Intrapulse radar-embedded communications via multiobjective optimization

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.

[1]  Yonina C. Eldar,et al.  Convex Optimization in Signal Processing and Communications , 2009 .

[2]  Morio Onoe,et al.  Radar reflectors with controllable reflection , 1980 .

[3]  Joseph R. Guerci,et al.  Signal Waveform's Optimal Under Restriction Design for Active Sensing , 2006, SAM 2006.

[4]  A. Goldsmith Communication by Means of Reflected Power , 2022 .

[5]  S D Blunt,et al.  Intrapulse Radar-Embedded Communications , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Victor S. Frost,et al.  Exploiting OFDM systems for covert communication , 2010, 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE.

[7]  B. Picinbono On deflection as a performance criterion in detection , 1995 .

[8]  Daniel M. Dobkin,et al.  The RF in RFID: Passive UHF RFID in Practice , 2007 .

[9]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[10]  A. De Maio,et al.  Pareto-theory for enabling covert intrapulse radar-embedded communications , 2015, 2015 IEEE Radar Conference (RadarCon).

[11]  Shuzhong Zhang,et al.  New results on Hermitian matrix rank-one decomposition , 2011, Math. Program..

[12]  Daniel W. Bliss,et al.  Cooperative radar and communications signaling: The estimation and information theory odd couple , 2014, 2014 IEEE Radar Conference.

[13]  Antonio De Maio,et al.  Design of Pareto-Optimal Radar Receive Filters , 2011 .

[14]  Daniel Pérez Palomar,et al.  Fractional QCQP With Applications in ML Steering Direction Estimation for Radar Detection , 2011, IEEE Transactions on Signal Processing.

[15]  Arkadi Nemirovski,et al.  Lectures on modern convex optimization - analysis, algorithms, and engineering applications , 2001, MPS-SIAM series on optimization.

[16]  David Hounam,et al.  A technique for the identification and localization of SAR targets using encoding transponders , 2001, IEEE Trans. Geosci. Remote. Sens..

[17]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[18]  A. Farina,et al.  Pareto-optimal radar waveform design , 2010, 2010 International Waveform Diversity and Design Conference.

[19]  Gongguo Tang,et al.  Multiobjective Optimization of OFDM Radar Waveform for Target Detection , 2011, IEEE Transactions on Signal Processing.

[20]  M. C. Wicks,et al.  On the spectrum sharing between radar and communication systems , 2014, 2014 International Conference on Electromagnetics in Advanced Applications (ICEAA).

[21]  Shannon D. Blunt,et al.  Performance Characteristics and Metrics for Intra-Pulse Radar-Embedded Communication , 2011, IEEE Journal on Selected Areas in Communications.

[22]  J. Capon High-resolution frequency-wavenumber spectrum analysis , 1969 .