Accepted 2025 March 31 ; Published 2025 April 1

Journal of Industrial Engineering and Applied Science, 2025, 3(2), 3005-6071.

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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.

Weather Radar Signal Processing , Low-complexity Angle Estimation , Modified ESPRIT Algorithm , Joint Parameter Estimation .

APA Style

Chen, C., Zhang, Z. & Lian, H. (2025). A low-complexity joint angle estimation algorithm for weather radar echo signals based on modified esprit. Journal of Industrial Engineering and Applied Science, 3(2), 33-43. https://doi.org/10.70393/6a69656173.323832

I would like to extend my sincere gratitude to Chaoyue Jiang, Hanqing Zhang, and Yue Xi for their groundbreaking research on game localization quality assessment using deep learning techniques as published in their article titled "Automated Game Localization Quality Assessment Using Deep Learning: A Case Study in Error Pattern Recognition"[30]. Their insights and methodologies in error pattern recognition have significantly influenced my understanding of deep learning applications and have provided valuable inspiration for my research in weather radar signal processing. I would also like to express my heartfelt appreciation to Enmiao Feng, Yizhe Chen, and Zhipeng Ling for their innovative study on secure resource allocation optimization using deep reinforcement learning, as published in their article titled "Secure Resource Allocation Optimization in Cloud Computing Using Deep Reinforcement Learning"[31]. Their comprehensive analysis and optimization approaches have significantly enhanced my understanding of computational efficiency and inspired the development of low-complexity algorithms in my research.

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