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METHOD #5: AUTOMATIC TRACKING OF UNMANNED AERIAL VEHICLES (UAV). Remote controlled helicopters (RCH) can be used to validate that the automatic tracking coordinates produced by the digital radar processor (DRP) used in eBirdRad and the AR-1 and AR-2 radars are accurate within sensor resolution limits. By placing a GPS onboard the RCH, the coordinates of the RCH during flight tests can be recorded and compared subsequently to radar tracks of the RCH produced by the radar. The method can be described as follows: 1. Fly the RCH within the coverage zone of the radar and at detectible ranges recording the helicopter's trajectory with onboard GPS 2. Operate the radar to track RCH - record raw data for subsequent reprocessing 3. Extract helicopter trajectory coordinates from GPS and provide in suitable format 4. Extract helicopter radar track coordinates by reprocessing raw radar data and outputting helicopter tracks 5. Compare radar track coordinates and GPS coordinates by overlaying onto common graph or display 6. Analyze radar and GPS coordinates and assess accuracy given sensor resolution limits Achieving (1) above is perhaps the most challenging part of the method. An RCH is typically small, and if it is flown at any significant distance from the radar (say 1-2 km), then the radar operator will likely not be able to see the helicopter. It is also difficult for the RCH pilot to estimate the height of the RCH needed to ensure that the helicopter is flying in the radar beam. As a result, it can be quite challenging for the radar operator and pilot to ensure that the helicopter remains in the radar beam for sufficient time to collect meaningful data. This is particularly true at locations such as commercial airports (e.g., Seattle Tacoma International Airport) where the radar location is fixed, and where authorizations to fly the RCH are provided only at certain locations. Recording raw data throughout the test ensures that whenever the helicopter was in the beam of the radar (to be confirmed off-line through analyses of the GPS coordinates), the radar data is available for comparison. If the RCH dynamics can be reasonably controlled to mimic birds in flight, and if the RCH remains in the beam for sufficient periods of time, then radar tracks can be compared directly against the GPS coordinates; otherwise, radar detections can be compared against GPS coordinates. 330
METHOD #6: DEMONSTRATING REAL-TIME DATA FUSION. Method #6 is intended to demonstrate that in spite of the fact that any pair of avian radars are asynchronous temporally (they have independent time sources) and spatially (they rotate independently and asynchronously and are displaced in space), they can be reasonably aligned so as to provide meaningful improvements when their radar tracks are combined through integration or fusion. Spatial Alignment Procedure (SD3.1) 1. Use a pair of radars with overlapping coverage. 2. Review recorded track datasets using the TrackViewer’s playback feature to identify at least three timeframes where one or more targets were tracked simultaneously by both radars for a duration of at least 10 track updates (i.e., a minimum of 30 pair-wise comparisons of target positions between the two radars); note the TrackIDs of the respective track pairs. 3. Extract the respective track pairs as follows: Load the track data from each radar into the TrackDataViewer. For each radar, select the identified tracks from each of the three selected timeframes using the TrackID and export the updates for these tracks into a spreadsheet using the export feature of the TrackDataViewer. Also export the same data into a KML format for visual comparisons of the track pairs in GoogleEarth. 4. Organize the respective track pairs into adjacent columns of the spreadsheet so that their spatial positions can be easily compared. 5. Compute the signed distance between each pair of spatial positions of the target(s) from the two radars. The spatial misalignment error is the absolute value of the mean of these paired differences. 6. Define the a priori spatial uncertainty as the sum of: a) The range-resolution of the radar (use the larger value if the radars are different); b) The maximum cross-range-resolution for the track pair (taken as the maximum of the point-wise range*azimuth-beamwidth); and c) The maximum-speed of the target (taken from the track pair) multiplied by twice the scan-time uncertainty of 5 seconds. 7. If the spatial misalignment error is
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FINAL REPORT Integration and Valida
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Table of Contents List of Tables ..
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APPENDIX A. POINTS OF CONTACT .....
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Table 6-17. Altitudinal distributio
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List of Figures Figure 2-1. Chronol
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Figure 6-17. Plot of the northing c
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Figure 6-42. eBirdRad display based
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Figure 6-82. Hourly track count ove
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Figure 6-115: COP display showing f
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List of Acronyms Acronym AGL AIP AR
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Acronym RST RT SEA SQL SSC Pacific
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Carmen Lombardo Naval Air Station,
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EXECUTIVE SUMMARY Military bases an
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database and redistributed to other
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1 INTRODUCTION 1.1 BACKGROUND Encro
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A seventh objective, the Functional
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Table 1-1 (cont.). IVAR TASK/ OBJEC
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environmental stewardship with miss
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2 TECHNOLOGY/METHODOLOGY DESCRIPTIO
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The original BirdRad system was des
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2.4 TECHNOLOGY/METHODOLOGY DEVELOPM
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Figure 2-2. Configuration of BirdRa
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2.4.3 eBirdRad - Digital Radars wit
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Figure 2-5. Interior view of the eB
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accuracy and target continuity over
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permanently installed at SEA. Prior
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2.5 ADVANTAGES AND LIMITATIONS OF T
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3 PERFORMANCE OBJECTIVES Table 3-1
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Table 3-1 (cont.). Performance Obje
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For the third test, Validation Usin
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temporal domain by recording all bi
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3.1.2.2.7 Provides Bird Abundance o
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difference in the time (i.e., the l
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3.1.6.2.4 Maintenance [SE4.2] We es
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Table 4-1. Summary of the IVAR stud
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4.1 MCAS CHERRY POINT Marine Corps
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2006 the primary eBirdRad unit from
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(directional WiFi) to the Internet.
