Method for Noise Subspace Determination in HFSWR’s High-Resolution Range-Doppler Map Estimation

Accurate and efficient estimation of the boundary between the signal and the noise subspace is a critical task in the estimation of high-resolution range-Doppler (RD-HR) maps in High Frequency Surface Wave Radar (HFSWR). In this paper, we present a method for noise subspace determination in order to have better performance in terms of target detection probability. The method is based on the eigenvalue decomposition of the covariance matrix of radar data. Conventional algorithms such as Akaike’s Information Criterion (AIC) and Minimum Description Length (MDL) do not function properly because the max-to-min-eigenvalue ratio can be between 1010 and 1019. We compared the proposed method with the Principal Component Analysis (PCA) method, which is often used to reduce dimensionality of the data, while keeping the maximum amount of information. We also made the comparison with empirically obtained results. The paper contains the description of the proposed algorithm, the performance analysis and optimal setting of the algorithm parameters. The results of calculations performed on real HFSWR data show an improvement in the detectability and shorter execution time of the program for RD-HR map estimation, which is of great importance in order to achieve real-time processing.