Research on High-Precision DOA Estimation Method for UAV Platform in Strong Multipath Environment

Utilizing unmanned aerial vehicles (UAVs) to achieve accurate direction finding of radiation sources in hazardous and complex regions is an important means of information recon- naissance. However, the significant multipath effects of UAVs in complex environments cause serious signal coherence problems. Conventional signal decoherence techniques such as spatial smoothing (SS) and matrix reconstruction suffer from array aperture loss, which makes it difficult to meet the requirements of UAVs for high-resolution direction finding in severe multipath environments. Therefore, resolving the signal coherence problem has become a key bottleneck for high-resolution direction-of-arrival (DOA) estimation techniques in severe multipath environments. This paper proposes a joint high-precision DOA estimation method based on conjugate cross-correlation Toeplitz reconstruction and the Parallel Factor Analysis (PARAFAC) tensor model. First, we introduce the conjugate cross-correlation values of array element data collected by the UAV to conduct Toeplitz reconstruction without dimensionality-reduced reconstruction, achieving signal decoherence. Furthermore, we conduct cross-snapshot cross-correlation between the reconstruction matrix and the data of each array element collected by the UAV, which effectively suppresses noise accumulation and improves the signal-to-noise ratio (SNR). Finally, we stack the set of matrices into a three-dimensional tensor, employing PARAFAC tensor decomposition to enhance the UAV DOA estimation performance. Simulation results show that at low SNR, the proposed method can effectively improve estimation accuracy and solve the problem of signal correlation in strong multipath scenarios that limits traditional UAV lateral methods.