Modified PUMA/EPUMA Based on Forward and Backward Linear Prediction for DOA Estimation

Abstract

The principal-singular-vector utilization for modal analysis (PUMA) and its modification (Mod-PUMA), which utilize forward linear prediction (FLP) to process the signal subspace, experience significant performance degradation if there are multiple coherent sources and such a performance degradation will be further aggravated in low-SNR regions, which is primarily attributed to the outliers arising from inaccurate estimations of the signal subspace. To address these issues, we propose an extension version of PUMA-related algorithms, called FBLP-Mod-PUMA/enhanced-PUMA (EPUMA). The proposed algorithms improve the threshold performance by refining the signal subspace through forward and backward linear prediction (FBLP), effectively mitigating subspace leakage when dealing with coherent sources. The number of resolvable coherent sources has been theoretically derived and simulation results are provided to show the performance of the proposed algorithms.