TE im Fokus 02/2013 - DFS Deutsche Flugsicherung GmbH

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Informationen aus dem Bereich Forschung und Entwicklung der DFS Deutsche Flugsicherung GmbH Fig. 11: Position accuracy with latency compensation Fig. 10: Position deviation caused by ADS-B uncompensated latency (Example) 7.4 WAM HPA Analysis During the HPA analysis it turned out that further filtering steps for the ADS-B reference data where required in order to insure a satisfactory quality of the data. An additional filtering criteria was the ADS-B quality indicator Navigation Uncertainty Category – Position (NUC P), which describes the integrity of the ADS-B position data. The identified threshold was that only ADS-B data with a NUC P value of at least 5 were taken into for HPA analysis. 2 It was furthermore decided to use only ADS-B data with a quite small ADS-B uncompensated latency. The threshold “quite small” was elaborated with an empirical analysis of the data in order to find the optimal balance between a reasonable statistical baseline (test coverage) and ADS-B reference data with a small ADS-B uncompensated latency. Figure 11 shows the result if either the ADS-B data with small uncompensated latency are selected or the latency is compensated. During the qualification of the PAM-FRA WAM sensor, it was agreed that the system supplier (THALES ATM) used a tool, which covered WAM specific tests, which were not covered at all and/or not sufficiently covered by the SASS-C V6.7 tool. The tool called MAGS-EXPLORER was developed by THALES ATM and was qualified by DFS. 2 NUCP ranges from 0 to 9, with 9=best quality The HPA module of the MAGS-EXPLORER implemented the HPA test approach based on the DAWC tool jointly developed by EUROCON- TROL, THALES and DFS. Further features where added in order to insure the traceability of the results. In Figure 12 pre-processing statistics are shown including how many input tracks/plots were existing in the chosen data set and to which extent and by which reason the relevant data were not used for HPA analysis. Fig. 12: WAM HPA results (example 72h test) 7.4 Conclusion from HPA analysis The evaluations were proving that ADS-B data with small ADS-B uncompensated latency could be used as reference data for WAM HPA analysis. The test approach was verified with different test tools in order to find out the limitations and possibilities of this new HPA test method. 8. False Target Detection A false target is identified as a target report at a position of greater distance from the true position than 2100m for 5NM separation and/or 1690m Data Performance Analysis of WAM Systems Ausgabe 2/2013 Seite 11
Informationen aus dem Bereich Forschung und Entwicklung der DFS Deutsche Flugsicherung GmbH for 3NM separation or a target report that is spurious i.e. does not relate to any target within the service coverage volume. According to ED-142 [1], the probability of false detection (PFD) shall be less than or equal to 0.1%. The WAM false target analysis intended to identify firstly the nominal cases and secondly the different false target cases. The following symbols and terms are used for the description of the nominal and false target cases: • Detection Coverage Volume (DCV): Volume of airspace in which the surveillance system receives signals from targets. • Surveillance Coverage Volume (SCV): Volume of airspace for which the surveillance system provides target reports to surveillance data processing system (SDPS). Within the SCV the required performance criteria shall be fulfilled. The SCV must be a sub-volume of the detection coverage volume. • Symbols: Fig. 14: Type 2: large position deviation („jumps“) • Type 3: small position deviation („outlier“, also three cases distinguished, see Figure 15). As the difference in position deviation is smaller, another detection mechanism needs to be used. The analysis of cases and in Figure 14 has to clarify if the corresponding false target reports could generate a false track. This is usually the case if more than three false target reports in a timeframe of about 15 second exist having similar orientation. A structured analysis about possible WAM false targets identified the following four cases (plus several sub-cases) • Type 1: not existing traffic (two cases distinguished, see Figure 13) Fig. 15: Type 3: small position deviation („outlier“) Fig. 13: Type 1: not existing traffic • Type 2: large position deviation („jumps“, three cases distinguished, see Figure 14) • Type 4: Split Tracks (see Figure 16). This case identifies a situation where the WAM system has established a track for a certain target (e.g. as Mode S target) and establishes another track (using a different track number) for the same target as Mode 3A/C target. Usually these split tracks have no significant position deviation (horizontal and vertical) between original and split track. In most cases, the Mode 3A code is similar for both tracks. Fig. 16: Type 4: split tracks Data Performance Analysis of WAM Systems Ausgabe 2/2013 Seite 12
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TE im Fokus 02/2013 - DFS Deutsche Flugsicherung GmbH

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