Optimization of Angle-of-Arrival Estimation Via Real-Valued Sparse Representation With Circular Array Radar

Kazemi, Iman; Moniri, M. R.; Kandovan, Ramin Shaghaghi

doi:10.1109/access.2013.2270173

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…In other words, a considerable amount of computations can be saved if the complex‐valued DOA estimation problem in (11) can be replaced by real‐valued problem. In addition, Dai et al [24] and Kazemi et al [34] showed that the real‐valued methods can obtain better resolution ability and estimation accuracy than the complex‐valued methods. To avoid the limitation of unitary algorithms, we present a generalised RVSR for efficient single snapshot DOA estimation with arbitrary array configurations in this part.…”

Section: Proposed Methodsmentioning

confidence: 99%

Real‐valued sparse representation for single snapshot direction‐of‐arrival estimation in shipborne high‐frequency surface wave radar

Wang

Xie

Zhou

et al. 2015

IET Radar, Sonar & Navigation

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By exploiting the intrinsic sparsity of the spatial spectrum for a given resolution cell of range and Doppler, this study introduces the recently developed sparse representation approaches for direction-of-arrival estimation in shipborne high-frequency surface wave radar (HFSWR). These approaches can reconstruct the sparse signal and obtain highresolution spatial spectrum with a small number of snapshots or even single snapshot. A generalised real-valued sparse representation, which seeks to replace the complex-valued problem with a real one, is proposed to reduce the computational complexity and improve the resolution probability and the estimation accuracy. The advantage of the proposed method is demonstrated by theoretical analysis and numeral simulations. Furthermore, validation of the proposed method is verified by the experiment of shipborne HFSWR on the Yellow Sea of China.

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Section: Proposed Methodsmentioning

confidence: 99%

Real‐valued sparse representation for single snapshot direction‐of‐arrival estimation in shipborne high‐frequency surface wave radar

Wang

Xie

Zhou

et al. 2015

IET Radar, Sonar & Navigation

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“…In recent years, much research has been done on the topic of localization by estimating the directions-of-arrival (DOA) (i.e., azimuth angles and elevation angles) and ranges of multiple targets in many applications, such as radar, sonar, and wireless communications [ 1 , 2 , 3 ]. Many methods based on one-dimensional (1D) uniform linear array (ULA) have been proposed for estimating azimuth angle [ 4 , 5 , 6 , 7 ], or joint range and azimuth angle estimation [ 8 , 9 ], such as the well-known multiple signal classification (MUSIC) algorithm [ 4 ] and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm [ 5 ], and the generalized MUSIC and ESPRIT [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

3D Target Localization of Modified 3D MUSIC for a Triple-Channel K-Band Radar

Choi

Chong

et al. 2018

Sensors

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In this paper, a modified 3D multiple signal classification (MUSIC) algorithm is proposed for joint estimation of range, azimuth, and elevation angles of K-band radar with a small 2 × 2 horn antenna array. Three channels of the 2 × 2 horn antenna array are utilized as receiving channels, and the other one is a transmitting antenna. The proposed modified 3D MUSIC is designed to make use of a stacked autocorrelation matrix, whose element matrices are related to each other in the spatial domain. An augmented 2D steering vector based on the stacked autocorrelation matrix is proposed for the modified 3D MUSIC, instead of the conventional 3D steering vector. The effectiveness of the proposed modified 3D MUSIC is verified through implementation with a K-band frequency-modulated continuous-wave (FMCW) radar with the 2 × 2 horn antenna array through a variety of experiments in a chamber.

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“…Hence, in this paper, the non-redundant TDOA measurements are used. Reference [5] studies the optimum sensor array of the source localization based on different measurements, such as Angle of Arrival (AOA) [9,10], Time of Arrival (TOA) [11,12], and Received Signal Strength (RSS) [13]. The optimality criterion used in [5] is maximizing the determinant of the FIM matrix.…”

Section: Introductionmentioning

confidence: 99%

Optimum Sensor Array for Passive Localization from Time Differences of Arrival

Zhou

Huang

Gao

2014

AMM

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The problem how to improve the accuracy of passive localization from time differences of arrival received considerable interest. The localization performance of any unbiased estimator can be explicitly characterized by certain measures, for example, by the Cramer-Rao lower bound (CRLB) on the estimator variance. The lower the CRLB, the better localization performance. It is well known that the relative sensor-target geometry can significantly affect the performance of any particular localization algorithm. It demonstrates, when target is surrounded by the sensors, uniform angular array is the optimum sensor placement, in which the CRLB is minimized.

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Optimization of Angle-of-Arrival Estimation Via Real-Valued Sparse Representation With Circular Array Radar

Cited by 8 publications

References 11 publications

Real‐valued sparse representation for single snapshot direction‐of‐arrival estimation in shipborne high‐frequency surface wave radar

Real‐valued sparse representation for single snapshot direction‐of‐arrival estimation in shipborne high‐frequency surface wave radar

3D Target Localization of Modified 3D MUSIC for a Triple-Channel K-Band Radar

Optimum Sensor Array for Passive Localization from Time Differences of Arrival

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