An Investigation on the Contributing Factors of Enhanced DME Ranging Errors
Bibliographic record
Abstract
With the introduction of Performance-Based Navigation (PBN) in NextGen and SESAR there is a significant increase in the navigation performance requirements. GNSS has become the cornerstone of future aviation navigation, and through ADS-B also of aviation surveillance. However, GNSS vulnerability to interference and jamming necessitates an Alternate Positioning Navigation and Timing or APNT solution that can meet the PBN performance requirements. One of the candidate APNT architectures is the Distance Measuring Equipment (DME) system. However, the legacy DME/N system’s performance is unlikely to meet the stringent APNT performance criteria (e.g. RNP 0.3). Many enabling technologies are proposed to enhance the DME performance, which evolves the legacy DME/N to an enhanced DME (eDME) system. Some enabling technologies include: - Carrier phase tracking, providing precise displacement measurements. These displacement measurements can be used in a “carrier-smoothed-pulse” range and a “pulse-minus-carrier” fashion, analogous to GPS, to increase accuracy and integrity of the ranging solution [1,2]. - Beat signal broadcast, facilitating robust carrier phase tracking and data broadcast. Passive DME ranging with unlimited capacity is possible when the beat signals from multiple transponders are time-synchronized [3]. - Data broadcast, for example by phase shift keying (PSK) of the second pulse of the DME beat signal pulse pairs. Data broadcast can be used for, for example, station identification, ephemeris, health, and signal authentication [4]. The abovementioned three key elements of the eDME system were successfully implemented and validated during a 6-hour flight test campaign in November 2014 [6]. During this flight test, a software-defined-radio- (SDR-) based data recording system was used to record all the outgoing and incoming pulses for both the airborne and the ground equipment. The high sampling rate recordings (10 mega samples per second) are precisely time-tagged with 10-ns resolution. These high-fidelity recordings enable a detailed qualitative and quantitative investigation into DME and eDME errors. A similar but more extensive eDME flight test is planned in March 2015 and will be used to expand this paper. The uplink, downlink, and two-way range performances are evaluated by post-processing of the SDR recordings. As a baseline, the results are first compared with the measurements from a modern DME interrogator. Next, an assessment is made of the sensitivity of DME/N and eDME ranging performance to a variety of system parameters. The same RF data set is processed using different algorithms and processing settings, to provide a fair comparison under a variety of propagation environments. Some of the factors that will be addressed in this paper are: - Smoothing time constant. Smoothing is an effective method of reducing noise and multipath errors. The optimal choice of the smoothing time constant and its merit is related to the multipath frequency components, and to the availability of external aiding, for example from eDME carrier phase [1] - Multipath-induced biases. Multipath with a fading period that is long compared to the receiver smoothing time, or multipath that is very strong compared to the direct signal, can introduce biases in the range measurements. Such bias errors can become a dominant factor in the accuracy and integrity error budget. - Pulse waveform and receiver processing bandwidth. A carefully chosen specification-compliant waveform can provide additional benefits for multipath mitigation. However, the receiver processing bandwidth is a key element to achieving these benefits. The impact of these factors will be discussed in this paper using the data from multiple flight tests that cover various propagation environments. Recommendations are made based on the findings. References: [1] K. Li and W. Pelgrum, Enhanced DME Carrier Phase: Concepts, Implementation, and Flight-test Results, NAVIGATION, vol. 62, no. 3, Fall 2013. [2] K. Li and W. Pelgrum, “Robust DME Carrier Phase Tracking Under Flight Dynamics,” Proceedings of the 2013 International Technical Meeting of The Institute of Navigation, San Diego, CA, January 2013. [3] L. Eldredge et al., “Alternative Positioning, Navigation & Timing (PNT) Study,” International Civil Aviation Organization Navigation Systems Panel NSP), Working Group Meetings, Montreal, Canada, May 2010 [4] “TACAN/DME Digital Data Broadcast Design Plan”, ADA001403, EDMAC Associates, Inc., East Rochester, NY, September 1974 [5] A. Naab-Levy, K. Li, and W. Pelgrum, “DME/N Error Budget Allocation and DME-Next Proof-of-Concept Flight Test and Performance Evaluation,” Proceedings of the ION 2013 Pacific PNT Meeting, Honululu, HI, April 2013. [6] W.Pelgrum et al., “eDME on-air: design, implementation, and demonstration,” submitted to Proceedings of the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, CA, Januray 2015.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".