Tracking of highly maneuvering targets is an area of considerable interest to the Radar community. Conventionally, tracking is performed using some filtering based some maneuvering model with the measurements of range and bearing in data processing. Due to the absence of acceleration, worse precision or divergence of tracking will occur when the current radars track high maneuvering targets. It can be improved the performance of maneuvering tracking if radial acceleration and radial velocity estimates can be brought into the measurement vector in maneuvering tracking. Therefore, a RAV-EKFD method is proposed for enhancing the tracking of a highly maneuvering target. In the proposed method, the radial acceleration and radial velocity is derived based on Compressive Sensing method in signal processing and then applied to the measurement vector after coordinates transform. In the filtering approach, a method of EKFD is used based on Debiased Converted Measurements Kalman Filter (CMKF-D) and Extended Kalman Filter(EKF) to resolve the problem of the non-linearity of the measurement equation. In simulations, the tracking performance of the proposed method is compared with the traditional EKF and CMKF-D algorithms, and the results show that the RAV-EKFD outperforms these algorithms in maneuvering scenario. (7 pages)