A procedure for measuring the coherence length of the sea texture

Conte, E.; Bisceglie, M. Di; Galdi, C.; Ricci, Giuseppe

doi:10.1109/19.650784

Cited by 54 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over such temporal scale, the clutter exhibits a local Gaussian behavior, namely the received data (under the SIRP assumption) can be deemed as a zero-mean complex circular Gaussian process with unknown variance. This observation paves the way to the design of an effective strategy to estimate T c [44]. Specifically, T c can be inferred from the available measurements by considering data sequences of increasing length (from both the quadrature components) and establishing whether the following hypothesis cannot be rejected:…”

Section: B Higher Order Statisticsmentioning

confidence: 99%

Compatibility Assessment of Multistatic/Polarimetric Clutter Data With the SIRP Model

Aubry

Carotenuto

Maio

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

This article deals with the statistical inference of simultaneously recorded co-and cross-polarized bistatic coherent sea-clutter returns at S-band. This study is conducted employing appropriate statistical learning tools, involving the complex envelope of data, to assess the compliance of the available measurements with the spherically invariant random process (SIRP) representation, as well as to analyze possible texture correlations among the diverse polarimetric channels. Moreover, the spatial heterogeneity of the sea-clutter data is studied. The results highlight that the SIRP model is a good candidate for the representation of bistatic coherent clutter and usually the coherence time of the SIRP texture at the bistatic nodes is longer than that in the monostatic sensing. Notably, at bistatic angles in order of 60 • , the quadrature components of the cross-polarized bistatic measurements substantially exhibit a Gaussian behavior. These achievements further shed light on the bistatic sea-clutter diversity from the geometric and polarimetric point of view.

show abstract

Section: B Higher Order Statisticsmentioning

confidence: 99%

Compatibility Assessment of Multistatic/Polarimetric Clutter Data With the SIRP Model

Aubry

Carotenuto

Maio

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

show abstract

“…Their probabilities density functions (pdfs) are, respectively, given by

f_{normalG} false(τ_{italicrk} false) = \frac{ϱ_{r}^{θ_{r}}}{normalΓ false(θ_{r} false)} τ_{italicrk}^{θ_{r} - 1} \exp (- ϱ_{r} τ_{rk}), τ_{rk} > 0,

f_{IG} false(τ_{italicrk} false) = \frac{β_{r}^{ν_{r}}}{normalΓ false(ν_{r} false)} τ_{italicrk}^{- (ν_{r} + 1)} \exp (- β_{r} / τ_{rk}), τ_{rk} > 0

where Γ( · ) is the Eulerian gamma function, θ r and ν r are the shape parameters, ϱ r and β r are the scale parameters. The choice of a Gamma distribution for texture is widely accepted and verified [5, 6]. The compound‐Gaussian model with inverse Gamma texture is suitable for sea‐clutter validated by real data [31, 32].…”

Section: Problem Formulationmentioning

confidence: 99%

“…The adaptive detection with MIMO radar has been widely studied for the case of Gaussian clutter [3, 4]. However, experimental data have shown that the Gaussian assumption is not always valid for the real clutter environment [5–8]. In fact, it is more suitable to describe the clutter as a compound‐Gaussian model [9–11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Bayesian detection for multiple‐input multiple‐output radar in compound‐Gaussian clutter with random texture

Kong

Cui

et al. 2016

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…[5,6]. It turns out that a compound-Gaussian model has been found to be the best fit for sea clutter, especially at a low-grazing angle and high-resolution radar [7].…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Covariance Matrix Estimation and Its Application to Whitening Filter

Choi

Yang²,

Song³

et al. 2021

J Electromagn Eng Sci

View full text Add to dashboard Cite

The accurate estimation of clutter covariance matrix (CCM) is essential in designing a radar detector/filter to suppress sea clutter. This estimation might not be easily accomplished because of the scarcity of valid training vectors adjacent to the range cell under test (CUT). We propose a new CCM estimation algorithm that is derived by modeling time-series clutter returns into a clutter Doppler spectrum in the frequency domain and exploiting mutual independence among spectral components. To justify its excellence over the conventional sample covariance matrix (SCM) algorithm, we design two filters—a maximum signal-to-interference-plus-noise ratio (SINR)-based filter and a whitening filter—that use the estimated CCMs and compare their performance in a numerically simulated sea clutter scenario. Comparisons are made by showing the eigenvector spectra of the estimated CCMs and the frequency responses and outputs of the filters. Moreover, SINRs at the target Doppler bin are examined and compared with a theoretical, analytically derived SINR.

show abstract

A procedure for measuring the coherence length of the sea texture

Cited by 54 publications

References 10 publications

Compatibility Assessment of Multistatic/Polarimetric Clutter Data With the SIRP Model

Compatibility Assessment of Multistatic/Polarimetric Clutter Data With the SIRP Model

Adaptive Bayesian detection for multiple‐input multiple‐output radar in compound‐Gaussian clutter with random texture

Sea Clutter Covariance Matrix Estimation and Its Application to Whitening Filter

Contact Info

Product

Resources

About