A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
Abstract
:1. Introduction
2. Methods
2.1. Tracking Methods
2.2. Scalar Tracking
Tracking Method | Core Concept | Advantages | Limitations | Representative References |
---|---|---|---|---|
Scalar Tracking | Each satellite channel is tracked independently without exploiting inter-satellite geometry. | Simple structure; low computational cost. | Poor performance in dynamic or degraded environments; no channel cooperation. | Zhodzishsky, M. (1998) [19], Spilker Jr, J. (1996) [13], Pany, T. (2005) [20], Lian, P. (2005) [21], Ziedan, N. I. (2004) [10], Ren, T. (2012) [22], Curran, J. T. (2012) [23], Sun, Z. (2013) [24], Guo, W. (2014) [25], Yan, K. (2016) [11], Chen, S. (2017) [26], Li, J. (2018) [27], Feng, X. (2023) [12] |
Vector Tracking | Navigation solution derived from all channels jointly feeds back into all loops, utilizing inter-channel geometric relationships. | Robust tracking in weak signal scenarios; better accuracy. | Complex navigation filter; sensitive to modeling errors. | Copps, D. B. (1980) [28], Lashley, M. (2007) [29], Lashley, M. (2009) [30], Lashley, M. (2009) [31], Lashley, M. (2010) [32], Henkel, P. (2009) [33], Chen, Q. (2014) [34], Peng, S. (2012) [35], Yang, H. (2021) [36], Farhad, M. (2021) [37], Mou, M. (2021) [14], Liu, W. (2022) [38], Marcal, J. (2016) [39], Lin, H. (2017) [40], Chen, Q. (2018) [41] |
Open-Loop Tracking | Signal parameters are estimated through feedforward processing without feedback loops. | High observability; robust re-acquisition in fading. | High computational complexity; less suitable for real-time use. | Van Graas, F. (2005) [42], Anyaegbu, E. (2006) [43], Yan, K. (2016) [11], Tahir, M. (2012) [44], Jin, T. (2020) [45], Wu, C. (2022) [46], Stienne, G. (2012) [47], Closas, P. (2007) [48], Tahir, M. (2012) [49] |
Direct Position Estimation | Jointly estimates position directly from raw signals without intermediate synchronization steps. | High positioning accuracy; bypasses code/carrier synchronization. | Computationally expensive; limited practical deployment. | Closas, P. (2007) [48], Closas, P. (2008) [50], Closas, P. (2009) [51], Closas, P. (2010) [52], Closas, P. (2017) [53], Amar, A. (2008) [54], Ramesh Kumar, A. (2015) [55], Ng, Y. (2016) [56], Chu, A. H.-P. (2019) [57] |
2.2.1. Extending Coherent Integration Time
2.2.2. Optimizing the Discriminator
2.2.3. Optimizing the Loop Filter
2.3. Vector Tracking Loops
2.3.1. Evolution of Vector Tracking Technique
2.3.2. Optimizing the Navigation Filter
2.3.3. Vector–Scalar Hybrid Tracking
2.3.4. Ultra-Tight Combining
2.4. Conventional Approach
2.4.1. Improving Discriminator Estimation Precision
2.4.2. Open-Loop and Closed-Loop Hybrid Tracking
Direct Position Estimation
3. Results
4. Conclusions
- 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;
- 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;
- 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;
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Technique | Advantages | Challenges | Representative References |
---|---|---|---|
Maximum Likelihood | Extends coherent integration by performing data erasure after bit synchronization. | Navigation bit flips; computational complexity. | Ren, T. (2012) [22] |
Non-Coherent Integration | Combines multiple 20 ms coherent integrations using a memory discriminator. | Computational complexity; synchronization between different channels. | Borio, D. (2009) [59], Borio, D. (2009) [60] |
Assisted GNSS | Eliminates the effects of data bit flipping using reference base stations. | Requires a reference station; limited by signal availability. | Van Diggelen, F. (2009) [18] |
Deep Combining with INS | Enhances GNSS tracking by combining with inertial navigation systems to mitigate receiver dynamics. | High cost of INS; requires sophisticated integration. | Pany, T. (2005) [20], Pany, T. (2009) [64] |
Wiener Filtering | Robustly tracks the carrier phase by modeling oscillator noise. | Complexity of filter design; requires high-quality oscillators. | Curran, J. T. (2012) [23] |
