Tuning a Kalman filter carrier tracking algorithm in the presence of ionospheric scintillation

Abstract

Strong ionospheric electron content gradients may lead to fast and unpredictable fluctuations in the phase and amplitude of the signals from Global Navigation Satellite Systems (GNSS). This phenomenon, known as ionospheric scintillation, is capable of deteriorating the tracking performance of a GNSS receiver, leading to increased phase and Doppler errors, cycle slips and also to complete losses of signal lock. In order to mitigate scintillation effects at receiver level, the robustness of the carrier tracking loop, the receiver weakest link under scintillation, must be enhanced. Kalman filter (KF)-based tracking algorithms are particularly suitable to cope with the variable working conditions imposed by scintillation. However, the effectiveness of this tracking approach strongly depends on the accuracy of the assumed dynamic model, which can quickly become inaccurate under randomly variable situations. This study first shows how inaccurate dynamic models can lead to a KF suboptimum solution or divergence, when both strong phase and amplitude scintillation are present. Then, to overcome this issue, it proposes two self-tuning KF-based carrier tracking algorithms, which self-tune their dynamic models by exploiting the knowledge about scintillation that can be achieved through scintillation monitoring. The algorithms have been assessed with live equatorial data affected by strong scintillation. Results show that the algorithms are able to maintain the signal lock and provide reliable scintillation indices when classical architectures and commercial ionospheric scintillation monitoring receivers fail.

Carrier Tracking using Extended Kalman Filters for GNSS Synthetic Aperture Processing with a Rotating Antenna

“…The use of Kalman filters (KFs) for tracking GNSS signals has received significant attention during the last years [14][15][16][17]. A KF might be thought of as a sequential minimum mean square error (MSE) estimator which exploits a statistical and dynamical model to predict and estimate the parameters of interest representing the filter states [12].…”

“…In general, the design of a KF requires a selection of R k , Q k and P 0|0 . In standard KF tracking architectures, these matrices must be carefully initialized [11,15,17]. This selection has a major impact on the performance of the KF.…”

“…where Q c can be defined as a diagonal matrix with its non-zero elements being the spectral densities of each of the individual processes noises, as in [15].…”

“…The model for Q fref , corresponding to the reference Doppler parameter, is assuming the receiver oscillator error as the dominant error source. The parameter h −2 depends on the type of oscillator used [15]. In our case we will set h −2 = 2 • 10 −20 Hz, which is a reference value for a receiver's clock using an oven controlled crystal oscillator (OCXO).…”

Mitigation of ionospheric scintillation effects on GNSS precise point positioning (PPP) at low latitudes

“…Several approaches have been proposed to improve the positioning performance under scintillation. One approach is to enhance the robustness of the GPS receiver carrier tracking loop by implementing various enhanced tracking algorithms such as a Kalman filter-based phase lock loop (PLL) (Humphreys 2005;Susi et al 2017), frequency lock loop (FLL)-assisted PLL (Zhang and Morton 2009) and FLLassisted PLL with in-phase pre-filtering (Xu et al 2015). A second approach is to exclude the subset of scintillationaffected satellites, especially with the increase in the number of satellites with multiple GNSS systems.…”