Neural network based direction of arrival estimation for a MIMO OFDM radar

In this paper, the usage of artificial neural networks (ANN) for the estimation of the direction-of-arrival (DOA) in an OFDM-based MIMO configuration radar is explored. For the extension of its range-Doppler estimation functionality, a third dimension of estimation, namely the position of objects in the azimuth plane is considered. Popular subspace-based DOA methods such as MUSIC have been explored, however they required a large processing effort. This added to the latency of the radar processing and thus is deemed to be sub-optimal for real time target localization applications. This paper presents a simulation-based investigation of using ANN for DOA estimation. The results showed that the ANN based algorithm requires less processing time and outperforms the MUSIC algorithm in terms of object separability at the separation angle of less than 5°.