Article citation information:
Fedorchuk, A., Goś, A. Comparative analysis of radar systems in regional
aviation: a case study of Deblin airfield. Scientific Journal of Silesian University of
Technology. Series Transport. 2026, 132,
111-127. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2026.132.6
Alina FEDORCHUK[1],
Artur GOŚ[2]
COMPARATIVE ANALYSIS OF RADAR SYSTEMS IN REGIONAL AVIATION: A CASE STUDY
OF DEBLIN AIRFIELD
Summary. This article
presents the results of an experimental study on the registration performance
of two radar-based systems - GCA-2000 and AVIA-W - using flight operations
between the regional airfields of Deblin and Radom as
a case study. GPS satellite navigation data were employed as a reference source
to enable both quantitative and spatiotemporal comparisons of radar
registration characteristics. When the comparison was restricted to the common
temporal window of each radar and the 1-s GPS reference series, GCA-2000
recorded 28.1% and 30.7% of the corresponding GPS epochs for flights 3117 and
3121, respectively, whereas AVIA-W recorded 11.9% and 12.3%. These values
characterize the relative temporal sampling density of the radar records and
not the overall spatial coverage or target-detection efficiency. A graphical
analysis of the temporal characteristics showed that the average interval
between successive GCA-2000 records was 3-4 seconds. This empirical interval
characterizes the recorded COMBINED-mode display output and is not directly
comparable with the 5-second refresh period of the ASR component alone, because
GCA-2000 operated in COMBINED mode during the experiment. For AVIA-W, the mean
registration interval was approximately 8 seconds, which is longer than the
nominal 6-second refresh period and therefore corresponds to a lower update
frequency than the nominal value. The study also examined the spatial
relationship between radar-registration gaps, terrain changes, and forested
areas. Several gaps coincided spatially with these features; however, because
only two flights were analyzed and no inferential
statistical test was performed, these patterns are treated as descriptive
spatial associations rather than statistically confirmed causal effects. These
descriptive observations can support future studies of ground-based
surveillance-system placement and integrated radar-satellite airspace
monitoring.
Keywords: accuracy,
flight monitoring systems, flight parameters, navigation, radar
1.
INTRODUCTION
Before the implementation of modern flight
monitoring systems such as GPS (Global Positioning System), flight management
relied primarily on ground‑based navigation aids and radar systems.
Ground-based radio-navigation aids such as DME (Distance Measuring Equipment),
VOR (VHF Omnidirectional Range), and NDB (Non-Directional Beacon) have
historically supported aircraft navigation, with DME remaining an important
stand-by positioning source in civil aviation (Pleter
& Constantinescu, 2024; Ostroumov et al., 2021).
Although these systems provided a basic level of safety, their accuracy and
coverage were limited, particularly in remote or challenging environments such
as mountainous or oceanic regions. Radar surveillance systems also played a
significant role in real-time air traffic monitoring (Bloisi
et al., 2012; Stahl et al., 2018). Nevertheless, these methods required
substantial technical infrastructure and presented coverage limitations, which
posed challenges to maintaining adequate positioning accuracy in specific areas
(Stahl et al., 2018).
The introduction of GNSS (Global Navigation
Satellite System) technologies, such as GPS, marked a new era in flight
monitoring. GNSS enables high‑precision real‑time aircraft
positioning, significantly improving flight safety and reducing the risk of mid‑air
collisions (Baburov et al., 2020). At the same time,
GNSS performance can be degraded by antenna obscuration, ionospheric
scintillation, multipath, and radio-frequency interference, which may cause
tracking degradation or loss (Sabatini et al., 2017). To enhance reliability,
monitoring systems are increasingly supported by modern radar‑based
solutions, including ADS‑B (Automatic Dependent Surveillance-Broadcast),
MLAT (Multilateration), and other technologies. ADS-B
is a cooperative surveillance system that obtains aircraft position from
onboard navigation systems and broadcasts position and other aircraft state
information to ground stations and other equipped aircraft (Ali et al., 2015).
In turn, MLAT determines aircraft position using triangulation based on signals
from onboard transponders, making it especially effective in areas with limited
GNSS coverage (Lo & Enge, 2012; Zekavat et al.,
2021). Integrating GNSS with systems like ADS‑B and MLAT minimizes the
impact of external disturbances, enhancing the stability and accuracy of flight
tracking even in challenging environments such as mountainous terrain or near
large airfields (Inoue et al., 2022).
