In this paper, a method of grating lobes and sidelobes suppression for distributed two-level nested array is presented. The method combines the distributed nested array structure and the pattern product theorem to obt...
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ISBN:
(纸本)9781728166704
In this paper, a method of grating lobes and sidelobes suppression for distributed two-level nested array is presented. The method combines the distributed nested array structure and the pattern product theorem to obtain a distributed two-level nested array two-way pattern, which can effectively suppress the grating lobes and sidelobes. The Chebyshev window is used to design the weights of the transmit array pattern to achieve ultralow sidelobes performance. Numerical simulations are provided for testing the proposed method. Simulation results demonstrate the effectiveness of the proposed method, which will help to improve the detection performance of radar.
In this paper, we investigate the issue of direction of arrival (DOA) estimation of non-circular (NC) signals, and propose a modified nested array with enhanced degrees of freedom (DOFs) and a vectorized NC propagator...
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ISBN:
(纸本)9781728119465
In this paper, we investigate the issue of direction of arrival (DOA) estimation of non-circular (NC) signals, and propose a modified nested array with enhanced degrees of freedom (DOFs) and a vectorized NC propagator method (VNC-PM) algorithm which can utilize the difference-sum co-array to perform DOA estimation. Specifically, we modify the conventional nested array by switching the positions of the two subarrays aiming to obtain a difference-sum co-array with higher DOFs. In order to fully exploit the provided DOFs and obtain higher estimation accuracy, we utilize the NC characteristic to generate the equivalent received signal of the difference-sum co-array and exploit the quasi-stationary property of the signal to avoid the spatial smoothing technique which leads to the DOF degradation. The modified PM based on the rotational invariance is then employed to obtain DOA estimates, where NC phases can be eliminated. Simulations are provided to evaluate the superiority of the proposed configuration and DOA estimation algorithm.
This paper mainly investigates the problem of direction of arrival (DOA) estimation for a monostatic MIMO radar. Specifically, the proposed array, which is called a nested-nested sparse array (NNSA), is structurally c...
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This paper mainly investigates the problem of direction of arrival (DOA) estimation for a monostatic MIMO radar. Specifically, the proposed array, which is called a nested-nested sparse array (NNSA), is structurally composed of two nested subarrays, a NA with N1+N2 elements and a sparse NA, respectively, with N3+N4 elements. The design process of NNSA is optimized into two steps and presented in detail. Setting NNSA as transmitter/receiver arrays, we derive the closed-form expression of consecutive DOFs and calculate the mutual coupling coefficient. Eventually, extensive simulations are carried out and the results verify the superiority of the proposed array over the previous arrays in terms of consecutive DOFs, array aperture and mutual coupling effect.
In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D DOA estimation algorithm...
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In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) model is obtained by the difference coarray corresponding to one-dimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. Using this method, the dimensionality of the sensing matrix and data size can be reduced. The sparse Bayesian learning algorithm is used to estimate one-dimensional angles. By using the one-dimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angles are estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.
This paper proposes a subspace data fusion (SDF)-based direct positioning determination (DPD) method for noncircular sources with a moving nested array (NA). The DPD algorithms using a uniform linear array (ULA) and c...
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This paper proposes a subspace data fusion (SDF)-based direct positioning determination (DPD) method for noncircular sources with a moving nested array (NA). The DPD algorithms using a uniform linear array (ULA) and coprime (CP) array are available in the existing literature. Sparse arrays have larger apertures than ULAs, which enhances degrees of freedom (DOFs). However, the existing sparse CP arrays have limited DOF and also have poor detecting ability. In this paper, the physical sensors of NA are rearranged to obtain a difference sum NA (DSNA) and second-order super NA (II-SNA). The rearranged NA sensors are seen to increase array aperture. The noncircular characteristics of the sources are also used to improve accuracy in positioning. To obtain high DOF in NA, DSNA, and II-SNA, the covariance matrices are vectorized. The coherency of the virtual array is resolved by applying the spatial smoothing technique. Finally, the SDF-based DPD is used to establish the cost function and the target is localized. The simulation results are provided for the NA, DSNA, and II-SNA and are compared with the existing CP array DPD algorithm. The results show that the proposed method shows significant enhancement in localization accuracy. The Cramer-Rao lower bound (CRLB), complexity, and performance comparisons are also described in this paper.
nested arrays exhibit higher spatial resolution (SR) and enhanced degrees-of-freedom (DOFs) with fewer sensors, and they have been utilized for direction-of-arrival (DOA) estimation of both far-field (FF) and near-fie...
