Delay estimation of incoming signals in passive systems is still nowadays at the base of many signal processing applications ranging from passive radars to underwater acoustics, indoor acoustic positioning, and others. This paper aims at improving the estimation of the delays with respect to multiple sensing nodes for user localization for rescue operations under the unavailability of the base stations in the area of interest. To this end, it suitably exploits a method grounded on the computation of the cross-correlation between the cross-correlation estimates (say cross-cross-correlation) of the received signals. The estimation problem is formulated as a least squares (LS) optimization problem. As a consequence, the proposed method inherits an important feature of the LS approach, namely that is independent of the underlying data distributions. The performance assessment is conducted in comparison with its classic counterpart.

User Localization for Rescue Operations Exploiting the Cross-Cross-Correlations of Signals from Multiple Sensors

Pallotta, Luca;
2023-01-01

Abstract

Delay estimation of incoming signals in passive systems is still nowadays at the base of many signal processing applications ranging from passive radars to underwater acoustics, indoor acoustic positioning, and others. This paper aims at improving the estimation of the delays with respect to multiple sensing nodes for user localization for rescue operations under the unavailability of the base stations in the area of interest. To this end, it suitably exploits a method grounded on the computation of the cross-correlation between the cross-correlation estimates (say cross-cross-correlation) of the received signals. The estimation problem is formulated as a least squares (LS) optimization problem. As a consequence, the proposed method inherits an important feature of the LS approach, namely that is independent of the underlying data distributions. The performance assessment is conducted in comparison with its classic counterpart.
2023
979-8-3503-3884-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/170355
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