We deal with the problem of intrapulse radar-embedded communication and propose a novel waveform design procedure based on a multiobjective optimization paradigm. More specifically, under both energy and similarity constraints, we devise signals according to the following criterion: constrained maximization of the signal-to-interference ratio and constrained minimization of a suitable correlation index (which is related to the possibility of waveform interception). This is tantamount to jointly maximizing two competing quadratic forms under two quadratic constraints so that the problem can be formulated in terms of a nonconvex multiobjective optimization. In order to solve it, we resort to the scalarization technique, which reduces the vectorial problem into a scalar one using Pareto weights defining the relative importance of the two objectives. At the analysis stage, we assess the performance of the proposed waveform design scheme in terms of symbol error rate and the so-called intercept metric.
Intrapulse radar-embedded communications via multiobjective optimization / Ciuonzo, Domenico; De Maio, Antonio; Foglia, Goffredo; Piezzo, Marco. - In: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. - ISSN 0018-9251. - 51:4(2015), pp. 2960-2974. [10.1109/TAES.2015.140821]
Intrapulse radar-embedded communications via multiobjective optimization
Ciuonzo, Domenico;De Maio, Antonio;Piezzo, Marco
2015
Abstract
We deal with the problem of intrapulse radar-embedded communication and propose a novel waveform design procedure based on a multiobjective optimization paradigm. More specifically, under both energy and similarity constraints, we devise signals according to the following criterion: constrained maximization of the signal-to-interference ratio and constrained minimization of a suitable correlation index (which is related to the possibility of waveform interception). This is tantamount to jointly maximizing two competing quadratic forms under two quadratic constraints so that the problem can be formulated in terms of a nonconvex multiobjective optimization. In order to solve it, we resort to the scalarization technique, which reduces the vectorial problem into a scalar one using Pareto weights defining the relative importance of the two objectives. At the analysis stage, we assess the performance of the proposed waveform design scheme in terms of symbol error rate and the so-called intercept metric.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.