Different airfields utilize various flight
monitoring systems depending on operational needs, geographic location, and
financial capacity. Airfields with high traffic volumes are typically equipped
with multifunctional radar platforms such as the GCA-2000 (Ground Controlled
Approach 2000). In contrast, smaller regional airfields may rely on simpler
systems based primarily on GNSS technologies. This approach allows the
infrastructure to be tailored to each airfield’s specific characteristics while
ensuring an adequate level of flight safety and operational efficiency. At the Deblin airfield, three main systems are used for navigation
and flight monitoring, providing aircraft positioning capabilities. The first
uses GPS position data recorded by the onboard S2-3a flight recording system;
the GPS latitude and longitude channels are stored at 1-s intervals. However,
GNSS-based positioning can be affected by signal obstruction, multipath,
ionospheric effects, and radio-frequency interference (Sabatini et al., 2017).
The second system is the GCA-2000 multifunctional radar platform, which operates
in three modes: PAR (Precision Approach Radar), ASR (Airport Surveillance
Radar), and COM (Communication). The PAR mode ensures accurate guidance during
final approach, ASR monitors air traffic within the airfield zone, and COM
integrates with air traffic management systems. Due to its multifunctional
nature, GCA-2000 plays a critical role in ensuring flight safety (Farhadmanesh et al., 2024). The third system is the AVIA‑W
radar, which incorporates advanced technologies for monitoring flights under
complex operational conditions. Its innovative features support comprehensive
air traffic control with high accuracy and reliability.
Studies confirm that the GCA-2000 system
complies with international accuracy standards established by the ICAO
(International Civil Aviation Organization) for determining coordinates and
azimuth angles (Ciećko et al., 2023). Additionally,
analysis shows that the GCA-2000 radar performs its functions effectively even
under conditions that may cause short‑term target detection
interruptions. The combined use of GPS, GCA-2000, and AVIA‑W systems at
the Deblin airfield ensures a high level of flight
accuracy and safety, adapting each system to specific tasks and operational
conditions. Nevertheless, each system requires ongoing testing and data
analysis to ensure safe and efficient flight operations through regular quality
assessments. The Deblin airfield serves as an
important regional aviation hub, supporting both training and operational
flights. Therefore, comprehensive analysis of flight monitoring system data
remains a key task in maintaining the effectiveness and safety of aviation
activities.
This study aims to conduct a detailed
comparative analysis of the registration characteristics of the surveillance
and navigation systems operating at the Deblin
airfield. The systems under investigation include GPS position data recorded by
the onboard S2-3a flight recording system, the GCA-2000 radar system operating
in multiple modes (PAR/ASR/COM), and the AVIA‑W radar. Special attention
is given to comparing these systems in terms of their spatial and temporal
registration characteristics.
2. METHODOLOGY
AND EXPERIMENTAL DATA
To
compare the registration characteristics of the GCA-2000 and AVIA-W radar
systems using GPS data as the reference trajectory, a series of sequential
testing and data-processing stages was conducted. Each stage was based on a
comparative evaluation of the registration parameters of the GCA-2000 and
AVIA-W radars against the synchronized GPS reference trajectory. Additionally,
the analysis incorporated spatiotemporal characteristics of the flights,
including registration timestamps, distances from the radar, and flight
altitude. The study also accounted for the influence of topographic features,
including both natural and artificial obstacles, which could affect the quality
of data registration. All three datasets were transformed to UTC using the
synchronization procedure described below. To avoid combining temporal sampling
density with periods outside radar coverage, the radar-to-GPS ratio was
calculated only within the common temporal window corresponding to each radar
series. The absence of radar registrations outside this window was assessed
separately in the spatial coverage analysis.
GPS
latitude and longitude were obtained from the onboard S2-3a flight recording
system at a sampling rate of 1 Hz (Jóźko, 2006). The
specific installed GPS solution was not independently calibrated against an
external geodetic reference during this experiment; therefore, the GPS series
is treated as a reference trajectory rather than as error-free ground truth.
