Altitude measurement of low-angle target in complex terrain for very high-frequency radar

For low-angle targets, the performance of altitude measurement is affected by multipath phenomenon. Especially, for complex terrain case, the main problem is due to the fact that the multipath signal is perturbed by irregular reflector, which leads to the mismatch of the steering vector, and thus degrades the performance of or even fails the existing methods. To deal with this problem, the authors propose a new perturbational multipath signal model, where perturbation caused by complex terrain is considered as the gain and phase errors of the steering vector of the multipath signal. With the spatial sparsity of the incident signals, the sparse Bayesian learning technique is adopted to estimate the perturbation and direction of arrival (DOA) iteratively. The computer simulation results show that their algorithm is able to estimate the effect of perturbation with high precision and to enhance the DOA accuracy compared with existing algorithms. Furthermore, real data analysis validates the efficiency of altitude measurement in practice. Finally, the proposed perturbational multipath signal model is also applicable to other situations where complex multipath phenomenon is not negligible.