# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 4 Conclusions  
This paper presents a comprehensive review of the development trajectory of high-sensitivity tracking technologies in GNSS. It systematically analyzes the transition from traditional closed-loop tracking toward open-loop, hybrid, and deeply integrated architectures. Key strategies such as coherent integration time extension, discriminator design, loop filter optimization, vector tracking (VT), and Direct Position Estimation (DPE) are evaluated in terms of their adaptability to weak signal environments. By comparing various approaches across performance and complexity dimensions, this review outlines the current technical trends and challenges in the design of robust GNSS tracking systems.  
Despite significant advancements, several critical challenges remain. First, the navigation filters in vector tracking and the loop filters in scalar tracking are highly dependent on accurate system models and noise statistics, limiting their robustness under various environmental conditions [33; 58]. Second, the phase noise of consumer-grade oscillators restricts the achievable coherent integration time, posing a major bottleneck for sensitivity improvement [26; 60; 70]. Third, open-loop tracking has certain advantages in terms of dynamics and sensitivity, and its computational complexity hinders implementation on resource-constrained platforms [48; 53; 146].  
In response to the aforementioned challenges, future research is likely to focus on the following three key directions.  
1. Integrating deep learning techniques into filter structures to enhance adaptability and robustness. Deep learning has the capability to model and adaptively adjust signal characteristics under various environmental conditions, thereby achieving more accurate state estimation in complex and dynamic channel environments. By incorporating deep learning algorithms, the parameters of the filters can be dynamically adjusted to maintain optimal performance in challenging scenarios;
2. Current research on collaborative estimation schemes requires strong signals, but complex scenarios are often characterized by weak signals [12]. Therefore, there is a need to continue developing multi-channel collaborative estimation schemes for oscillator noise. By integrating external signal data, such as 4G/5G communication signals, the impact of oscillator noise on the signal can be reduced, coherent integration time can be extended, and the overall system performance can be improved;
3. Design low-complexity approximations for open-loop tracking to better align with hardware resource constraints. By optimizing the algorithm structure and simplifying the computational process, it is possible to reduce the computational complexity of open-loop tracking without significantly compromising sensitivity and dynamic performance, thereby enhancing its feasibility for practical deployment;  
[PERSON], H.L. and G.O.: Conceptualization; Writing--Original Draft; Writing--Review and Editing. [PERSON]. ([PERSON]), [PERSON] ([PERSON]), and L.H.: Writing--Original Draft; Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under grant number U20A20193.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.