For scale only, the published GPS Standard Positioning Service performance
standard specifies global-average 95% position errors of ≤8 m
horizontally and ≤13 m vertically, with worst-location values of
≤15 m horizontally and ≤33 m vertically (U.S. Department of Defense,
2020). These specification values are not used as an empirical uncertainty
estimate for the particular S2-3a installation.
Temporal
synchronization was established after the experiment by simultaneously
comparing UTC displayed by a GPS receiver with the clocks shown on the GCA-2000
and AVIA-W radar displays. The GCA-2000 display clock was found to be 46 s
ahead of UTC, whereas AVIA-W displayed Polish local summer time (UTC+2). The
corrected radar times were therefore obtained as
At
the first stage of the analysis, a relative temporal sampling ratio (
where: nsys -
relative temporal sampling ratio, %; nrad - number of observations recorded by the
radar within its registration window; nGPS,win - number of GPS observations recorded
within the same temporal window (from the first to the last radar record,
inclusive). Because GPS data were recorded at 1-s intervals,
The
second stage involved calculating the average data update interval for each
surveillance system, as well as the average flight altitudes. For GCA-2000, the
technical refresh characteristics are mode-dependent: the PAR component updates
once per second, the ASR component has a maximum refresh interval of 5 seconds,
and the SSR component updates every 4 seconds. During the experiment, GCA-2000
operated in COMBINED mode, integrating PAR and ASR information; therefore, the
observed GCA-2000 record sequence cannot be evaluated against a single 5-second
ASR interval. The GCA-2000 observations used in the analysis were extracted
frame-by-frame from the recorded PPI display, with a timestamp assigned to each
displayed change in aircraft position. For AVIA-W,
the nominal refresh period is 6 seconds. Consequently, the empirical intervals
derived from the recorded sequences were interpreted as intervals between
successive displayed position updates rather than as direct estimates of the
scan period of an individual GCA-2000 subsystem. For the temporal statistics
reported in Table 3, radar records were ordered chronologically by UTC time and
the inter-record interval was defined as
At
the third stage of the study, a detailed analysis of the topographic
characteristics of the area between the Deblin and
Radom airfields was carried out. The assessment included identifying elevation
changes, natural and artificial obstructions, and examining their potential
impact on the data accuracy of the GCA-2000, AVIA‑W, and GPS systems. For
this purpose, flight altitudes were converted into a relative height system
using formula (2), with consideration of the geoid height based on the EGM2008
model:
where:
For
each flight, map diagrams were generated to illustrate the relationship between
flight altitude, topographic characteristics, and radar registration distance.
Additionally, the flight trajectories were plotted on topographic maps of the
study areas. The use of terrain maps allowed for a better understanding of the
spatial patterns of radar registration and enabled the assessment of the
relationship between terrain topography and radar data quality. The applied
methodology ensures a comprehensive assessment of the performance of the
GCA-2000 and AVIA‑W surveillance systems under real‑world operating
conditions. The analysis considers the influence of natural obstacles, terrain
topography, and flight trajectory complexity. This approach makes it possible
to compare the observed registration characteristics with the available
technical specifications and to identify potential factors affecting data
registration. For the analysis of surveillance system parameters, flight data
from two M28 «Bryza» aircraft (Fig. 1) were used. The experiment was conducted
on 17 April 2023 using two M-28 “Bryza” turboprop aircraft operating from
Warszawa-Radom and performing training approaches at Deblin.
The aircraft were assigned individual transponder SQUAWK codes 3117 and 3121;
these codes are used throughout the paper as the two flight identifiers. The
source documentation also indicates that Mode 3/C altitude information was
available on the radar displays. The radar reference coordinates used in the
spatial calculations were GCA-2000: 51°33′00.39″ N,
21°53′27.19″ E and AVIA-W: 51°33′19.42″ N,
21°54′09.00″ E; these positions were obtained from MIL AIP Poland
(MIL AIP Poland, 2023). For the spatial boundary analysis in Figs. 5-8, the
distances corresponding to the first and last radar registration epochs were
recalculated as WGS84 geodesic distances from the synchronized GPS position of
the aircraft to the corresponding radar site. These values are reported to 0.1
km in Table 2 and represent observed boundary locations at registered epochs
rather than distances estimated visually from the figures. To avoid ambiguity
in the vertical quantities, Fig. 3 is interpreted as relative terrain
elevation, whereas H in Figs. 9-11 denotes the QFE-referenced aircraft-altitude
series (QFE = 1008 hPa).