Kalman Filtering | Uses Kalman filters to track oscillator noise and improve carrier phase tracking accuracy. | High computational load; needs accurate noise models. | Chen, S. (2017) [26], Zhodzishsky, M. (1998) [19] |
Quartz Phase-Locked Loop | Optimizes tracking loops into a common loop and multiple individual loops to improve stability. | Receiver vibration can still impact performance; requires strong signals. | Zhodzishsky, M. (2020) [77] |
Long Coherent Integration | Uses multi-channel cooperative loops to track oscillator error for ultra-long coherent integration cycles. | Requires strong satellite signals; high complexity. | Feng, X. (2023) [12] |
Discriminator Type | Advantages | Limitations | Representative References |
---|---|---|---|
Maximum Likelihood | High estimation accuracy using DTFT; effective in low SNR conditions. | Computational complexity; sensitive to bit flips. | Borio, D. (2008) [78] |
Differential Amplitude | Uses multiple NCOs; straightforward structure. | Performance degrades in dynamic environments. | Guo, W. (2014) [25], Li, J. (2018) [27] |
FFT-Based Estimation | Applies FFT on I/Q coherent integration results in closed-loop tracking; enables real-time processing. | Implementation complexity; less explored in the literature. | Yan, K. (2016) [11], Yan, K. (2017) [83], Wang, X. (2015) [84], Van Graas, F. (2009) [15], Borio, D. (2008) [78], Ba, X. (2009) [82] |
Multiband/Early Delayed | Allows higher frequency resolution; applicable to dynamic environments. | Pseudorange accuracy lower than carrier phase; estimator design more complex. | Yang, C. (2003) [80], Wang, X. (2015) [84] |
Filter Type | Advantages | Challenges | Representative References |
---|---|---|---|
Wiener Filter | Effective at suppressing thermal and oscillator noise; low implementation complexity. | Fixed gain; less adaptive to environmental changes. | Curran, J. T. (2012) [23] |
Kalman Filter | Provides optimal tracking performance with white Gaussian noise; flexible model design. | Sensitive to model inaccuracies; assumes known noise statistics. | Henkel, P. (2009) [33], Lashley, M. (2009) [31], Peng, S. (2012) [35], Liu, W. (2025) [86] |
Extended Kalman Filter | Can handle nonlinear measurement equations; effective in dynamic or weak signal scenarios. | Requires Jacobian calculation; highly sensitive to incorrect linearization or modeling. | Ziedan, N. I. (2004) [10], Chen, Q. (2018) [41], Sun, X. (2013) [24], Psiaki, M. L. (2002) [94], Zhu, Z. (2010) [96] |
Technique | Advantages | Challenges | Representative References |
---|---|---|---|
Extended Kalman Filter | Provides baseline for navigation filter; handles non-linearities in measurements. | Sensitive to model errors; requires linearization at each step. | Liu, J. (2011) [105], Henkel, P. (2009) [33], Lashley, M. (2009) [31], Lin, H. (2017) [40] |
Adaptive EKF | Dynamically adjusts noise covariance; improves adaptability to signal variation. | Increased computational complexity; tuning difficulties. | Peng, S. (2012) [35], Yang, H. (2021) [36], Farhad, M. (2021) [37], Chen, Q. (2014) [34] |
Unscented Kalman Filter | No need for Jacobian; better performance in highly non-linear systems. | Higher complexity; increased memory and computation load. | Liu, W. (2022) [38] |
Graph Optimization | Flexible modeling of dynamic state changes and NLOS effects; scalable to large networks. | Model design is complex; sensitive to constraint inconsistencies. | Jiang, C. (2020) [106], Jiang, C. (2022) [107] |
Technique | Advantages | Challenges | Representative References |
---|---|---|---|
VTL-Assisted STL | Combines robustness of vector tracking with simplicity of scalar tracking. | Complexity in switching logic; balancing performance trade-offs. | Peng, S. (2012) [35], Marcal, J. (2016) [39], Marcal, J. (2016) [104] |
FLL-Assisted VPLL | Maintains carrier lock with improved sensitivity under weak signal conditions. | Requires careful integration and tuning of FLL and PLL components. | Mou, M. (2021) [14], Zhang, X. (2022) [108], Liu, W. (2022) [109] |
Hybrid Tracking Loop | Reduces computational load while preserving tracking accuracy by dual update rates. | Complexity of coordinating multiple update rates; stability concerns. | Lin, H. (2017) [110] |
