## 1 Introduction  
Multipath interference is a primary source of positioning errors in satellite navigation systems [1, 2]. This phenomenon occurs when a satellite signal is reflected by the ground or obstacles during propagation, resulting in multiple signal paths reaching the receiver. These reflected signals cause delays, attenuation, and distortion, which degrade the receiver's signal acquisition and tracking performance. Consequently, the accuracy of positioning and timing for the user is significantly compromised. The positioning error caused by multipath signals can be as high as hundreds of meters [3]. In severe cases, it may cause the receiver to lose lock on the satellite signal and be unable to complete the positioning function. With the increasing demand for new applications of the GlobalNavigation Satellite System (GNSS) in complex environments such as urban canyons [4], and the heavy reliance on the pseudo-code phase in the implementation of the Low Orbit Navigation Satellite Networking Program pseudo-code [5; 6], the study of efficient mitigation technology for multipath errors in navigation signals is particularly important and has become a research hotspot in the field of navigation signal processing [7]. Approaches for mitigating multipath signals can be classified into three categories: measurement-domain processing techniques [8; 9], hardware-based design methods [10; 11], and baseband signal processing techniques [12; 13]. Measurement-domain processing techniques usually rely on external support or require long-term static observations, limiting their applicability in dynamic environments. Hardware-based methods are constrained by equipment design and cost considerations. In contrast, baseband signal processing techniques have emerged as the most widely adopted approach due to their flexibility and practical implementation potential. Data processing-based methods enable more accurate estimation of multipath parameters by extracting and analyzing signal features. [PERSON] et al. [12] employed the particle filter (PF) to track the evolution parameters of both the direct signal and its multipath signal. However, its calibration accuracy and efficiency are influenced by the a priori distribution of the initial values [13]. [PERSON] et al. [14] proposed a resampling strategy to mitigate particle degradation and missing issues, resulting in higher multipath estimation accuracy compared to conventional methods such as the adaptive multipath estimating delay lock loop (MEDLL) and the Teager-Kaiser-MEDLL approaches. [PERSON] et al. [15] introduced an adaptive difference evolution-based particle filtering algorithm, which can improve accuracy, but requires more priori information than other multipath parameter estimation algorithms. Similarly, the Genetic Algorithm Particle Filter (GAPF) algorithm proposed in [16] integrates genetic algorithm (GA) into the PF framework to address pseudo-code correlation function distortions. However, the GAPF algorithm suffers from premature convergence and only estimates the time delay, and the amplitude of the multipath signal still needs to be estimated separately through Kalman Filtering (KF). [PERSON] et al. [17] and [PERSON] et al. [18] proposed multipath estimation methods based on the Extended Kalman Filter (EKF), effectively reducing the influence of multipath signals on localization error using multiple correlator structures. Meanwhile, [PERSON] et al. [19] employed the Least Squares (LS) method to correct EKF observation points, achieving higher pseudo-code tracking accuracy. However, both LS and EKF rely on linearization to handle nonlinear systems, which may introduce linearization errors and consequently reduce estimation accuracy. To address the sensitivity of the EKF algorithm to initial values, [PERSON] et al. [20] combined PF and EKF, but the constructed estimation exhibits obvious fluctuations. Subsequently, [PERSON] et al.