Multipath adaptive filtering in GNSS/RTK-based machine automation applications
Bibliographic record
Abstract
Machine control and automation has always been perceived as an intermediate process to increase industrial productivity (and thus profitability), operability, comfort, and safety net gain for human lives and goods. However one of the biggest limitation factors to achieve and implement successful automation systems for the markets of surveying, precision agriculture, aircraft precision approach, maritime ship guidance, and construction automation (just to name a few) has been the difficulty to prove that the underlying positioning infra-structure can provide reliably and continuously position and navigation information throughout all conditions, and scenarios. Nevertheless, in these days the automation of machines based on GNSS-RTK techniques is becoming one of the major trends in the precise positioning industry. In fact, it is projected to grow even further in the long term with the advent of new SBAS and GNSS systems such as the European EGNOS and Galileo systems, respectively. Undoubtedly the decrease in price and complexity from the integration of GNSS with other sensor systems (such as inertial providing higher bandwidths, and lasers bringing better one-dimensional accuracies), has made machinery automation solutions more robust and applicable in different scenarios, including in high-dynamic/vibration applications and harsh environments. Moreover, with the growing establishment of continuous operating GNSS reference stations, to be employed in network-RTK services from which machine automation has been one of the most keen users, some of the problems in mitigating GNSS residual biases (mostly atmospheric) known to occur in the single-baseline RTK technique, have been successfully ameliorated. Despite all these improvements, the mitigation of carrierphase multipath in real-time remains, to a large extent, very limited (contrarily to the mitigation of code multipath through receiver improvements) and it is commonly considered the major source of error in GNSS-RTK applications. This is due to the very nature of multipath spectra, which depends mainly on the antenna location and characteristics of the reflector(s) in its vicinity. Any change in this binomial (antenna/reflectors) regardless of how small, will cause an unknown multipath effect, thus the removal of this error due to receiver spatial correlation is not achievable. In machine automation applications the machinery is expected to perform complex and unpredictable manoeuvres, therefore the removal of carrier-phase multipath should rely on smart digital filtering techniques that adapt not only to the background multipath (coming mostly from the machine reflecting surfaces), but also to the changing multipath environment along the machine path. In this paper, we describe how a typical GPS-based machine automation application using a dual antenna system is used to calibrate, in a first stage, and remove carrier-phase multipath afterwards. The intricate relationship between the platform's 2 rover antennas' dynamics and the changing multipath from nearby reflectors is explored and modelled through several stochastic and dynamical models, and their implementation in an extended Kalman filter (EKF). Tests were performed using real live satellite signals, and from the results we can say that it is possible to estimate in real time, after an initial calibration phase, the relative position of short distance strong multipath reflectors in the vicinity of the platform. Based on that, a multipath profile is created and used to correct the multipath-affected signals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".