Fast Angle Estimation and Sensor Self-Calibration in Bistatic MIMO Radar With Gain-Phase Errors and Spatially Colored Noise

Sheng, Guanqun; Wang, Han; Wen, Fangqing; Wang, Xinhai

doi:10.1109/access.2020.3004485

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…During the past few years, a large number of self-calibration and DOA estimation methods have been proposed to deal with the imperfect manifold [4][5][6][7][8][9]. However, most of the selfcalibration techniques are either applicable in particular array models, or focus on a certain type of array imperfection, for example, sensor location errors only in ref.…”

Section: Introductionmentioning

confidence: 99%

“…Then, authors performed the DOA estimation via the sparse total least squares technique after compensating the gain and phase errors. Sheng et al [6] discussed a covariance tensor-based parallel factor analysis estimator to handle the DOA estimation and sensor self-calibration problem in bistatic multiple-input multiple-output radar with gain-phase errors. Moreover, the mutual coupling matrix (MCM) in refs.…”

Section: Introductionmentioning

confidence: 99%

“…Sheng et al. [6] discussed a covariance tensor‐based parallel factor analysis estimator to handle the DOA estimation and sensor self‐calibration problem in bistatic multiple‐input multiple‐output radar with gain‐phase errors. Moreover, the mutual coupling matrix (MCM) in refs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Direction of arrival estimation of unknown emitter by deep neural networks with array imperfections

Chen

Mao

Liu

2023

IET Radar Sonar & Navi

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In array signal processing, high‐resolution direction‐of‐arrival (DOA) estimation by eigendecomposition method requires knowledge of the array covariance matrix and an exact characterisation of the array. The dependence of most such methods on the array manifold substantially degrades their array performance when the actual sensors deviate from their assumed nominal values. This article aims at formulating a deep neural network framework for DOA estimation with feasible computational complexity. The two‐stage algorithm is composed of a detection network and a series of parallel DOA estimation networks: the multilayer perceptron is trained to detect the presence of one or more sources in an angular sector at the first stage, the DOA estimation networks then estimate their respective DOAs at the second stage. When a single type of array imperfection is considered, numerical results reveal that the DNN‐based method can obtain DOA estimates with much higher precision than the existing techniques for significant location errors and gain/phase perturbations, they also illustrate the potential usefulness of the proposed algorithm towards better DOA estimation accuracy for mutual coupling as the mutual coupling matrix is not simplified. Moreover, the proposed method is finally extended to deal with the DOA estimation problem when multiple types of array perturbations coexist.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direction of arrival estimation of unknown emitter by deep neural networks with array imperfections

Chen

Mao

Liu

2023

IET Radar Sonar & Navi

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“…Array error estimation methods have been comprehensively researched. The method proposed in [31] estimates the channel-to-channel amplitude and phase errors based on a single target. The error between the azimuth signal processing results and the ideal results is minimized by adjusting channel weights.…”

Section: Introductionmentioning

confidence: 99%

A Novel GB-SAR System Based on TD-MIMO for High-Precision Bridge Vibration Monitoring

et al. 2022

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Ground-based synthetic aperture radar (GB-SAR) is a highly effective technique that is widely used in landslide and bridge deformation monitoring. GB-SAR based on multiple input multiple output (MIMO) technology can achieve high accuracy and real-time detection performance. In this paper, a novel method is proposed to design transmitting and receiving array elements, which increases the minimum spacing of the antenna by sacrificing several equivalent phase centers. In MIMO arrays, the minimum antenna spacing in the azimuth direction is doubled, which increases the variety of antenna options for this design. To improve the accuracy of the system, a new method is proposed to estimate channel phase errors, amplitude errors, and position errors. The position error is decomposed into three directions with one compensated by the phase error and two estimated by the strong point. Finally, we validate the accuracy of the system and our error estimation method through simulations and experiments. The results prove that the GB-SAR system performs well in bridge deformation and vibration monitoring with the proposed method.

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“…Wireless sensor network (WSN) has the advantages of flexible networking and low deployment cost. WSN adopts multihop transmission to realize coverage enhancement and increase fault tolerance for on-demand monitoring of substations [6,7]. It is complementary to fiber-optical communication and acts as an effective enabler for SPIoT.…”

Section: Introductionmentioning

confidence: 99%

Matching Learning-Based Relay Selection for Substation Power Internet of Things

Wang

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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Wireless sensor network (WSN) can effectively solve the problems of weak coverage, blind coverage, and low survivability of smart substation communication networks by deploying multiple relay nodes and adopting multihop transmission. However, there are still some challenges in the traditional relay selection strategy of WSN in substation, including incomplete information and the selection conflicts among multisource nodes. In this paper, we propose a matching learning-based relay selection mechanism for WSN-based substation power Internet of things (SPIoT). Firstly, considering the electromagnetic interference caused by the operation of high-voltage equipment, a multihop transmission model of SPIoT is built. Furthermore, based on the upper confidence bound (UCB) algorithm and matching theory, a matching learning-based relay selection (MLRS) algorithm is proposed to minimize the energy consumption of SPIoT devices. Simulation results demonstrate that MLRS outperforms existing algorithms in terms of energy consumption and optimal selection probability.

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Fast Angle Estimation and Sensor Self-Calibration in Bistatic MIMO Radar With Gain-Phase Errors and Spatially Colored Noise

Cited by 8 publications

References 31 publications

Direction of arrival estimation of unknown emitter by deep neural networks with array imperfections

Direction of arrival estimation of unknown emitter by deep neural networks with array imperfections

A Novel GB-SAR System Based on TD-MIMO for High-Precision Bridge Vibration Monitoring

Matching Learning-Based Relay Selection for Substation Power Internet of Things

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