A
calibrated radar cross-section (RCS) for the particular M-28 aircraft and a
systematic time series of instantaneous target aspect/elevation angles were not
available in the experimental dataset. No separate time-varying
transponder-mode log beyond the assigned SQUAWK identifiers and displayed Mode
3/C altitude information was retained for this analysis. These variables are
therefore treated as unobserved factors. In addition, because the study
comprises only two flights and successive trajectory observations are serially
dependent, no inferential significance test or confidence interval was used to
establish terrain or forest effects. The spatial analysis is consequently
descriptive and reports coincidence/association of registration gaps with
mapped features rather than causal or statistically significant correlations.
Fig. 1.
General view of the M28 “Bryza” aircraft
Both
analyzed flights were conducted on 17 April 2023. The first flight, identified
by SQUAWK 3117, began at 18:08:37 and ended at 20:32:10. GPS data were recorded
throughout the entire flight, while the GCA-2000 system registered data from
18:19:57 to 20:22:29, and the AVIA-W system - from 18:18:02 to 20:23:03. The
second flight, identified by SQUAWK 3121, started at 18:01:33 and ended at
20:24:43. GPS also provided continuous data, whereas GCA-2000 recorded
information from 18:20:44 to 20:14:20, and AVIA-W - from 18:20:57 to 20:14:58.
Selected technical and nominal update characteristics of the observation
systems used for flight data analysis are presented in Table 1.
Tab.
1
Technical and Nominal Update Characteristics of
the Surveillance Systems
|
Registration |
Maximum
Registration Distance, Rmax, km |
Registration
Period, Trad, s |
Theoretical
number of registered data per hour, n, h⁻¹ |
|
GPS position data /S2-3a/ |
not applicable |
~ 1 s |
3600 |
|
Radar GCA-2000 (COMBINED during test) |
ASR mode ~ 53 km |
PAR: 1 s; ASR: max. 5 s; SSR: 4 s |
mode-dependent; no single value for COMBINED |
|
Radar AVIA‑W |
ASR mode ~ 100 km |
~ 6 s |
600 |
Sources for
Table 1: S2-3a sampling characteristics - Jóźko
(2006); GCA-2000 - POL-3 1P5-GCA-2000-2, Chapter 1, Technical Documentation;
AVIA-W - AVIA-W Airfield Area Control Radar Station Technical Documentation
(1988).
According
to Table 1, the S2-3a flight recording system stores GPS position data at 1-s
intervals, corresponding to up to 3,600 epochs per hour. This sampling rate
provides a dense temporal reference series; a radar-style maximum registration
distance is not applicable to GPS, and positional accuracy is considered
separately. For GCA-2000, the refresh characteristics
depend on the active radar component: PAR updates once per second, ASR has a
minimum refresh interval of 5 seconds, and SSR updates every 4 seconds. During
the test, GCA-2000 operated in COMBINED mode; therefore, the previously used
value of 720 records per hour, derived from a 5-second ASR interval, does not
represent the theoretical output of the analyzed COMBINED-mode sequence and is
no longer used as a reference value. The ASR component has a maximum signal
tracking distance of approximately 53 kilometers. The AVIA-W system features an
extended maximum signal tracking range of about 100 kilometers. Its nominal
data refresh interval is 6 seconds, corresponding to up to 600 updates per hour
under uninterrupted operation. This larger nominal detection range provides a
wider potential surveillance area than that specified for the GCA-2000 ASR
component. To evaluate the performance of the tracking systems, actual flight
data records were used, as presented in Table 2.
Tab.
