Abstract
It is challenging to estimate the elevation angle of low-altitude targets due to the multipath effect. Various signal processing techniques have been proposed to mitigate these effects, including the use of multi-frequency signals as opposed to single narrowband signals. However, the optimal type of multi-frequency signals and their effective utilization have not been thoroughly explored. Compressive sensing was also proposed as a high-resolution angle estimation method. But, that was conducted with narrowband signals. In this paper, we employ MIMO-OFDM signals along with a block sparse Bayesian learning fast marginalized (BSBL-FM) method. This combination allows for the effective processing of multi-frequency signals and provides high resolution estimates. The MIMO-OFDM approach represents radar signals in a block-sparse matrix form, and the BSBL-FM method leverages this sparsity to achieve high-resolution angle estimates. Simulation results demonstrate that our method can accurately estimate angles at extremely low altitudes where the elevation angle is less than 1 degree.
Published Version
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