## 1 Introduction
[PERSON] [16] introduces a sparse non-uniform matrix array using spatially extended electromagnetic vector sensors and proposes a dimensionality reduction method for estimating both the 2D direction of arrival and signal polarization. Based on the structure, accurate yet ambiguous directional cosine estimates are first obtained. These estimates are refined by extracting guiding vectors from the array manifolds, and a de-blurring method is applied to achieve precise 2D direction of arrival and polarization estimates. [PERSON] [17] proposes a reduced-dimensional, low-complexity algorithm to estimate the direction of arrival, direction of departure, and Doppler shift of a moving target in MIMO radar systems. Two cost functions are derived from distinct objective functions and solved in 3D space using a low-complexity FFT method, which is further reduced to a 2D search. Another dimensionality reduction algorithm using the generalized eigenvalue method estimates unknown parameters in 1D space, minimizing memory requirements. He [18] proposed a RD-MUSIC algorithm utilizing a polarization-sensitive array. The direction of arrival and polarization information of the source signal can be obtained simultaneously using a polarization-sensitive array. The RD-MUSIC algorithm estimates the two-dimensional direction of arrival without the need to jointly calculate the polarization parameters. [PERSON] [19] proposes a method for non-correlated MIMO scenarios, where the large covariance matrix is reduced in size through redundancy reduction, preserving angular information. The resulting conversion matrix enables efficient 2D angle estimation on a reduced prob lem scale. [PERSON] [20] proposed a one-snapshot MUSIC algorithm derived from channel reconstruction, which addresses matrix rank insufficiency and reduces complexity while maintaining satisfactory accuracy compared to traditional 2D-MUSIC. [PERSON] [21] demonstrated that paths with significant power can have their orientation and gain reconstructed for millimeter-wave channel estimation, utilizing spatial sparsity caused by propagation losses. Based on this property, a two-dimensional beam spatial direction estimation method was developed, which estimates both departure and arrival angles, and pairs them using a reduced-dimension MUSIC algorithm. LEO satellites have significantly higher signal strength than conventional GNSS satellites due to their lower orbital altitude. This advantage not only enhances signal availability but also allows for the possibility of utilizing NLOS signals for positioning. However, the propagation of distance errors between NLOS and direct-beam signals can result in significant positioning errors, with NLOS errors reaching tens or even hundreds of meters [22]. In the context of NLOS positioning, researchers have proposed several solutions to mitigate the adverse effects of NLOS signals. One of the more classical approaches is to classify signals as either LOS or NLOS, thereby excluding NLOS signals and using only LOS signals for positioning [23]. However, this method has significant drawbacks when there are insufficient available signals. To address these limitations, researchers have explored alternative methods. One approach involves using a fisheye camera mounted on the user device, which captures images pointing towards the sky to detect NLOS signals [24; 25]. Another method employs 3D LiDAR [26], which corrects for NLOS propagation based on the distances to surrounding buildings obtained from scans and NLOS propagation models. However, both of these methods consume a large amount of power, making them unsuitable for devices that are sensitive to power consumption. Additionally, researchers have developed three-dimensional map-assisted (3 DMA) GNSS positioning algorithms that use ray-tracing techniques to detect and correct reflections from NLOS and multipath propagation [27], providing a positioning accuracy within about 10 m or less.