Low-angle target tracking using frequency-agile refined maximum likelihood algorithm

An Low-angle target tracking problem is investigated via the refined maximum likelihood (RML) algorithm. The results of the RML algorithm reveal that increasing the operating frequency of radar does not always reduce the mean-squared error (MSE) of angle estimate and thus an appropriate selection of the operating frequency can improve the angle estimation accuracy. Here, a frequency-agile RML algorithm is proposed, which adaptively adjusts the operating frequency during target tracking to minimize the MSEs of angle estimate. Theoretical analysis and simulation are made to verify the effectiveness of the frequency-agile RML algorithm.