2
Recorded (Actual) Flight Data and
Registration-Boundary Distances
|
Registration System |
Flight Number |
Flight Duration /h:m:s/ |
Actual number of registered data /n/ |
Distance at
first radar registration, km |
Distance at
last radar registration, km |
|
GPS |
3117 |
2:23:33 |
8614 |
- |
- |
|
3121 |
2:23:10 |
8591 |
- |
- |
|
|
GCA-2000 |
3117 |
2:02:32 |
2066 |
23.6 |
43.7 |
|
3121 |
1:53:36 |
2092 |
2.3 |
44.1 |
|
|
AVIA‑W |
3117 |
2:05:01 |
890 |
29.9 |
47.2 |
|
3121 |
1:54:01 |
842 |
2.0 |
47.7 |
For
flight 3117, GPS recorded 8,614 epochs over 2 h 23 min 33 s, GCA-2000 recorded
2,066 observations over 2 h 02 min 32 s, and AVIA-W recorded 890 observations
over 2 h 05 min 01 s. For flight 3121, GPS recorded 8,591 epochs over 2 h
23 min 10 s, GCA-2000 recorded 2,092 observations over 1 h 53 min 36 s, and
AVIA-W recorded 842 observations over 1 h 54 min 01 s. The experimental
flights were conducted under comparable field conditions. The synchronized GPS
position series from the S2-3a recorder was used as the reference trajectory
for the spatial comparisons and was not treated as an error-free reference
standard. The first and last registration distances in Table 2 were calculated
from synchronized GPS positions at the corresponding radar epochs and were used
in the spatial discussion instead of approximate distances read from the map
figures.
3. RESULTS
The first analysis compared the number of radar observations with the
number of 1-s GPS epochs within the same radar registration window. For flight
3117, the GCA-2000 window (18:19:57-20:22:29) contained 7,353 GPS epochs; 2,066
radar observations corresponded to a relative temporal sampling ratio of 28.1%.
For AVIA-W, the window (18:18:02-20:23:03) contained 7,502 GPS epochs, and 890
radar observations corresponded to 11.9%. For flight 3121, the GCA-2000 window
(18:20:44-20:14:20) contained 6,817 GPS epochs; 2,092 radar observations
yielded 30.7%. The corresponding AVIA-W window (18:20:57-20:14:58) contained
6,842 GPS epochs, and 842 radar observations yielded 12.3%. GCA-2000 therefore
showed a higher relative temporal sampling density than AVIA-W in both flights.
The revised indicator excluded the GPS-only periods before the first and after
the last radar registration; those periods were considered separately in the
spatial coverage analysis. The reported percentages characterized temporal
sampling density within the radar registration interval and were not
interpreted as overall registration or detection efficiency.
Successive inter-record intervals Δt were
then analyzed for the GCA-2000 and AVIA-W radar sequences after UTC
synchronization. The 1-s S2-3a GPS series provided the temporal reference
cadence, whereas Δt was calculated between
consecutive radar epochs and not as a radar-minus-GPS clock offset. For flights
3117 and 3121, Figure 2 illustrated the distribution of these inter-record
intervals over the observation period.
|
|
|
|
(a) |
(b) |
|
|
|
|
(c) |
(d) |
Fig. 2.
Inter-record interval Δt
as a function of observation time: (a) flight 3117, GCA-2000; (b) flight 3121,
GCA-2000; (c) flight 3117, AVIA-W; (d) flight 3121, AVIA-W
Figures 2a-d showed that the intervals between successive radar
registrations were non-uniform and included longer gaps in both flight records.
Because fixed upper thresholds could not be established reliably by visual
inspection, temporal variability was quantified in Table 3 using the mean and
standard deviation of Δt. For GCA-2000, the
empirical mean interval of
3-4 s characterized the COMBINED-mode display record and did not imply that the
ASR component itself refreshed faster than its technical specification. The
AVIA-W sequences also contained longer gaps, as reflected by the mean and SD
values in Table 3. The plots were therefore used for qualitative visualization,
whereas the numerical assessment was based on the tabulated statistics.
A topographic analysis was then conducted for the area between the Deblin and Radom airfields. Figure 3 presented the
longitudinal terrain profile between the two airfields in the adopted relative
height reference system.
Fig. 3.
Terrain profile between Deblin and Radom airfields in
the relative height reference system
The relative terrain elevation along the route reached approximately +60
m in the adopted local reference system (Fig. 3). This value described the
terrain profile only and was distinct from the aircraft-altitude parameter H
used in Figures 9-11. The distance between the two airfields was approximately
50 km, within the nominal ASR coverage ranges reported for GCA-2000 (~53 km)
and AVIA-W (~100 km). The terrain profile was retained as spatial context for
interpreting registration gaps; no causal terrain effect was inferred from
these two flights.
