee, Young-Jun Kim, Hyoung-Nam Pusan Natl Univ Dept Elect Engn Pusan 602739 South Korea Attached Inst Elect & Telecommun Res Inst ETRI Taejon 305600 South Korea

Spatial aliasing is an undesirable phenomenon that prevents the unique determination of the direction of arrival (DOA) of impinging signals in array signal processing. However, the characteristics of spatial aliasing that generates ambiguous DOAs can be also used to reduce the computational complexity in maximum-likelihood (ML) DOA estimation. This paper proposes a structural method to dramatically reduce the computational complexity of the ML DOA estimation using the spatial aliasing generated by a nested array structure with a doubly scaled aperture. An ML full grid search is computationally simplified by the highly compressed searching range and the small number of candidate values to be searched which are derived based on spatial aliasing. Performance analyses based on the theoretical bounds and computational complexity with computer simulations show that the proposed method requires an extremely reduced computational load compared to the conventional ML DOA estimation with a uniform linear array (ULA) while achieving the performance enhancement in the estimation accuracy due to the enlarged array aperture.

关键词： array signal processing direction of arrival estimation maximum likelihood estimation nested array spatial aliasing

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