Along Track Interferometric Synthetic Aperture Radar (AT-InSAR) systems use more than one SAR antennas (typically two), mounted on the same platform and displaced along the platform moving direction, to detect slow ground moving targets. The phase of the ATI signal is related to the target motion parameters and may thus be used to estimate the radial velocity. In this paper we approach the velocity estimation problem using statistical techniques based on the statistical distribution of the measured interferometric phases. We analyze the radial velocity estimation with respect to ATI system parameters, such as velocity values, the signal to clutter ratio (SCR), the clutter to noise ratio (CNR), considering a deterministic target whose velocity is estimated using a Gaussian model. This model allows to take into account the lack of knowledge of the target radar cross section (RCS) values and provides an analytical form for the interferometric phase probability density function. Simulations results show that the adoption of Maximum Likelihood (ML) techniques, to perform a joint estimation of velocity and SCR, and multi-channel configurations, to overcome ambiguities problems, provide very good velocity estimation accuracy.

Velocity estimation of slow moving targets in AT-InSAR systems

BUDILLON, Alessandra;PASCAZIO, Vito;SCHIRINZI, Gilda
2007

Abstract

Along Track Interferometric Synthetic Aperture Radar (AT-InSAR) systems use more than one SAR antennas (typically two), mounted on the same platform and displaced along the platform moving direction, to detect slow ground moving targets. The phase of the ATI signal is related to the target motion parameters and may thus be used to estimate the radial velocity. In this paper we approach the velocity estimation problem using statistical techniques based on the statistical distribution of the measured interferometric phases. We analyze the radial velocity estimation with respect to ATI system parameters, such as velocity values, the signal to clutter ratio (SCR), the clutter to noise ratio (CNR), considering a deterministic target whose velocity is estimated using a Gaussian model. This model allows to take into account the lack of knowledge of the target radar cross section (RCS) values and provides an analytical form for the interferometric phase probability density function. Simulations results show that the adoption of Maximum Likelihood (ML) techniques, to perform a joint estimation of velocity and SCR, and multi-channel configurations, to overcome ambiguities problems, provide very good velocity estimation accuracy.
9780819469045
9780819469045
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/56959
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