Figure 4 showed the relative elevation map and GPS trajectories of both
flights. Flight 3121 followed a more complex route with sharper turns, multiple
landing approaches, and additional maneuvers within the radar coverage area,
reflecting the training exercises performed during the flight. Flight 3117
contained fewer maneuvers and a more structured landing pattern. This contrast
supported a descriptive comparison of the two registration patterns, but with
only two flights the study did not establish a general dependence or
independence of radar performance on route complexity or flight duration.
Fig. 4. Flight
trajectories recorded by the GPS system
Figure 5 showed the GCA-2000 registration pattern for flight 3117
against the reference GPS trajectory and relative-elevation base layer. The
corresponding first and last registration distances were summarized in Table 2.
Fig. 5. Flight
trajectory recorded by the GPS and GCA-2000 systems for the first flight
(schematic distance annotations; quantitative registration-boundary distances
are given in Table 2)
For flight 3117, GCA-2000 registration began at 23.6 km from the radar
site and the last registered epoch occurred at 43.7 km (Table 2). The onset of
the registered segment coincided spatially with a transition in relative
terrain elevation, while the central part of the flight and the trajectory near
Deblin contained the densest GCA-2000 registration.
Figure 6 showed the AVIA-W registration pattern for the same flight.
AVIA-W registration began at 29.9 km from the radar site and the last
registered epoch occurred at 47.2 km (Table 2). The onset again coincided
spatially with a relative-elevation transition, and the last registered epoch
occurred on the return leg toward Radom.
Fig. 6. Flight
trajectory recorded by the GPS and AVIA-W systems for the first flight
(schematic distance annotations; quantitative registration-boundary distances
are given in Table 2)
Figure 7 showed the GCA-2000 registration pattern for flight 3121.
Registration began at 2.3 km from the GCA-2000 site and the last registered
epoch occurred at 44.1 km on the return leg (Table 2). Thus, a substantial
initial part of the Radom-Deblin leg was not represented in the GCA-2000
record, and registration began only when the aircraft was already close to the
radar site.
Fig. 7. Flight
trajectory recorded by the GPS and GCA-2000 systems for the second flight
(schematic distance annotations; quantitative registration-boundary distances
are given in Table 2)
Figure 8 showed the AVIA-W registration pattern for flight 3121.
Registration began at 2.0 km from the AVIA-W site and the last registered epoch
occurred at 47.7 km on the return leg (Table 2). The pattern was similar to
that of GCA-2000 for the same flight: radar registration began only after the
aircraft approached the Deblin radar area and ended
during the return toward Radom.
Fig. 8. Flight
trajectory recorded by the GPS and AVIA-W radar systems for the second flight
(schematic distance annotations; quantitative registration-boundary distances
are given in Table 2)
Figure 9 showed the relationship between QFE-referenced aircraft
altitude H (QFE = 1008 hPa) and relative terrain
elevation for flights 3117 and 3121. The radar registration epochs were
compared with the terrain profile. The two vertical quantities were distinct: H
represented aircraft altitude, whereas relative terrain elevation characterized
the ground surface.
|
(a) |
|
|
(b) |
|
Fig. 9.
Relationship between QFE-referenced aircraft altitude and relative terrain
elevation: (a) flight 3117; (b) flight 3121
The altitude profiles in Figures 9a and 9b showed the takeoff, cruise,
approach, and landing phases. Radar observations were comparatively dense
during several approach and landing segments near the Deblin
airfield. Lower observation density and gaps occurred mainly during the initial
departure from Radom and in selected low-altitude or near-radar portions of the trajectories.
Consequently, missing observations were interpreted as segment-specific
features of these flights rather than as a general characteristic of aircraft
approaching the radars.
