A Low-Complexity Joint Angle Estimation Algorithm for Weather Radar Echo Signals Based on Modified ESPRIT

A low-complexity joint angle estimation algorithm based on a modified ESPRIT technique is proposed for weather radar echo signals. The algorithm employs a novel dimension reduction approach combined with optimized subspace estimation to reduce computational complexity while maintaining estimation accuracy. The proposed method achieves efficient implementation through a truncated convolution operation that preserves the essential angle information of weather signals. A new signal subspace construction technique is developed to handle the non-stationary characteristics typical in weather radar applications. The algorithm incorporates an adaptive thresholding mechanism and parallel processing structures to optimize computational resource utilization. Theoretical analysis establishes performance bounds and validates the algorithm's computational advantages. The Cramér-Rao Lower Bound (CRLB) for the modified algorithm demonstrates theoretical optimality under specified conditions. Extensive simulation results indicate that the proposed method achieves a 65-75% reduction in processing time and 55-65% improvement in memory efficiency compared to traditional implementations. The algorithm maintains robust performance with Root Mean Square Error (RMSE) of 1.2° at medium SNR (5dB) conditions while exhibiting superior stability under array imperfections and signal perturbations. The practical applicability of the algorithm is verified through comprehensive evaluation using simulated weather radar data, demonstrating its effectiveness for real-time weather signal processing applications.