Kalman filtering with noncoherent integrations for Galileo E6‐B tracking

Susi, Melania; Borio, Daniele

doi:10.1002/navi.380

Cited by 16 publications

(12 citation statements)

References 13 publications

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“…As a benchmark algorithm we consider the adaptive extended Kalman PLL with AR scintillation model presented in [15] and we label this algorithm as EKPLL-sAR-ADAPT in the following. A Kalman DLL would further reduce the noise sensitivity of the code loop [28], [29], but as we are only interested in the evaluation of the proposed algorithms with respect to carrier phase estimation, we use a second order noncoherent DLL configured with the initial time-delay estimate being the timedelay of the simulated input signal as well as with a very small loop bandwidth and extended correlators [28] for the early and late correlators in order to reduce the noise and keep the code replica synchronized with the input signal, so that approximations (18), (19) and (20) The discriminator-based Kalman PLL of KPLL-sKIN is defined by F 1 , Q 1 , H 1 , and R 1 , as described in the previous section. The innovations are directly computed by the phase discriminator given in (19).…”

Section: Evaluation Of the Proposed Algorithms For Scintillation Monitoring And Mitigationmentioning

confidence: 99%

Traditional and Kalman Filter-Based GNSS Receiver Structures for Ionospheric Scintillation Monitoring and Mitigation

Lopes¹,

Antreich²,

Kuga³

2021

Preprint

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Section: Evaluation Of the Proposed Algorithms For Scintillation Monitoring And Mitigationmentioning

confidence: 99%

Traditional and Kalman Filter-Based GNSS Receiver Structures for Ionospheric Scintillation Monitoring and Mitigation

Lopes¹,

Antreich²,

Kuga³

2021

Preprint

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“…In case of the extended Kalman filter, we have (12) as the nonlinear observation equations, whose linearization leads to…”

Section: Kalman Plls For Scintillation Mitigationmentioning

confidence: 99%

Kalman Filter-Based Carrier Tracking with Ionospheric Scintillation Mitigation for GNSS

Lopes¹,

Antreich²,

Kuga³

2021

Anais Do XXXIX Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In this work we present two architectures of Kalman phase locked loop (PLL) to mitigate ionospheric scintillation induced effects on global navigation satellite systems (GNSS) receivers: a discriminator-based and an extended Kalman PLL using independent kinematic process models for the scintillation phase and amplitude to improve robustness in carrier synchronization. We show that the Kalman PLLs achieve advanced Doppler phase and scintillation amplitude estimation performance compared to an extended Kalman PLL with an autoregressive (AR) scintillation process model, with comparable scintillation phase estimation performance. The proposed Kalman PLLs have the additional benefits of not requiring parameter identification of AR models.

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“…This algorithm updates the loop bandwidth performing a weighted difference of estimated noise and estimated signal dynamics. Alternative tracking methods with Kalman filtering (KF) are promising and have gained popularity in the literature [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]; however, this approach still has some hardware implementation limitations and will not be considered for the remainder of the paper.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Adaptive Loop-Bandwidth Tracking Techniques in GNSS Receivers

Cortés

Merwe

Nurmi

et al. 2021

Sensors

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Global navigation satellite system (GNSS) receivers use tracking loops to lock onto GNSS signals. Fixed loop settings limit the tracking performance against noise, receiver dynamics, and the current scenario. Adaptive tracking loops adjust these settings to achieve optimal performance for a given scenario. This paper evaluates the performance and complexity of state-of-the-art adaptive scalar tracking techniques used in modern digital GNSS receivers. Ideally, a tracking channel should be adjusted to both noisy and dynamic environments for optimal performance, defined by tracking precision and loop robustness. The difference between the average tracking jitter of the discriminator’s output and the square-root Cramér-Rao bound (CRB) indicates the loops’ tracking capability. The ability to maintain lock characterizes the robustness in highly dynamic scenarios. From a system perspective, the average lock indicator is chosen as a metric to measure the performance in terms of precision, whereas the average number of visible satellites being tracked indicates the system’s robustness against dynamics. The average of these metrics’ product at different noise levels leads to a reliable system performance metric. Adaptive tracking techniques, such as the fast adaptive bandwidth (FAB), the fuzzy logic (FL), and the loop-bandwidth control algorithm (LBCA), facilitate a trade-off for optimal performance. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. All three methods steer a third-order adaptive phase locked loop (PLL) and are tested in simulated scenarios emulating static and high-dynamic vehicular conditions. The measured tracking performance, system performance, and time complexity of each algorithm present a detailed analysis of the adaptive techniques. The results show that the LBCA with a piece-wise linear approximation is above the other adaptive loop-bandwidth tracking techniques while preserving the best performance and lowest time complexity. This technique achieves superior static and dynamic system performance being 1.5 times more complex than the traditional tracking loop.

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Kalman filtering with noncoherent integrations for Galileo E6‐B tracking

Cited by 16 publications

References 13 publications

Traditional and Kalman Filter-Based GNSS Receiver Structures for Ionospheric Scintillation Monitoring and Mitigation

Traditional and Kalman Filter-Based GNSS Receiver Structures for Ionospheric Scintillation Monitoring and Mitigation

Kalman Filter-Based Carrier Tracking with Ionospheric Scintillation Mitigation for GNSS

Evaluation of Adaptive Loop-Bandwidth Tracking Techniques in GNSS Receivers

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