Order-Statistic Based Target Detection with Compressive Measurements in Single-Frequency Multistatic Passive Radar

Ma, Junhu; Li, Hongbin; Gan, Lu

doi:10.1016/j.sigpro.2022.108785

Cited by 6 publications

(12 citation statements)

References 23 publications

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“…We compare the proposed detectors with the OSOMP detector, the CC detector, the energy detector (ED) and the GCC detector [21,27]. The test statistic of the OSOMP detector is given by the following equation:…”

Section: Simulationsmentioning

confidence: 99%

“…In this experiment, numerical Monte Carlo (MC) simulations are conducted to verify the performance of P FA for the proposed SFN-CSD in (14) and SFN-UNCSD in (21) as a function of the detection threshold. More specifically, we select N = 1000, M = 600, K ∈ {3, 5}, Φ is a real random Gaussian measurement matrix satisfying N 0; I M ð Þ, the dictionary matrix Ψ is built based on the previous work refs.…”

Section: False Alarm Of the Proposed Detectorsmentioning

confidence: 99%

“…Additionally, because the target is sparse in the detection cells, the CS-based framework in passive radar detection has been widely investigated [16][17][18][19][20][21]. Specifically, the authors in ref.…”

Section: Introductionmentioning

confidence: 99%

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GLRT‐based compressive subspace detectors in single‐frequency multistatic passive radar systems

Ma,

Zhao,

Wang

et al. 2023

IET Radar Sonar & Navi

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The authors study the problem of compressive target detection in a single‐frequency network (SFN)‐based multistatic passive radar system (MS‐PRS) consisting of multiple illuminators of opportunity (IOs) and one receiver. Firstly, a generalised likelihood ratio test (GLRT)‐based SFN‐based compressive subspace detector (SFN‐CSD) is derived by exploiting the sparsity of the target echoes for the case of known noise variance. When the noise variance is unknown, an SFN‐based unknown‐noise (UN) compressive subspace detector is proposed, referred to as the SFN‐UNCSD. Moreover, closed‐form expressions of the probability of false alarm and detection of the proposed detectors are deriived. It is proved that the SNF‐UNCSD has a constant false alarm rate (CFAR) property. Finally, numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors.

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Section: Simulationsmentioning

confidence: 99%

Section: False Alarm Of the Proposed Detectorsmentioning

confidence: 99%

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GLRT‐based compressive subspace detectors in single‐frequency multistatic passive radar systems

Ma,

Zhao,

Wang

et al. 2023

IET Radar Sonar & Navi

Self Cite

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“…Similar to Refs. [27–29], it is assumed that the transmitted signal s is a deterministic waveform sampled from

\mathcal{C}\mathcal{N}\left(\mathbf{0},\mathbf{I}\right)

, while the noise w sc is sampled from complex Gaussian distribution with

\mathcal{C}\mathcal{N}\left(\mathbf{0},{\sigma }^{2}\mathbf{I}\right)

. From Ref.…”

Section: Simulationsmentioning

confidence: 99%

“…Similar to Refs. [27][28][29], it is assumed that the transmitted signal s is a deterministic waveform sampled from CN 0; I ð Þ, while the noise w sc is sampled from complex Gaussian distribution with CN 0; σ 2 I ð Þ. From Ref.…”

Section: Simulationsmentioning

confidence: 99%

Compressive detection of multiple targets in passive bistatic radar

Ma¹,

Rui²

2022

IET Radar Sonar & Navi

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The problem of compressive detection of multiple targets in a passive bistatic radar system consisting of one illuminator of opportunity and one receiver is considered. Specifically, a measurement matrix is designed to remove the direct-path and multi-path interference and obtain the compressive observations simultaneously. Considering that the degree of sparsity, that is, the number of targets, is unknown, an orthogonal matching pursuit (OMP)-based detector, referred to as the ‖r k ‖ ∞ -based OMP detector, is proposed by analysing the distribution characteristics of the infinite norm of the residual. It is shown that the test statistic of the proposed detector leads to an order statistic, and then a closed-form expression of probability of false alarm and detection of the proposed detector is provided. Finally, numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors.

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Masking Effect Mitigation for FM-Based Passive Radar via Nonlinear Sparse Recovery

Xie

Wan

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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Order-Statistic Based Target Detection with Compressive Measurements in Single-Frequency Multistatic Passive Radar

Cited by 6 publications

References 23 publications

GLRT‐based compressive subspace detectors in single‐frequency multistatic passive radar systems

GLRT‐based compressive subspace detectors in single‐frequency multistatic passive radar systems

Compressive detection of multiple targets in passive bistatic radar

Masking Effect Mitigation for FM-Based Passive Radar via Nonlinear Sparse Recovery

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