# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Vector Tracking Loops
#### 2.3.4 Ultra-Tight Combining  
The complementary nature of GNSS and INS is most evident in their positioning principles. When satellite signals are weak, INS can operate independently, while GNSS can correct the error that INS accumulates over time. Based on how the systems are integrated, there are three main forms of combination: loose combination, tight combination, and ultra-tight combination. Both loose and tight combinations primarily use GNSS to assist INS for navigation. However, when satellite signals are unavailable, the error accumulation problem in INS remains. Ultra-tight combination, however, allows the combination filter to provide correction information to the receiver based on the tight combination, enabling deeper integration and significantly improving system robustness [113]. The concept of ultra-tight combining emerged in the 1980s, when the US Department of Defense began using INS measurement data to support GPS loop tracking. Around the same time, [PERSON] discussed the use of INS to assist GPS signal acquisition and tracking [114]. With the advent of VT technology, researchers began exploring ultra-tight combining systems based on both vector and scalar tracking, although there has been some disagreement on the definitions of deep combining versus ultra-tight combining. Researchers like [PERSON] et al. at Stanford University define deep combining as methods where INS assists GNSS receivers in tracking loops [115]. On the other hand, [PERSON] et al. argue that only methods where INS assists in VT of loops should be called deep combining [116]. While the industry lacks a unified definition, the general consensus is that deep combining and ultra-tight combining are essentially the same. Both terms refer to the use of INS information to assist VT of loops [117]. Further, researchers like [PERSON]'s team at Wuhan University suggest that deep combining systems can \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technique** & **Advantages** & **Challenges** & **Representative References** \\ \hline VTL-Assisted STL & Combines robustness of vector tracking with simplicity of scalar tracking. & Complexity in switching logic; balancing performance trade-offs. & [PERSON] (2012) [35], [PERSON] (2016) [39], [PERSON] (2016) [104] \\ \hline FLL-Assisted VPLL & Maintains carrier lock with improved sensitivity under weak signal conditions. & Requires careful integration and tuning of FLL and PLL components. & [PERSON] (2021) [14], [PERSON] (2022) [108], [PERSON] (2022) [109] \\ \hline Hybrid Tracking Loop & Reduces computational load while preserving tracking accuracy by dual update rates. & Complexity of coordinating multiple update rates; stability concerns. & [PERSON] (2017) [110] \\ \hline PDR-Enhanced VT & Enhances positioning robustness by fusing pedestrian dead reckoning (PDR) with VT. & Errors in PDR can propagate into the VT solution. & [PERSON] (2023) [111] \\ \hline LSTM-RNN-Aided VT & Utilizes deep learning (LSTM) to assist state estimation, improving robustness in complex environments. & Requires large training data; risk of overfitting and poor generalization. & [PERSON] (2020) [112] \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of hybrid vector–scalar tracking techniques. be classified into scalar deep combining and vector deep combining, depending on the receiver's tracking loop. They propose that ultra-tight combining is equivalent to vector deep combining, meaning that ultra-tight combining always relies on VTL [118; 119]. As shown in Figure 8, this ongoing debate underscores the lack of consensus on the definition of ultra-tight combining. For the remainder of this section, the term \"deep combining\" will be used exclusively to refer to ultra-tight vector-tracking combining. In general, vector-tracking-based ultra-tight integration systems can be classified into centralized ultra-tight integration and cascaded ultra-tight integration, depending on the core structure of the vector-tracking system.