Ambiguity function shaping for cognitive MCPC radar

Abstract

The ambiguity function (AF) plays a key role in radar systems to measure their detection ability. Cognitive radars provide feedback information of interference, and therefore create the possibility of reshaping AF of radars ideally. In this paper, an AF shaping method for a cognitive multicarrier phase coded (MCPC) radar, which adjusts the transmitted MCPC signal according to predicted information, is presented. The optimality criterion is maximizing the signal to interference and noise ratio under an energy constraint, which is an NP-hard problem due to its complex quartic and nonconvex constraint. The model is firstly simplified according to the signal characteristics by a process of deduction. Then, the objective function is optimized through a majorization minimization algorithm. Finally, numerical results show a level reduction of the undesired range-Doppler bins and autocorrelation sidelobes. The designed signal improves the radar's probing and adapting to the environment.