# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Conventional Approach
#### Direct Position Estimation
To enhance satellite visibility under complex environments, [PERSON] and [PERSON] [143] proposed a dual-mode DPE receiver design based on GPS L1 and BeiDou BIC signals, thereby improving the robustness of the DPE system by leveraging signals from both systems. In addition, [PERSON] and [PERSON] [144] introduced a method that treats NLOS GPS signals as useful LOS signals, significantly improving DPE positioning accuracy and robustness in urban environments. Regarding algorithmic optimization, [PERSON] incorporated modern estimation techniques into DPE by employing the Unscented Kalman Filter (UKF) to  
Figure 12: Direct Position Estimation (DPE) method. optimize the likelihood estimation process, thereby enhancing overall performance [55]. [PERSON] proposed a novel initialization strategy, categorizing navigation states into two groups--position and clock offset variations and velocity and clock drift variations--followed by vector-based computation to improve estimation efficiency and accuracy [145]. In the domain of multi-sensor fusion, [PERSON] further developed an approach that combines DPE-estimated navigation solutions with image-based map matching, closed-loop GPS signal tracking, and camera image tracking. By incorporating known static GPS receiver positions as a priori information, this method simplifies the joint estimation of 3D position, clock bias, 3D velocity, and clock drift parameters, thereby enhancing the robustness of DPE under multipath conditions. Additionally, [PERSON] proposed a technique to convert originally interfering NLOS signals into useful navigation information [56]. Focusing on DPE architecture, [PERSON] [57] proposed the Multi-Receiver DPE (MR-DPE) technique, which forms a network by connecting multiple DPE receivers with known antenna baselines, fuses their signal measurements, constructs a network-wide likelihood function, and solves for the overall Position/Velocity/Time (PVT) solution using maximum-likelihood (ML) estimation. To reduce the computational complexity of DPE algorithms, [PERSON] introduced the Relaxed DPE (RDPE) approach [53], which precomputes correlation functions on discrete grids and neglects temporal correlations between noisy samples, significantly decreasing computational load. Furthermore, recent research has introduced emerging technologies into DPE. [PERSON] et al. [146] explored the integration of machine learning techniques, such as Random Forest regression, to assist DPE in urban environments heavily affected by multipath and NLOS effects, demonstrating that machine learning can effectively correct DPE positioning errors. [PERSON], [PERSON], and [PERSON] [147] proposed a single-difference code-based DPE method, which mitigates satellite clock errors and atmospheric delays, resulting in significantly improved positioning accuracy compared to conventional DPE methods. Table 9 summarizes the major Direct Position Estimation (DPE) techniques developed in recent years, highlighting their advantages and representative studies. Despite these advancements, all DPE approaches inherently suffer from high computational demands. As the technology continues to evolve, DPE holds significant potential to enhance the accuracy and robustness of satellite navigation. However, there are still relatively few studies on DPE technology, and it faces challenges in entering the consumer market due to its high computational requirements. Nevertheless, as DPE algorithms are optimized, they are expected to bring revolutionary advances to GNSS navigation systems in the future.