A novel unitary PARAFAC method for DOD and DOA estimation in bistatic MIMO radar

Xu, Baoqing; Zhao, Yongbo; Cheng, Zhiwei; Li, Hui

doi:10.1016/j.sigpro.2017.03.016

Cited by 66 publications

(35 citation statements)

References 21 publications

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“…Tensor decomposition, such as PARAFAC decomposition and Higher-order Singular Value Decomposition (HOSVD), can be used to construct three-dimensional received signal model in radar system. The PARAFAC algorithm has been applied to parameter estimation of MIMO radar [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Angle-Range-Polarization Estimation for Polarization Sensitive Bistatic FDA-MIMO Radar via PARAFAC Algorithm

2020

KSII TIIS

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In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor (PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved.

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Section: Introductionmentioning

confidence: 99%

Angle-Range-Polarization Estimation for Polarization Sensitive Bistatic FDA-MIMO Radar via PARAFAC Algorithm

2020

KSII TIIS

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“…Many efforts have been made and various algorithms have been developed on the topic of angle estimation algorithms for bistatic MIMO radar. For instance, Capon [9], subspace algorithms such as multiple signal classification (MUSIC) [10] and estimation of signal parameters via rotational invariance technique (ESPRIT) [11], parallel factor algorithm [12], higher‐order singular value decomposition (SVD) method [13] and sparse recovery (SR)‐based method [14]. Generally, the antenna arrays used in MIMO radar consist of a large number of antennas to achieve superior spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Each damaged antenna in the receive array structure, which is not working for receiving the target signals, results in the absence of a group of virtual elements in bistatic MIMO radar. Unfortunately, these algorithms in [9–14], which are heavily dependent on the structure of the array received data matrix, suffer the performance degradations as the received data matrix changes in the presence of antenna failures. It is a critical problem that cannot be solved by replacing the defective elements in a battlefield or space platform.…”

Section: Introductionmentioning

confidence: 99%

Array diagnosis and angle estimation in bistatic MIMO radar under array antenna failures

Chen

Zhuo

et al. 2019

IET Radar, Sonar & Navigation

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“…e array signal processing methods have high accuracy, and they are simple to implement [4][5][6][7]. At present, the technology of DOA estimation based on array signal processing is relatively well developed, typical representatives are the MUSIC method, ESPRIT method, and root-MUSIC method [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

DOA-Based Localization Method with Multiple Screening K-Means Clustering for Multiple Sources

Zhang

Wang

Cui

et al. 2019

Wireless Communications and Mobile Computing

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The existing angle-based localization methods are mainly suitable for the single source. Actually, there often exists situation which contains multiple target sources. To solve the problem of localization of multitarget sources, this paper presents a K-means clustering method based on multiple screening, which can effectively realize the localization of multiple sources based on DOA (direction of arrival) parameters. The method firstly establishes a cost function of position coordinates by using DOA parameters from the measuring position coordinates and then solves the cost function to obtain a complete set of real position coordinates and fuzzy position coordinates. As the distribution of real target coordinates is concentrated and the fuzzy target positions are scattered, the K-means clustering method is adopted to classify the coordinate set. In order to improve the positioning accuracy, a multiscreening process is introduced to screen the input samples before each clustering, and it can be finally concluded that clustering centers are the position coordinates of the target sources. Meanwhile, the complexity analysis and performance verification of this method are proposed. Simulation experiments show that this method can efficiently realize ambiguity-free, highly precise localization of multitarget sources.

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A novel unitary PARAFAC method for DOD and DOA estimation in bistatic MIMO radar

Cited by 66 publications

References 21 publications

Angle-Range-Polarization Estimation for Polarization Sensitive Bistatic FDA-MIMO Radar via PARAFAC Algorithm

Angle-Range-Polarization Estimation for Polarization Sensitive Bistatic FDA-MIMO Radar via PARAFAC Algorithm

Array diagnosis and angle estimation in bistatic MIMO radar under array antenna failures

DOA-Based Localization Method with Multiple Screening K-Means Clustering for Multiple Sources

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