This article deals with the problem of joint detection and localization of multiple targets in a distributed multiple-input multiple-output radar system where the emitted waveforms present imperfect auto- and cross-correlation functions. In this context, the sidelobe masking effects can considerably degrade the detection and localization performance of correlation-based detectors. To address this issue, we first derive a convenient discrete-time signal model, wherein the echoes generated by a target towards each receive antenna are regarded as a subspace signal. Then, we formulate the joint detection and localization problem as a composite multiple hypothesis test and derive the optimal solution according to a generalized information criterion, whose complexity scales exponentially with the number of prospective targets. To reduce the computational burden, we derive two iterative approximate solutions which detect and localize one target at a time from the noisy data, upon mitigating the sidelobe masking caused by the previously-detected ones. Finally, we provide an extensive numerical analysis to demonstrate the effectiveness of the proposed solutions, even in the challenging case where an unknown number of strong and weak targets is present in the covered area.

Joint Detection and Localization in Distributed MIMO Radars Employing Waveforms With Imperfect Auto- and Cross-Correlation

Venturino L.;Grossi E.;
2023-01-01

Abstract

This article deals with the problem of joint detection and localization of multiple targets in a distributed multiple-input multiple-output radar system where the emitted waveforms present imperfect auto- and cross-correlation functions. In this context, the sidelobe masking effects can considerably degrade the detection and localization performance of correlation-based detectors. To address this issue, we first derive a convenient discrete-time signal model, wherein the echoes generated by a target towards each receive antenna are regarded as a subspace signal. Then, we formulate the joint detection and localization problem as a composite multiple hypothesis test and derive the optimal solution according to a generalized information criterion, whose complexity scales exponentially with the number of prospective targets. To reduce the computational burden, we derive two iterative approximate solutions which detect and localize one target at a time from the noisy data, upon mitigating the sidelobe masking caused by the previously-detected ones. Finally, we provide an extensive numerical analysis to demonstrate the effectiveness of the proposed solutions, even in the challenging case where an unknown number of strong and weak targets is present in the covered area.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/104285
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