New Results on Fractional QCQP with Applications to Radar Steering Direction Estimation

This letter considers constrained steering direction estimation in the presence of additive Gaussian disturbance. The uncertainty region is modeled through double-sided quadratic constraints (up to three) and the Maximum Likelihood (ML) criterion is adopted to get the direction estimator. It is shown that the considered formulation leads to a fractional Quadratically Constrained Quadratic Program (QCQP) whose solution can be computed in polynomial time via semidefinite programming relaxation, Charnes-Cooper transformation, and suitable rank-one decomposition tools. At the analysis stage, with reference to a specific constraint set, the performance of the devised estimator is compared with the constrained Cramer Rao lower Bound (CRB).