Adaptive multichannel detectors for distributed target based on gradient test

Tang, Peiqin; Dong, Ran; Liu, Weijian; Li, Jun; Du, Qian; Wang, Yongliang

doi:10.1016/j.sigpro.2021.108350

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…First, we investigate the influence of different detector parameters, including typical models of multiple dominant scatters (MDS) in Table 1, where only h 0 range cells are assumed to have signal components in K range cells. Then for comparison, we consider the existing unstructured subspace detectors (S‐Gradient [18], S‐GLRT [17], S‐Wald [17], S‐2SD [17] and S‐Rao [17]) and the persymmetric subspace detectors (Per‐Rao and Per‐Wald [31]). The false alarm probability P fa is set to 10 −3 .…”

Section: Resultsmentioning

confidence: 99%

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Persymmetric subspace detector for distributed target in partially homogeneous environment

Jian¹,

Xie²,

Wang³

et al. 2022

IET Radar Sonar & Navi

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This paper deals with the problem of distributed target detection in partially homogeneous Gaussian clutter whose covariance matrix is unknown but persymmetric. It is assumed that primary data and training data share the same clutter covariance matrix structure but different power levels. The target signal is supposed to lie in a multi‐rank subspace with unknown coordinates. A persymmetric subspace detector is designed based on the generalised likelihood ratio test criteria. It is theoretically demonstrated that the proposed detector possesses constant false alarm rate property with respect to the unknown clutter covariance matrix as well as the power level. Experimental results illustrate the performance advantage of the proposed detector over the existing competitors, especially in training‐limited scenarios.

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Section: Resultsmentioning

confidence: 99%

“…In addition, the detectors based on Gradient criteria are also derived for HE and PHE in Ref. [18] and exhibit the effectiveness in presupposed scenarios.…”

Section: Introductionmentioning

confidence: 99%

Persymmetric subspace detector for distributed target in partially homogeneous environment

Jian¹,

Xie²,

Wang³

et al. 2022

IET Radar Sonar & Navi

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“…We can directly obtain

{\boldsymbol{\Theta }}_{\mathrm{r}0}={\mathbf{0}}_{pK\times 1}

and

{f}_{0}(\boldsymbol{Z},\boldsymbol{Y})={{f}_{1}(\boldsymbol{Z},\boldsymbol{Y})\vert }_{{\boldsymbol{\Theta }}_{\mathrm{r}}={\boldsymbol{\Theta }}_{\mathrm{r}0}}

, for the target is absent under hypothesis H 0 . Compared with the GLRT, Wald and Rao tests, the Gradient test seems to have much simpler form [21]. Note that the expression in square brackets with

\boldsymbol{\Theta }={\widehat{\boldsymbol{\Theta }}}_{0}

in Equation (5) has the similar form as the locally most powerful (LMP) test [30–32].…”

Section: Detector Designmentioning

confidence: 99%

“…For the clutter-dominant environment, the detectors based on generalised likelihood ratio test (GLRT), Rao test, Wald test and their two-step variations are derived for range-spread targets in HE [20]. Moreover, the Gradient test is applied to detect a range-spread target in the HE [21] without considering the interference. As for the clutter-dominant PHE, the GLRT, Rao test, Wald test and their two-step versions are derived in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Subspace gradient tests for range‐spread target detection in structured interference plus Gaussian clutter

Jian¹,

He²,

Liu³

et al. 2023

IET Radar Sonar & Navi

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This study considers the problem of detecting range-spread targets embedded in subspace interference plus Gaussian clutter with an unknown covariance matrix. The target and interference signals are modeled in terms of deterministic signals belonging to two known subspaces, respectively. Based on the Gradient test criterion, two adaptive detectors are devised for rejecting subspace interference in homogeneous and partially homogeneous environments, respectively. Both of the proposed detectors theoretically exhibit a desirable property of a constant false alarm rate with respect to the clutter covariance matrix as well as the power level. Furthermore, the numerical results show that, compared with their existing counterparts, the proposed detectors exhibit better detection performance and satisfactory suppression performance for the interference.

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