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An Improved Quantile Estimator With Its Application in CFAR Detection


Abstract:

We construct an improved and distribution-free quantile estimator (called IB quantile (IBQ) estimator) with representation as a linear combination of order statistics (OS...Show More

Abstract:

We construct an improved and distribution-free quantile estimator (called IB quantile (IBQ) estimator) with representation as a linear combination of order statistics (OSs). The IBQ estimator can estimate quantiles of any scale-invariant population by using censored samples. Taking exponential population as an example, we derive the mean square error (mse) of the IBQ estimator under a small sample case. It is found that the IBQ estimator exhibits higher estimation efficiency when estimating large quantiles compared with the other two existing estimators. Applying the IBQ estimator to create the clutter level, we establish the IBQ constant false alarm rate (CFAR) processor. We investigate the performance of IBQ-CFAR in homogeneous background and multiple target scenarios. The theoretical analyses show that the IBQ-CFAR exhibits outstanding detection performance and false alarm control ability in multiple target scenarios.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 3507705
Date of Publication: 30 August 2023

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