Estimation Method of Target Propeller Parameters under Low Signal-to-noise Ratio

HAN Chuang; LENG Bing; LAN Chaofeng; XING Bowen

doi:10.11999/JEIT240790

Volume 47 Issue 7

Jul. 2025

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HAN Chuang, LENG Bing, LAN Chaofeng, XING Bowen. Estimation Method of Target Propeller Parameters under Low Signal-to-noise Ratio[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2149-2162. doi: 10.11999/JEIT240790

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HAN Chuang, LENG Bing, LAN Chaofeng, XING Bowen. Estimation Method of Target Propeller Parameters under Low Signal-to-noise Ratio[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2149-2162. doi: 10.11999/JEIT240790

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Estimation Method of Target Propeller Parameters under Low Signal-to-noise Ratio

doi: 10.11999/JEIT240790 cstr: 32379.14.JEIT240790

1.
School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2.
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China

Funds: The National Natural Science Foundation of China (11804068), The Natural Science Foundation of Heilongjiang Province (LH2020F033), Heilongjiang Provincial Undergraduate Colleges and Universities Outstanding Young Teachers Basic Research Support Program (YQJH2024077)

Received Date: 2024-09-12
Rev Recd Date: 2025-06-06

Available Online: 2025-06-24

Publish Date: 2025-07-22

Abstract

Abstract

Objective Accurate estimation of underwater propeller parameters—such as blade number, blade length, and rotational speed—is critical for target identification in marine environments. However, low Signal-to-Noise Ratio (SNR) conditions, caused by complex underwater clutter and ambient noise, substantially degrade the performance of conventional micro-Doppler feature extraction methods. Existing approaches, including Fourier Transform (FT), wavelet analysis, and Hilbert-Huang Transform (HHT), are limited in handling non-stationary signals and are highly susceptible to noise, leading to unreliable parameter estimation. To address these limitations, this study proposes a method that integrates Complex Variational Mode Decomposition (CVMD) for signal denoising with Orthogonal Matching Pursuit (OMP) for sparse parameter estimation. The combined approach improves robustness against noise while maintaining computational efficiency. This method contributes to advancing underwater acoustic target recognition in interference-rich environments and offers a theoretical basis for improving the reliability of marine detection systems. Methods The proposed method integrates CVMD and OMP to improve the estimation of propeller parameters in low-SNR environments. The approach consists of three sequential phases: signal decomposition and denoising, time-frequency feature extraction, and sparse parameter estimation. This structure enhances robustness to noise while maintaining computational efficiency. CVMD extends conventional Variational Mode Decomposition (VMD) to the complex domain, enabling adaptive decomposition of propeller echo signals into Intrinsic Mode Functions (IMFs) with preserved spectral symmetry. Unlike standard VMD, which cannot process complex-valued signals directly, CVMD treats the real and imaginary parts of the noisy signal separately. The decomposition is formulated as a constrained optimization problem, where IMFs are iteratively extracted by minimizing the total bandwidth of all modes. A correlation-based thresholding scheme is then used to identify and discard noise-dominated IMFs. The remaining signal-related IMFs are reconstructed to obtain a denoised signal, effectively isolating micro-Doppler features from background clutter. Time-frequency analysis is subsequently applied to the denoised signal to extract key scintillation parameters, including blade parity, scintillation intervals, and the maximum instantaneous micro-Doppler frequency. These parameters are used as prior information to constrain the parameter search space and reduce computational burden. Blade parity, inferred from the symmetry of the time-frequency distribution, narrows the candidate blade number range by half. Scintillation intervals and frequency bounds are also used to define physical constraints for sparse estimation. A sparse dictionary is constructed using Sinusoidal Frequency-Modulated (SFM) atoms, each corresponding to a candidate blade number. The OMP algorithm iteratively selects the atom most correlated with the residual signal, updates the sparse coefficient vector, and refines the residual until convergence. Incorporating prior information into dictionary design significantly reduces its dimensionality, transforming a multi-parameter estimation problem into an efficient single-parameter search. This step allows precise estimation of the blade number with minimal computational cost. Once the blade number is determined, the blade length and rotational speed are derived analytically using the relationships between the micro-Doppler frequency, scintillation period, and geometric parameters of the propeller. Results and Discussions The proposed CVMD-OMP framework demonstrates robust performance in propeller parameter estimation under low-SNR conditions, as verified through comprehensive simulations. The denoising efficacy of CVMD is illustrated by the reconstruction of distinct time-frequency features from heavily noise-corrupted propeller echoes (Fig. 10). By decomposing the complex-valued signal into IMFs and retaining only signal-dominant components, CVMD achieves a 12.4 dB improvement in SNR and reduces the Mean Square Error (MSE) to 0.009 at SNR = –10 dB, outperforming conventional methods such as EMD-WT and CEEMDAN-WT (Table 3). Time-frequency analysis of the denoised signal reveals clear periodic scintillation patterns (Fig. 11), which enable accurate extraction of blade parity and scintillation intervals. Guided by these prior features, the OMP algorithm achieves 91.9% accuracy in blade number estimation at SNR = –10 dB (Table 4). Accuracy improves progressively with increasing SNR, reaching 98% at SNR = 10 dB, highlighting the method’s adaptability to varying noise levels. The sparse dictionary, refined through prior-informed dimensionality reduction, maintains high precision while minimizing computational complexity. Comparative evaluations confirm that OMP outperforms CoSaMP and Subspace Pursuit (SP) in both estimation accuracy and computational efficiency. The execution time is reduced to 1.73 ms for single-parameter estimation (Fig. 15, Table 5). Parameter estimation consistency is further validated through the calculation of blade length and rotational speed. At SNR = –10 dB, the Mean Absolute Error (MAE) for blade length is 0.021 m, and 0.31 rad/s for rotational speed (Table 6). Both errors decrease significantly with improved SNR, demonstrating the method’s robustness across diverse noise conditions. The framework remains stable in multi-blade configurations, with extracted time-frequency characteristics closely matching theoretical expectations (Figs. 2 and 3). The integration of CVMD and OMP effectively balances accuracy and computational efficiency under low-SNR conditions. By leveraging prior-informed dimensionality reduction, the framework achieves a 90% reduction in computational load relative to conventional techniques. Future research will extend this framework to multi-target environments and validate its performance using real-world underwater acoustic datasets. Conclusions This study addresses the challenge of estimating underwater propeller parameters under low SNR conditions by proposing a novel framework that integrates CVMD and OMP. CVMD demonstrates strong capability in suppressing noise while preserving key micro-Doppler features, allowing reliable extraction of target signatures from severely corrupted signals. By incorporating time-frequency characteristics as prior knowledge, OMP enables accurate and efficient blade number estimation, substantially reducing computational complexity. The proposed framework shows high adaptability to varying noise levels and propeller configurations, ensuring robust performance in complex underwater environments. Its balance between estimation accuracy and computational efficiency supports real-time application in acoustic target recognition. The consistency of results with theoretical models further supports the method’s physical interpretability and practical relevance. Future work will extend this approach to multi-target scenarios and validate its effectiveness using experimental acoustic datasets, advancing the deployment of model-driven methods in real-world marine detection systems.
- Micro-doppler,
- Signal denoising,
- Feature extraction,
- Complex Variational Mode Decomposition (CVMD),
- Orthogonal Matching Pursuit(OMP)

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References(23)

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