Performance Analysis of Joint Range-Velocity Estimator With 2D-MUSIC in OFDM Radar

To achieve high resolution, orthogonal frequency division multiplex (OFDM) radars deploy two-dimensional multiple signal classification (2D-MUSIC) in the joint range-velocity estimator. However, it is obvious that both of the signal reconstruction and 2D smoothing affect the noise statistical distribution and virtual array aperture in the joint range-velocity estimator with 2D-MUSIC. Therefore, the conventional accuracy analysis methods for the MUSIC are no longer suitable. In this paper, we propose an estimation accuracy analysis for the joint range-velocity estimator with 2D-MUSIC. Firstly, we present that the noise statistical distribution of the signals being reconstructed and 2D smoothed has a 2-fold Hankel structure. Then, the closed-forms of the range and velocity estimation accuracies are derived, respectively, which both show that the estimation accuracies highly depend on the 2D smoothing and could be improved by properly choosing the 2D smoothing window. Further, we formulate the smoothing optimization and propose the quasi-optimal size of the 2D smoothing window. Finally, both theoretical analyses and simulations validate that the proposed smoothing optimization could significantly improve the estimation accuracies with a lower computational burden than the conventional smoothing.