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nested arrays exhibit higher spatial resolution (SR) and enhanced degrees-of-freedom (DOFs) with fewer sensors, and they have been utilized for direction-of-arrival (DOA) estimation of both far-field (FF) and near-field (NF) sources. In this study, an improved symmetric nested array configuration with a given number of sensors was developed, which achieved increased consecutive and unique lags and thus resolved more targets than the actual number of inherent array sensors. In particular, the analytical expressions of the number of consecutive lags, the number of unique lags, and the virtual array aperture were derived for quantitative evaluation and comparison, as well as the corresponding array composition parameters of the optimal array geometry were obtained. In the mixed sources localization scheme, a special cumulant matrix was constructed to eliminate the range parameter in the NF steer vectors by exploiting the symmetric feature. Both subspace and sparse reconstruction techniques were exploited in order to directly obtain the DOAs of both FF and NF sources. With the estimated DOAs and the covariance matrix of the array output, the NF sources could be identified, and the corresponding range parameters could also be obtained by a one-dimension (1-D) spectrum search scheme. Numerous simulation results demonstrated that the proposed array showed a remarkable performance in terms of estimation accuracy, SR capacity, and numerous DOFs compared to state-of-the-art symmetric nested arrays. (C) 2020 Elsevier Inc. All rights reserved.
Sparse nested arrays (SNAs) are presented for highly accurate angle estimation. A nested array is sparse if its sensor-spacing of the first level is extended beyond a half-wavelength. Each array grid is equipped with ...
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Sparse nested arrays (SNAs) are presented for highly accurate angle estimation. A nested array is sparse if its sensor-spacing of the first level is extended beyond a half-wavelength. Each array grid is equipped with an identical two-component subarray, such as a half-wavelength spaced doublet, a spatiallycolocated p-u probe and a spatially-orthogonal dipole/loop pair, to solve angle ambiguities caused by aperture extension. (C) 2020 Elsevier B.V. All rights reserved.
Millimeter wave systems often have very few number of RF chains as compared to the number of antenna elements in order to reduce power consumption and complexity. In this work, we study the design of the mapping funct...
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ISBN:
(纸本)9781728143002
Millimeter wave systems often have very few number of RF chains as compared to the number of antenna elements in order to reduce power consumption and complexity. In this work, we study the design of the mapping function Q(N) (X) (r) from the antenna element space of size N to the reduced beamspace dimension r. In the first part of this paper, we restrict the mapping function to antenna selection matrix that chooses antenna elements in a large ULA to form a nested array. For r <= 2 root N - 1, we can identify such nested arrays using appropriate selection matrices. For r > 2 root N - 1, we select elements associated with each additional RF chain to maximally improve CRLB for a limited scattering environment, modeled using a single angle of arrival. The above design of the mapping function lacks beamforming (BF) gain, which is crucial for the channel estimation (CE) phase in mmWave communications. In the second part of the paper, we design a mapping function Q(N) (X) (r) that provides BF gain while forming an approximate nested array, when focused in an appropriately narrow angular space.
Recently, nested array has been proposed to estimate the direction of arrival (DoA) of O(N-2) sources using only O(N) physical sensors. In this paper, we investigate a new method to achieve higher degrees of freedom f...
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ISBN:
(纸本)9781728119731
Recently, nested array has been proposed to estimate the direction of arrival (DoA) of O(N-2) sources using only O(N) physical sensors. In this paper, we investigate a new method to achieve higher degrees of freedom for the nested array for non-circular sources. By exploiting the properties of the signal sources, a new model is constructed based on a longer virtual uniform linear array (ULA). Simulation results show that the proposed method can identify more sources with N sensors and improves the DoA estimation accuracy.
In this treatise, we have proposed an uplink channel estimation algorithm for nested and linear array antennas with hardware imperfections, antenna position error, and channel correlation in a multi-cell, multi-user w...
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In this treatise, we have proposed an uplink channel estimation algorithm for nested and linear array antennas with hardware imperfections, antenna position error, and channel correlation in a multi-cell, multi-user wireless system with a one-ring scattering channel model. Hardware impairments and antenna position errors assume special importance in modern wireless system due to their severe impacts on system performance. With the increase of radio frequency (RF) in the modern wireless system and the subsequent usage of a large number of antennas for beamforming and meeting ever-increasing data rates, those errors are much relevant today. We have considered a sparse nested array (NA) and uniform linear array (ULA) for array geometry at the base station (BS). In this work, closed-form expression and bounds on the mean square error (MSE) of the linear minimum mean square error (LMMSE) channel estimate are derived. We have also analyzed the spectral efficiency (SE) of a simplified system with ULA and NA at the BS, given the model with hardware distortion and antenna position error. Two cases for (a) perfect channel side information (CSI) and (b) estimated channel are evaluated and compared in terms of their SEs, while the closed-form expression for SE with ULA at the BS considering perfect CSI is derived and verified. Numerical simulations are conducted to verify the accuracies of our derived results.
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