Figure 10 showed the spatial relationship between mapped natural and
artificial features and radar registration during flight 3117. The
radar-registration segments were color-coded by QFE-referenced aircraft
altitude H (QFE = 1008 hPa), with values up to
approximately 900 m. The color scale represented aircraft altitude rather than
the relative terrain elevation shown in Figure 3 and used 50-m classes over the
displayed range from −100 to 900 m.
|
(a) |
|
|
(b) |
|
|
Zone legend: (1)-(3) - GCA-2000 registration-gap zones; (1*)-(3*) -
corresponding AVIA-W registration-gap zones. |
|
Fig. 10. Maps
of natural and artificial objects relative to QFE-referenced aircraft altitude
for flight 3117: (a) GCA-2000; (b) AVIA-W; color scale: H (m), QFE = 1008 hPa, 50-m classes
In Figure 10a, GCA-2000 did not register data in zone (1), located near
the edge of a forested area. In Figure 10b, the first AVIA-W detection occurred
in zone (1*) over the same forest segment. This spatial coincidence was not
interpreted as an isolated forest effect because target aspect and RCS were not
recorded. GCA-2000 data were also absent in zone (2), whereas AVIA-W continued
to register the trajectory at point (2*). Registration interruptions occurred
near both radar locations (zones 3 and 3*), with a larger gap for GCA-2000.
These observations were treated as descriptive spatial associations;
quantifying a forest effect would require additional flights with target-aspect
and RCS information.
Figure 11 showed corresponding spatial registration patterns for flight
3121 for GCA-2000 (a) and AVIA-W (b). The mapped gaps and recoveries were
compared descriptively with terrain and infrastructure features.
|
(a) |
|
|
(b) |
|
|
Zone legend: (1)-(4) - GCA-2000 registration-gap zones; (1*)-(4*) -
corresponding AVIA-W registration-gap zones. |
|
Fig. 11. Maps
of natural and artificial objects relative to QFE-referenced aircraft altitude
for flight 3121: (a) GCA-2000; (b) AVIA-W; color scale: H (m), QFE = 1008 hPa, 50-m classes
In Figure 11a, a sudden loss of GCA-2000 registration occurred in the
central section of the flight, zone (1), at a QFE-referenced aircraft altitude
H of approximately 800-850 m. These values described aircraft altitude and were
distinct from the relative terrain elevation in Figure 3. AVIA-W showed more
continuous registration in zone (1*), although a partial gap remained. Zones
(2) and (2*) were similar to those observed during the first flight. In zones
(3) and (3*), both radar systems showed larger registration gaps than in flight
3117. Additional interruptions appeared in zone (4) for GCA-2000 and zone (4*)
for AVIA-W. The GCA-2000 interruption was more extensive, whereas AVIA-W
retained observations along most of the corresponding trajectory segment.
Because only two flights were available, these differences were reported
descriptively and were not attributed causally to route complexity or flight
duration.
Finally, the data collected during flights 3117 and 3121 were summarized
statistically for GCA-2000 and AVIA-W (Table 3). The temporal statistics
comprised the arithmetic mean and sample standard deviation (SD) of successive
registration intervals Δt, while the altitude
statistics comprised the corresponding mean and SD values of H. The SD of Δt quantified variability over the complete empirical
registration sequence, including longer gaps between recorded observations.
Tab.
3
Statistical
characteristics of the surveillance systems
|
Statistics |
Δt (sec) |
H (m) |
||||||
|
3117 |
3121 |
3117 |
3121 |
|||||
|
GCA-2000 |
AVIA-W |
GCA-2000 |
AVIA-W |
GCA-2000 |
AVIA-W |
GCA-2000 |
AVIA-W |
|
|
avg |
3.6 |
8.4 |
3.3 |
8.1 |
476 |
490 |
592 |
584 |
|
SD |
28.6 |
41.9 |
24.2 |
36.3 |
209 |
216 |
201 |
239 |
For flight 3117, the mean interval between successive GCA-2000 records
was 3.6 ± 28.6 s (mean ± SD), while AVIA-W yielded 8.4 ± 41.9 s. For flight
3121, the corresponding values were 3.3 ± 24.2 s for GCA-2000 and 8.1 ± 36.3 s
for AVIA-W. The SD values substantially exceeded the means because the complete
sequences included occasional long registration gaps; the mean interval alone
therefore did not demonstrate a stable or uniform update cadence. For GCA-2000,
the mean interval characterized the COMBINED-mode display record rather than
the refresh period of the ASR component alone. Mean flight altitudes were 476 ±
209 m (GCA-2000) and 490 ± 216 m (AVIA-W) for flight 3117, and 592 ± 201 m
(GCA-2000) and 584 ± 239 m (AVIA-W) for flight 3121.
