Research on precise positioning method of low-altitude targets based on DOA estimation

Direction of Arrival (DOA) estimation, as a core technology in array signal processing, holds significant application value in the precise positioning of "low, small, and slow" targets in low-altitude defense and unmanned aerial vehicle (UAV) monitoring. To address the performance degradation of traditional DOA estimation methods due to strong clutter interference, short observation time, multipath effects, and sparse signal distribution in complex low-altitude environments, this paper proposes an improved DOA estimation method that integrates the Hankel-Toeplitz matrix structure with momentum gradient descent. This method exploits the low-rank property induced by signal sparsity and constructs a Hankel-Toeplitz joint matrix to strengthen the signal subspace constraint, transforming the original problem into an unconstrained low-rank recovery model. It innovatively designs the introduction of a momentum coefficient to accelerate the gradient descent algorithm, optimizing the iteration step size and line search parameters to avoid local minima and accelerate convergence. This approach overcomes the limitations of traditional methods in array structure and sample size, enhancing angle resolution and sparse target positioning accuracy, and achieving rapid and accurate low-altitude target positioning. Theoretical analysis and simulation experiments demonstrate that the proposed method significantly improves angle resolution and scene adaptability, showing superior estimation accuracy and noise robustness under non-ideal conditions such as adjacent targets, multiple targets, and single snapshot, providing an effective solution for the precise positioning of low-altitude targets.