A Support Vector Regression-Based Method for Target Direction of Arrival Estimation From HF Radar Data
High-frequency (HF) radars have great potential for maritime surveillance, and the multiple signal classification (MUSIC) algorithm is usually used to estimate the direction of arrival (DOA) of targets for a wide-beam radar. However, the performance of the MUSIC algorithm relies on the precision of the antenna pattern, which could be contaminated by nearby electromagnetic interference. Therefore, the actual antenna pattern must be measured and used. In order to remove the requirement of antenna pattern measurement, a new method for target DOA estimation from wide-beam HF radar data using support vector regression (SVR) is proposed in this letter. A system model that relates target bearing and radar data feature is obtained through the SVR-based machine learning using the automatic identification system data and data associated with the vessels successfully detected by the HF radar. Then, such a model is used to determine the DOAs of targets from new data. The field experimental results at two sites demonstrate that the performance of the SVR method is better than that of the MUSIC algorithm.