Signal-Level Fusion-Based Height Estimation of Low-Elevation Target for Distributed Radar

The height estimation of low-elevation targets is highly susceptible to multipath effects caused by ground reflections, leading to significant estimation errors. Distributed radar provides a multidimensional spatial perspective, which can be utilized to improve the accuracy of target height estimation. However, existing methods typically estimate the target height separately at each receiving station and then fuse the estimates at the data level, resulting in limited accuracy improvement. To address this problem, this article proposes a signal-level fusion-based height estimation method of low-elevation targets for distributed radar. First, a target height estimation model based on signal-level fusion across distributed receiving stations is formulated using the maximum likelihood (ML) criterion, and the corresponding cost function for height estimation is subsequently derived. Then, an alternating iterative strategy is employed to decompose the multidimensional optimization problem into multiple 1-D subproblems, enabling efficient target height estimation. Furthermore, the Cramér–Rao bound (CRB) for distributed radar-based height estimation of low-elevation targets is derived, which serves as a theoretical performance benchmark. The numerical results of simulation data and measurement data demonstrate that the proposed method significantly enhances the accuracy of height estimation for low-elevation targets.