Array signal processing and systems

Liao, Bin; Madanayake, Arjuna; Agathoklis, P.

doi:10.1007/s11045-018-0555-7

Cited by 22 publications

(6 citation statements)

References 17 publications

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“…For CPA, the number of signals that can be resolved is K ≤ min{P 2 1 , P 2 2 } − 1, where P 1 and P 2 are the coprime integers, and the total number of sensors is W = P 2 1 + P 2 2 − 1. For GCPA, the number of signals that can be resolved is 144 (8,9) 109 (8,7,9,6) 41 (7,8) 31 (5,11) are the coprime integers, and the total number of sensors is…”

Section: Simulation Resultsmentioning

confidence: 99%

L-Shaped Sparse Array Structure for 2-D DOA Estimation

Cao

et al. 2020

IEEE Access

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In order to improve the performance of two-dimensional (2-D) direction-of-arrival (DOA) estimation, an L-shaped sparse array structured by two new sparse linear arrays is proposed. Each part of the proposed L-shaped array is used for one-dimensional (1-D) azimuth and elevation estimation, respectively. The new sparse linear array configuration is consisted of two subarrays which have N and M physical sensors, respectively. Owing to the advantages of the proposed sparse linear array configuration, higher degrees-of-freedom (DOF) and larger array aperture can be achieved when the second-order statistics of the received data is used. To match the azimuth and elevation angles automatically, the cross-covariance matrix of the two parts of the proposed L-shaped array is used to estimate the paired DOA angles. Based on the proposed L-shaped array configuration, (M + 1)(N − M/2) signal directions can be estimated with M + N sensors in each part and totally 2M + 2N − 1 sensors. Through the comparative analysis of the parameters with other sparse planar arrays, the proposed L-shaped array can achieve better performance due to its higher DOF and larger array aperture. Finally, numerical simulation results verify the superiority of the 2-D DOA method based on the proposed L-shaped array.

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Section: Simulation Resultsmentioning

confidence: 99%

L-Shaped Sparse Array Structure for 2-D DOA Estimation

Cao

et al. 2020

IEEE Access

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“…The superior angle discrimination curves are closer to the angulation results of an impulse response, which conforms to the theoretical derivation in the last section. Hence, during the actual imaging process, the accuracy degree of the angle discrimination curve will definitely affect the eventual imaging results [25,26].…”

Section: Fig 6 Angle Discrimination Results For Different K Valuesmentioning

confidence: 99%

High-resolution forward-looking imaging algorithm for missile-borne detectors

Cheng

Gao

Zhou

2019

JSEE

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Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions; thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.

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“…Direction-of-arrival (DOA) estimation is an important research direction of array signal processing. It is widely used in radar, communications, sonar and seismic fields [1][2][3][4][5][6][7][8][9][10][11][12][13]. The classic DOA estimation algorithm is mainly designed for uniform linear arrays (ULA).…”

Section: Introductionmentioning

confidence: 99%

Underdetermined direction of arrival estimation of wideband signal based on sparse array

Cao

Zhang

et al. 2022

IET Microwaves Antenna & Prop

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To solve the problem of the mismatch of the wideband underdetermined direction of arrival (DOA) estimation under the condition of the On‐grid model, this paper extends the narrowband Off‐Grid model to wideband and proposes a new DOA estimation algorithm for Off‐Grid sources based on group sparsity. The proposed algorithm first obtains the preliminary estimation result under the current predefined discrete grid through the group sparsity wideband DOA estimation algorithm. Then, the Off‐Grid optimisation problem is adopted to calculate the Off‐Grid deviation vector. It is also assumed that the off‐grid deviation vectors of each frequency subband are exactly the same, thereby reducing the number of parameters to be estimated. Therefore, the proposed algorithm can not only maintain similar or even better estimation accuracy but also greatly reduce the computational complexity. Finally, simulation is conducted and the results verify the effectiveness and performance of the proposed method.

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Array signal processing and systems

Cited by 22 publications

References 17 publications

L-Shaped Sparse Array Structure for 2-D DOA Estimation

L-Shaped Sparse Array Structure for 2-D DOA Estimation

High-resolution forward-looking imaging algorithm for missile-borne detectors

Underdetermined direction of arrival estimation of wideband signal based on sparse array

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