Relative Entropy-Based Waveform Optimization for Rician Target Detection With Dual-Function Radar Communication Systems

In this article, we consider waveform design for dual-function radar communication (DFRC) systems based on multiple-input–multiple-out (MIMO) arrays. To achieve better Rician target detection performance, we use the relative entropy associated with the formulated detection problem as the design metric. We also impose a multiuser interference (MUI) energy constraint on the waveforms to ensure the achievable sum rate of the communications. Two algorithms are presented to tackle the nonlinear nonconvex waveform design problem. In the first algorithm, we derive a quadratic function to minorize the objective function. To tackle the quadratically constrained quadratic programming (QCQP) problem at each iteration, a semidefinite relaxation (SDR) approach followed by a rank-one decomposition procedure and an efficient alternating direction method of multipliers (ADMM) are proposed, respectively. In the second algorithm, we present a novel ADMM algorithm to tackle the optimization problem and employ an efficient minorization-maximization (MM) approach in the inner loop of the ADMM algorithm. Numerical results demonstrate the superiority of both algorithms. Moreover, the presented algorithms can be extended to synthesize peak-to-average power ratio (PAPR) constrained waveforms, which allows the radio frequency amplifier to operate at increased efficiency.