Subspace-Based Detection and Localization in Distributed MIMO Radars

Lai, Yangming; Venturino, Luca; Grossi, Emanuele; Wei, Yi,

doi:10.1109/sam53842.2022.9827898

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Sensing, Computing and Communication Integration (SC2), which includes solid-state electronics, wireless communication, and mobile computing, is widely regarded as a new enabling technology and has been successful in both academia and industry [1]. In order to identify and locate targets utilising multiple-input multiple-output radars and sonars, integrated sensing and communication (ISAC) was introduced [2]. Today, one ISAC method utilised to find small geological structures in coal mines is guided wave exploration [3].…”

Section: Introductionmentioning

confidence: 99%

Guided wave signal‐based sensing and classification for small geological structure

Sun

Song

Zhou

et al. 2023

IET Signal Processing

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Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non‐negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three‐dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third‐order tensors data was constructed. Then, the TUCKER‐based NTSF algorithm was employed for feature extraction and classification. To achieve multi‐dimensional feature, the two‐dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi‐classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines.

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Section: Introductionmentioning

confidence: 99%

Guided wave signal‐based sensing and classification for small geological structure

Sun

Song

Zhou

et al. 2023

IET Signal Processing

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“…This paper considers the multi-target JDL problem in distributed MIMO radars where the transmit antennas employ waveforms with imperfect auto-and cross-correlation functions. Starting from the preliminary results in [47], we derive two novel subspace procedures that are inherently robust to the range sidelobe masking problem. More specifically, the main contribution can be summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms With Imperfect Auto- and Cross-Correlation

Lai

Venturino

Grossi

et al. 2023

IEEE Trans. Veh. Technol.

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This paper deals with the problem of joint detection and localization of multiple targets in a distributed multipleinput multiple-output radar system where the emitted waveforms present imperfect auto-and cross-correlation functions. In this context, the sidelobe masking effects can considerably degrade the detection and localization performance of correlation-based detectors. To address this issue, we first derive a convenient discrete-time signal model, wherein the echoes generated by a target towards each receive antenna are regarded as a subspace signal. Then, we formulate the joint detection and localization problem as a composite multiple hypothesis test and derive the optimal solution according to a generalized information criterion, whose complexity scales exponentially with the number of prospective targets. To reduce the computational burden, we derive two iterative approximate solutions which detect and localize one target at a time from the noisy data, upon mitigating the sidelobe masking caused by the previously-detected ones. Finally, we provide an extensive numerical analysis to demonstrate the effectiveness of the proposed solutions, even in the challenging case where an unknown number of strong and weak targets is present in the covered area.

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Subspace-Based Detection and Localization in Distributed MIMO Radars

Cited by 2 publications

References 23 publications

Guided wave signal‐based sensing and classification for small geological structure

Guided wave signal‐based sensing and classification for small geological structure

Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms With Imperfect Auto- and Cross-Correlation

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