PDR-Enhanced VT | Enhances positioning robustness by fusing pedestrian dead reckoning (PDR) with VT. | Errors in PDR can propagate into the VT solution. | Jiang, C. (2023) [111] |
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. | Liu, D. (2020) [112] |
Technique | Advantages | Challenges | Representative References |
---|---|---|---|
Centralized Ultra-Tight | Single filter processes multi-channel tracking data; robust against multipath and signal interruptions. | High computational complexity; sensitive to error propagation from model mismatches. | Spilker Jr, J. (1996) [13], Babu, R. (2009) [120], Karaim, M. (2020) [121], Yan, Z. (2021) [122], Yan, Z. (2022) [123], Yan, Z. (2023) [124], Gao, W. (2024) [125] |
Cascaded Ultra-Tight | Two-stage structure reduces processing burden and allows adaptive updates; better dynamic adaptability. | Design of pre-filters is critical; potential risk of accumulated estimation errors. | Abbott, A. S. (2003) [126], Lashley, M. (2010) [32], Petovello, M. (2006) [128], Jwo, D.-J. (2010) [129], Jwo, D.-J. (2013) [130], Luo, Y. (2012) [131], Zhang, X. (2016) [132] |
Technique | Advantages | Challenges | Representative References |
---|---|---|---|
FFT Discriminator | Generates 3D signal image for robust satellite signal detection. | High computational complexity; limited real-time applicability. | Van Graas, F. (2005) [42], Anyaegbu, E. (2006) [43], Yan, K. (2016) [11] |
Enhanced Kay’s Method | Improved estimation accuracy in low SNR scenarios. | Increased algorithm complexity; sensitive to model mismatch. | Tahir, M. (2012) [44] |
Four-Dimensional UKF Estimator | Captures high-order Doppler effects; robust against dynamics. | Increased state dimension leads to higher computational load. | Han, S. (2010) [138], Jin, T. (2020) [45] |
Two-Stage Frequency Estimation | Combines coarse and fine estimation for improved frequency accuracy. | Requires careful transition design between coarse and fine stages. | Wu, C. (2022) [46] |
Method | Advantages | Representative References |
---|---|---|
Maximum-Likelihood Estimation (MLE)-Based DPE | Foundational DPE framework; derives ML estimate directly from received signals. | Closas, P. (2007) [48] |
Bayesian DPE | Extends DPE to Bayesian framework; enables prior information fusion. | Closas, P. (2008) [50], Closas, P. (2010) [52] |
UKF-Enhanced DPE | Applies unscented Kalman Filter to optimize DPE likelihood estimation. | Ramesh Kumar, A. (2015) [55] |
Multi-Receiver DPE | Fuses signals from multiple receivers to jointly estimate PVT solution. | Chu, A. H.-P. (2019) [57] |
Relaxed DPE | Reduces computational complexity via precomputed correlation functions. | Closas, P. (2017) [53] |
Dual-Mode DPE | Utilizes multi-constellation signals to enhance robustness in challenging environments. | Jia, Q. (2025) [143] |
NLOS-Aided DPE | Treats NLOS signals as useful reflections to improve positioning accuracy. | Ng, Y. (2016) [144] |
Machine Learning-Assisted DPE | Integrates random forest regression to mitigate multipath/NLOS errors. | Vicenzo, S. (2024) [146] |
Single-Difference Code-Based DPE | Reduces satellite clock and atmospheric errors to enhance DPE accuracy. | Tang, S. (2024) [147] |
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Gong, Z.; Lin, H.; Liu, Z.; Liu, Z.; Huang, L.; Ou, G. A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals. Remote Sens. 2025, 17, 1713. https://doi.org/10.3390/rs17101713
Gong Z, Lin H, Liu Z, Liu Z, Huang L, Ou G. A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals. Remote Sensing. 2025; 17(10):1713. https://doi.org/10.3390/rs17101713
Chicago/Turabian StyleGong, Zhiqiang, Honglei Lin, Zhe Liu, Zengjun Liu, Long Huang, and Gang Ou. 2025. "A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals" Remote Sensing 17, no. 10: 1713. https://doi.org/10.3390/rs17101713
APA StyleGong, Z., Lin, H., Liu, Z., Liu, Z., Huang, L., & Ou, G. (2025). A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals. Remote Sensing, 17(10), 1713. https://doi.org/10.3390/rs17101713