Parameter bounds under misspecified models for adaptive radar detection

Fortunati, Stefano; Gini, Fulvio; Greco, Maria

doi:10.1016/b978-0-12-811887-0.00004-3

Cited by 9 publications

(10 citation statements)

References 53 publications

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“…the known location of the mth RIS element for 1 ≤ m ≤ M , and p = [x y z] T is the unknown UE location. For convenience, following the notation given in [42], [44], we use (•) for the true values of the parameters of interest throughout the manuscript.…”

Section: A Geometry and Signal Modelmentioning

confidence: 99%

“…In this scenario, we aim to quantify the localization performance loss due to the model mismatch resulting from the phase-dependent amplitude variations specified in (3). To that end, we will resort to the MCRB analysis [42], [44]- [46]. In the following, we first describe the true model, which corresponds to the realistic RIS response model in (3), and the assumed model, which is the ideal unit-amplitude RIS model commonly employed in the literature.…”

Section: Scenario-i: Misspecified Cram éR-rao Bound (Mcrb) Analysismentioning

confidence: 99%

“…Next, let η(y) be a misspecified-unbiased (MS-unbiased) estimator of η, i.e., the mean of the estimator η(y) under the true model is equal to η 0 . The MCRB is a lower bound for the covariance matrix of any MS-unbiased estimator of η, η(y) [42]- [44]:…”

Section: B Mcrb Definitionmentioning

confidence: 99%

“…In this scenario, the unknown system parameters are given by η = [Re(α) Im(α) p T ] T and the values of the RIS related parameters are perfectly known. For this scenario, the FIM, J (η) ∈ R 5×5 , can be computed as in (44). In addition, the derivatives presented in Appendix B can be used by replacing wt,m 's with w t,m 's.…”

Section: Scenario-iii: Known Ris Amplitude Model With Known Parametersmentioning

confidence: 99%

See 3 more Smart Citations

RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variations

Ozturk¹,

Keskin²,

Wymeersch³

et al. 2022

Preprint

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We investigate the problem of reconfigurable intelligent surface (RIS)-aided near-field localization of a user equipment (UE) served by a base station (BS) under phasedependent amplitude variations at each RIS element. Through a misspecified Cramér-Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér-Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.

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Section: A Geometry and Signal Modelmentioning

confidence: 99%

Section: Scenario-i: Misspecified Cram éR-rao Bound (Mcrb) Analysismentioning

confidence: 99%

Section: B Mcrb Definitionmentioning

confidence: 99%

Section: Scenario-iii: Known Ris Amplitude Model With Known Parametersmentioning

confidence: 99%

See 2 more Smart Citations

RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variations

Ozturk¹,

Keskin²,

Wymeersch³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…22 is a non-convex function, it does not represent a proper Allan deviation, and hence the wrong noise model has been used. The consistency between the corrected Allan deviation and the bathtub diagram could be used to design a model mismatch detector, as it is done in several fields, such as, for instance, radar detection [17], machine learning [18], and chemical engineering [19].…”

Section: Choosing Wrong Noise Componentsmentioning

confidence: 99%

The Corrected Allan Variance: Stability Analysis of Frequency Measurements With Missing Data

Galleani

Sesia

2019

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Atomic clocks are essential elements in a variety of applications, such as global navigation satellite systems. Consequently, monitoring their performances is fundamental. The Allan variance is the key statistical tool for the performance characterization of atomic clocks. This article proves that the Allan variance computed from frequency measurements with missing data is affected by a bias which can make it dramatically different from the expected behavior in the full data case. Furthermore, it shows how to eliminate (or largely reduce) this bias by correcting the Allan variance. The corrected Allan variance is obtained for some of the most common atomic clock noise components, and it is validated through numerical simulations.

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On the efficiency of misspecified Gaussian inference in nonlinear regression: Application to time-delay and Doppler estimation

Fortunati,

Ortega

2024

Signal Processing

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Parameter bounds under misspecified models for adaptive radar detection

Cited by 9 publications

References 53 publications

RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variations

RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variations

The Corrected Allan Variance: Stability Analysis of Frequency Measurements With Missing Data

On the efficiency of misspecified Gaussian inference in nonlinear regression: Application to time-delay and Doppler estimation

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