Compressed Symmetric Nested Arrays and Their Application for Direction-of-Arrival Estimation of Near-Field Sources

Li, Shuang; Xie, Dan

doi:10.3390/s16111939

Cited by 24 publications

(9 citation statements)

References 25 publications

(50 reference statements)

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“…The nested array is a concatenation of two uniform linear arrays (ULAs) [38]: the inner and outer arrays, where the inner ULA has

N_{1}

sensors with spacing

d_{1}

and the outer ULA has

N_{2}

sensors with spacing

d_{2}

. Although there are many types of nested arrays, we only focus on the two-level nested array because the difference co-array (DCA) produced by the two-level nested array has no holes on the aperture.…”

Section: Preliminariesmentioning

confidence: 99%

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Zhang

Fang

et al. 2017

Sensors

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In modern communication and radar applications, large-scale sensor arrays have increasingly been used to improve the performance of a system. However, the hardware cost and circuit power consumption scale linearly with the number of sensors, which makes the whole system expensive and power-hungry. This paper presents a low-cost nested multiple-input multiple-output (MIMO) array, which is capable of providing O(2N2) degrees of freedom (DOF) with O(N) physical sensors. The sensor locations of the proposed array have closed-form expressions. Thus, the aperture size and number of DOF can be predicted as a function of the total number of sensors. Additionally, with the help of time-sequence-phase-weighting (TSPW) technology, only one receiver channel is required for sampling the signals received by all of the sensors, which is conducive to reducing the hardware cost and power consumption. Numerical simulation results demonstrate the effectiveness and superiority of the proposed array.

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“…The nested array is a concatenation of two uniform linear arrays (ULAs) [38]: the inner and outer arrays, where the inner ULA has

N_{1}

sensors with spacing

d_{1}

and the outer ULA has

N_{2}

sensors with spacing

d_{2}

Section: Preliminariesmentioning

confidence: 99%

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Zhang

Fang

et al. 2017

Sensors

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“…Array configurations using three linear subarrays were considered to improve the [23]- [27] A coprime array collinear with a ULA was suggested in [23]. The number of consecutive lags can be increased by properly selecting the location of the appended ULA, though large aperture size is required.…”

Section: Introductionmentioning

confidence: 99%

Sparse DOA Estimation Based on Multi-Level Prime Array With Compression

Alawsh

Muqaibel

2019

IEEE Access

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A signal emitter can be located using diverse types of direction finding (DF) techniques. One of the most widely used techniques is the direction of arrival (DOA) estimation using antenna arrays. An array configuration that can increase the degrees of freedom (DOF) or the number of estimated sources is desired. Multi-level prime array (MLPA) uses multiple uniform linear subarrays where the number of elements in the subarrays is a pairwise coprime integer. Compared with nested and coprime arrays, the MLPA requires a smaller aperture size which is important in mobile applications. The different MLPA configurations can be constructed for a given number of antennas and the one that maximizes the DOF is exploited. These configurations have a difference coarray with a large number of consecutive lags and few holes. The number of consecutive lags can be increased by properly compressing the inter-element spacing of one subarray under a fixed number of antennas and without changing the aperture size. This paper proposes a new compressed MLPA configuration and demonstrates its performance in sparse DOA estimation. The resultant array, MLPA with compressed subarray (MLPAC), can have a hole-free difference coarray as in nested array case. The MLPAC can estimate a larger number of sources using both MUSIC and sparse reconstruction algorithms. Mutual coupling between sensors has also been evaluated. The simulation results confirm the achievable DOF and the advantage of the proposed configuration in the DOA estimation. INDEX TERMSCoprime array, degrees of freedom, difference coarray, direction-of-arrival estimation, multi-level prime array, multi-level prime array with compressed subarray, nested array.

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“…Uniform circular array (UCA) is an attractive geometry and is preferable over uniform linear array (ULA) because of its 360° azimuth coverage, additional elevation angle information and almost identical beamwidth in the context of three-dimensional (3-D) parameter estimation [4,5,6,7,8,9,10]. …”

Section: Introductionmentioning

confidence: 99%

Closed-Form Algorithm for 3-D Near-Field OFDM Signal Localization under Uniform Circular Array

Wei

2018

Sensors

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Due to its widespread application in communications, radar, etc., the orthogonal frequency division multiplexing (OFDM) signal has become increasingly urgent in the field of localization. Under uniform circular array (UCA) and near-field conditions, this paper presents a closed-form algorithm based on phase difference for estimating the three-dimensional (3-D) location (azimuth angle, elevation angle, and range) of the OFDM signal. In the algorithm, considering that it is difficult to distinguish the frequency of the OFDM signal’s subcarriers and the phase-based method is always affected by errors of the frequency estimation, this paper employs sparse representation (SR) to obtain the super-resolution frequencies and the corresponding phases of subcarriers. Further, as the phase differences of the adjacent sensors including azimuth angle, elevation angle and range parameters can be expressed as indefinite equations, the near-field OFDM signal’s 3-D location is obtained by employing the least square method, where the phase differences are based on the average of the estimated subcarriers. Finally, the performance of the proposed algorithm is demonstrated by several simulations.

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Compressed Symmetric Nested Arrays and Their Application for Direction-of-Arrival Estimation of Near-Field Sources

Cited by 24 publications

References 25 publications

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Sparse DOA Estimation Based on Multi-Level Prime Array With Compression

Closed-Form Algorithm for 3-D Near-Field OFDM Signal Localization under Uniform Circular Array

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