UDPE: Unsupervised Dual-Parameter Estimation for Radar Target Detection in HFSWR With Weibull Clutter

The performance of existing moment-based constant false alarm rate detectors is constrained by the limited availability of homogeneous samples and the difficulty in modeling the local correlation of clutter, which result in inaccurate estimation of clutter distribution parameters. Although neural networks (NNs) have the potential to exploit the spatial correlations among clutter samples to enhance estimation accuracy, their deployment in practical radar scenarios remains challenging due to the unavailability of true clutter parameters, the inconsistency of estimation errors, and the inherent randomness of NNs. To overcome these challenges, an unsupervised dual-parameter estimation method within an ensemble learning framework is proposed for target detection in high-frequency surface wave radar (HFSWR). First, to overcome the problem of unavailable ground truth, an unsupervised moment estimation network is developed, which is inspired by the concept of data reconstruction and accurately estimates the three moments of clutter data by utilizing the samples surrounding the cell under test. Second, we identify and theoretically analyze that the inconsistency of NN moment estimation errors significantly affects the accuracy of shape parameter estimation. To alleviate this issue, a multichannel moment estimation network is constructed to exploit interchannel correlations and improve error consistency. Third, to mitigate the problem of random fluctuations inherent in single-network moment estimation, an ensemble unsupervised moment estimation network is designed, which leverages model aggregation to reduce variance and enhance overall estimation accuracy. Finally, experiments on both simulated and measured shore-based HFSWR data demonstrate that the proposed method achieves superior target detection performance through more accurate and efficient parameter estimation.