Evaluation of partially adaptive STAP algorithms on the Mountain Top data set

Seliktar, Y.; Williams, D.B.; McClellan, J.H.

doi:10.1109/icassp.1996.543573

Cited by 11 publications

(3 citation statements)

References 2 publications

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“…Parameters of Mountain Top data used for simulations in this paper are shown in Table 1. For this set of Mountain Top data [30], there are 12 CPI datacubes and each datacube has 403 range bins. Since the radar transmitter moves horizontally along the IDPCA array axis at a rate of 12.2 inches per pulse repetition interval, T (0.0016 s), this is equivalent to a radar system mounted on a moving platform with a velocity vp = 193.675 m/s.…”

Section: Description Of Mountain Top Datamentioning

confidence: 99%

Performance Analysis of the NAMF Test in Heterogeneous Non-Gaussian Radar Clutter Scenarios

Rangaswamy

Lin

2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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An important issue in radar space-time adaptive processing (STAP) is the selection of training data for estimation of the unknown disturbance covariance matrix. In this paper, we address the training data selection issue and analyze the performance of normalized adaptive matched filter (NAMF) in heterogeneous non-Gaussian radar clutter scenarios. Simulations of the probability of detection (PD) versus signal-tointerference-plus-noise ratio (SINR) for the NAMF are conducted using Mountain Top data, which exhibit non-Gaussian statistics. To select the training data representative of the interference, we apply adaptive beamforming and show that PD versus SINR for NAMF is robust with respect to a wide range of variation in the beamformer output. To mitigate heterogeneous clutter, the self-censoring reiterative fast maximum likelihood (SCRFMIL) algorithm is employed to regularize the eigen spectrum underlying the unknown disturbance covariance matrix. We demonstrate that the NAMF detection performance can be significantly improved with the application of SCRFML.

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Section: Description Of Mountain Top Datamentioning

confidence: 99%

Performance Analysis of the NAMF Test in Heterogeneous Non-Gaussian Radar Clutter Scenarios

Rangaswamy

Lin

2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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“…(10) and (11) have been adapted from Seliktar et al [5] to accommodate a spatiotemporal steering matrix and a corresponding weight matrix rather than vectors. This weight matrix W is subsequently used to obtain the filter output, H z W .…”

Section: Element Space Partially Adaptive Stapmentioning

confidence: 99%

Partially Adaptive Stap for Fmri: A Method for Detecting Brain Activation Regions in Simulation and Human Data

Huang

Thompsonm

Holland

et al. 2007

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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This paper introduces three partially adaptive space-time processing (STAP) schemes for analyzing fMRI data. Element space partially adaptive STAP can achieve performance close to that of fully adaptive STAP while greatly decreasing the CPU running time and memory requirements when applied to both synthetic as well as real human brain data. In synthetic analyses, partially adaptive STAP algorithms exhibit better detection characteristics than the traditional cross-correlation method. This is supported by human data in which element space and fully adaptive STAP produce activation maps that closely resemble those of cross-correlation.INDEX TERMS: fMRI, statistical signal processing, partially adaptive STAP

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“…Then we combine these reduced weights W 0 , W 1 ... W N '–1 within the post-processor unit to form a single weight matrix W via

W = \sum_{p = 0}^{N - 1} (J_{p} \otimes I_{M}) {boldW}_{p} (d i a g ({W_{p}}^{H} {boldV}_{p}))

where diag returns a diagonal matrix. Equation (8) has been adapted from Seliktar et al [10] to accommodate a spatiotemporal steering matrix and a corresponding weight matrix rather than vectors. This weight matrix W is subsequently used to obtain the filter output,

\vec{normalz} = {boldW}^{H} \vec{bold χ} .

Here,

\overset{χ}{true}

is the original column-wise reshaped activated data signal of full dimensionality as defined in [2].…”

Section: Theorymentioning

confidence: 99%

Partially Adaptive STAP Algorithm Approaches to Functional MRI

Huang

Thompson

Schmithorst

et al. 2009

IEEE Trans. Biomed. Eng.

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In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis.

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Evaluation of partially adaptive STAP algorithms on the Mountain Top data set

Cited by 11 publications

References 2 publications

Performance Analysis of the NAMF Test in Heterogeneous Non-Gaussian Radar Clutter Scenarios

Performance Analysis of the NAMF Test in Heterogeneous Non-Gaussian Radar Clutter Scenarios

Partially Adaptive Stap for Fmri: A Method for Detecting Brain Activation Regions in Simulation and Human Data

Partially Adaptive STAP Algorithm Approaches to Functional MRI

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