# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods  
### Tracking Methods  
Tracking of satellite signals is a fundamental function of Global Navigation Satellite System (GNSS) receivers and is essential for ensuring accurate and reliable positioning. These tracking techniques can be systematically categorized based on their feedback structure and utilization of auxiliary information.  
Broadly, carrier tracking falls into two primary categories: closed-loop tracking and open-loop tracking, distinguished by whether a real-time feedback mechanism is employed [11]. Closed-loop tracking is the traditional method widely adopted due to its low computational complexity. However, it imposes a stringent stability constraint: the product of coherent integration time and loop noise bandwidth must remain below 0.5 [12]. This limitation becomes critical in dynamic or weak-signal scenarios, where long integration times and wider bandwidths are required--potentially leading to loop instability.  
Within closed-loop architectures, two subtypes are commonly used: scalar tracking and vector tracking (VT) [13]. Scalar tracking treats each satellite channel independently, estimating propagation delays without leveraging inter-satellite geometry. While simple, this approach does not exploit the spatial relationships between signals, which limits its performance in complex environments.  
In contrast, vector tracking employs a shared navigation filter that fuses measurements across all channels [14]. The navigation solution, obtained from all satellite observations, generates synchronized feedback for each channel, improving tracking robustness and accuracy--particularly under degraded signal conditions.  
Compared to closed-loop methods, open-loop tracking imposes higher computational demands but offers critical advantages. By employing feedforward estimation instead of feedback loops, open-loop tracking maintains better signal observability, especially in high-dynamic scenarios where Doppler effects cause rapid frequency variations [15]. This architecture enables broader search spaces and faster re-acquisition of signals following fading or interruption, as it can initiate estimation as soon as new samples arrive [16].  
Moreover, open-loop systems inherently avoid the stability issues associated with feedback-based filters [17]. Like closed-loop techniques, open-loop tracking can be classified into traditional open-loop tracking, which emphasizes feature extraction, and Direct Position Estimation (DPE), which estimates the receiver's position directly using the signal's geometric structure.  
Table 1 provides a structured comparison of the major GNSS signal tracking methods introduced above. It summarizes their core concepts, strengths, limitations, and representative references. This overview helps clarify the evolution of tracking techniques and highlights the trade-offs among different architectures in terms of complexity, robustness, and implementation feasibility.  
Another dimension of classification concerns whether external assistance is used. Standalone GNSS receivers rely solely on internal signal processing, whereas Assisted GNSS (A-GNSS) receivers integrate external sensor data--such as inertial measurement units (IMUs) or gyroscopes--to improve tracking performance, especially in signal-denied environments [13; 18].  
A summary of these tracking methods and their interrelationships is presented in Figure 1. This paper adopts the aforementioned classification as a framework to review recent advances in GNSS signal tracking, analyze their respective advantages and limitations across application scenarios, and identify promising directions for future research.