The large SD values of Δt primarily reflected occasional long registration gaps included in the
complete empirical sequences rather than variability in the nominal radar
refresh periods. Therefore, the mean and SD values characterize the overall
temporal structure of the recorded sequences, including both regular
observations and longer interruptions, and should not be interpreted as direct
measures of update-rate stability. For GCA-2000, the observed mean inter-record
intervals characterize the COMBINED-mode display record and are not directly
comparable with the 5-second refresh characteristic of the ASR component alone.
Future analyses should distinguish inter-record intervals within continuous
tracking segments from longer registration gaps to characterize nominal refresh
behavior and interruption patterns separately.
4. CONCLUSIONS
This article presents
an analysis of the temporal sampling characteristics of the GCA-2000 and AVIA-W
radar systems in comparison with GPS, used as a reference system. The study is
based on two flights conducted under lowland terrain conditions, with consideration
of both natural and man-made obstacles. The focus is placed on evaluating the spatio-temporal characteristics of data registration of
data registration, the temporal variability of the radar records, and the
influence of external factors. The recalculated relative temporal sampling
ratios, determined only within the corresponding radar-GPS common temporal
windows, were 28.1% and 30.7% for GCA-2000 and 11.9% and 12.3% for AVIA-W for
flights 3117 and 3121, respectively. These values describe temporal sampling
density rather than total spatial coverage or detection efficiency. The mean ±
SD of successive registration intervals were 3.6 ± 28.6 s and 3.3 ± 24.2 s for
GCA-2000, and 8.4 ± 41.9 s and 8.1 ± 36.3 s for AVIA-W for flights 3117 and
3121, respectively. The large SD values reflect long registration gaps retained
in the complete sequences and show that the mean intervals alone do not
demonstrate uniform temporal stability. For GCA-2000, the 3-4 second mean value
characterizes the empirical COMBINED-mode display record and is not directly
comparable with the 5-second ASR refresh period considered separately. Spatial
analysis identified data-loss zones that spatially coincided with local terrain
variations and forested areas. However, no statistically significant
correlation or causal relationship was established within the present
two-flight dataset. The altitude standard deviations were 209 and 216 m for
GCA-2000 and AVIA-W, respectively, in flight 3117, and 201 and 239 m in flight
3121. Because only two flights were analyzed, the observed differences do not
establish a general causal relationship between route complexity, flight
duration, and radar registration performance. Within these two flights,
GCA-2000 showed a higher temporal sampling density and shorter mean
inter-record intervals than AVIA-W; these metrics describe only the analyzed
temporal behavior and do not constitute an overall ranking of the systems. No
independent quantitative acceptance criterion for regional monitoring was
defined in this study; therefore, the results are not used to classify either
radar as operationally “acceptable” or “suitable” for regional monitoring.
AVIA-W showed a lower mean temporal sampling frequency, and the present Δt statistics do not support describing its temporal
registration as uniformly more stable over the complete observation window.
These conclusions may be useful for improving data fusion algorithms and
optimizing coverage areas in radar-based airspace control systems. Future
research should involve a larger dataset of flight records under varying
terrain and weather conditions and should record target-aspect/RCS-related
variables, so that the observed spatial associations can be tested
quantitatively. The findings provide a basis for further adaptation of radar
observation systems to challenging operational environments and for improvement
of their processing algorithms. Such analyses can support identification of
zones with potential signal-loss risk. They can also inform the design of
hybrid radar-GNSS monitoring systems for tactical airspace surveillance and
support flight-trajectory reconstruction under the variable topographic
conditions typical of Central and Eastern Europe.
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Received 16.06.2026; accepted in
revised form 17.09.2026
Scientific
Journal of Silesian University of Technology. Series Transport is licensed
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[1] Department of Geodesy and Astronomy, Institute of
Geodesy, Lviv Polytechnic National University, Bandera 12 Street, 79013 Lviv,
Ukraine. Email:
alina.v.fedorchuk@lpnu.ua. ORCID: https://orcid.org/0000-0003-0183-1132
[2] Department of Navigation and Air Traffic Management,
Institute of Navigation, Polish Air Force University, Dywizjonu
303 35 Street, 08-521 Deblin, Poland. Email: a.gos@law.mil.pl. ORCID:
https://orcid.org/0000-0002-4268-8830