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• Large (several thousand) flocks
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location for demonstrating the capa
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5.1 CONCEPTUAL TEST DESIGN 5 TEST D
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5.3 DESIGN AND LAYOUT OF TECHNOLOGY
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Assuming these lines evidence confi
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that dataset [Section 6.5.1.3]. 5.5
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6 PERFORMANCE ASSESSMENT The Perfor
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ecord the time, Track ID, and other
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Figure 6-1. Percentage of targets c
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Comparing the performance of dish v
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Figure 6-4. The basic sensor elemen
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Figure 6-6. The polygon around the
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Figure 6-8. Diagram of sampling “
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• It takes several scans (nominal
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Session Correla tions 600 500 y = 1
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To evaluate the capabilities of the
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Figure 6-15. Runway 3 at SEA, showi
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comparisons between the RCH-GPS dat
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Figure 6-18. Plot of the northing c
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Figure 6-20. Plot of the easting co
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Conclusion In the quantitative perf
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Table 6-8. Column headings of the T
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Table 6-9 demonstrates quantitative
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Table 6-10. The parameters of targe
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Table 6-11. The position of Visual
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Table 6-12. Solitary targets at MCA
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Figure 6-23. The track of target T2
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intended to demonstrate these syste
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Figure 6-34. Screen capture of bird
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6.1.2 Qualitative Performance Crite
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upper right have completely converg
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Figure 6-38. The progression of the
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The slight difference in alignment
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Figure 6-42. eBirdRad display based
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Figure 6-44. A partial history of t
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6.2 SAMPLING PROTOCOL 6.2.1 Quantit
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Figure 6-46. Image from the track h
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Table 6-14. Dates, times and names
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Figure 6-48. Fifteen-minute (06:04-
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Figure 6-50. Fifteen-minute (02:24-
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We used a TVW to process the plots
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Figure 6-53. In an example of night
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6.2.1.4 Efficiently Stores Bird Tra
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Figure 6-56 shows sites 13-24 in th
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We extracted the radar data for thi
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Figure 6-57. Digital image of the d
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Table 6-17. Altitudinal distributio
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hours apart, showed considerable va
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• The sampling event time period
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underwent a 4-minute initialization
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Figure 6-62. DRP Live application l
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commands we issued to the radar; th
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4. Range Decrease (x7) 5. Range Inc
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Figure 6-65, Figure 6-66, and Figur
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6.2.2.4 Provides Spatial Distributi
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Figure 6-69. A plot of all track ac
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Figure 6-71. Twelve 2-hour historie
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Figure 6-72. TVW application displa
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Conclusion Figure 6-74. Oblique vie
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Archived Data to a GIS We used the
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(SQL). Extracting target data from
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Figure 6-78. Daily, unfiltered, tra
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Figure 6-79. Time periods selected
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Figure 6-82. Hourly track count ove
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esults are displayed in Figure 6-83
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As can be seen in Figure 6-83, the
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Figure 6-84 clearly demonstrates th
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Conclusion We have successfully dem
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LAN within a facility to the operat
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All reference times were recorded i
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Description of Remote Display - As
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mounted laptop computer (Figure 6-8
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very good because the system was sa
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operations personnel or their contr
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Figure 6-90. Alarm status pane and
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Figure 6-92. Alarm triggered as a f
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Figure 6-95. Screen capture of the
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Figure 6-96. An example from a data
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Table 6-29. Differences found betwe
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6.4 DATA INTEGRATION 6.4.1 Quantita
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Figure 6-100. Screen captures for S
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We used a screen-capture program at
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Results Table 6-32 records the time
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We acquired the appropriate plots a
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Figure 6-105. Track histories of ta
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synchronized, both spatially and te
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0.004687 seconds ahead of the NTP t
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Figure 6-108: COP display showing f
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Figure 6-110: TVW display showing t
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The next example from NASWI involve
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Figure 6-114: TVW display showing t
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Figure 6-116: COP display showing a
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Fusion processing was enabled in th
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processing associated with this dat
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Figure 6-122: Track IDs shown for b
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Conclusion We successfully demonstr
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Of course, many other factors contr
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one, Jim Swift, had been exposed to
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Methods The US military 27 designat
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Natural R.esoan:es Br.mch 22541Jolm
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Given these considerations, the IVA
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Results No maintenance was required
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acquired and is testing JRC S-band
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areas of overlap. Data fusion algor
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6.8 FUNCTIONAL REQUIREMENTS AND PER
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Table 7-1. Cost Model for a Standal
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Page 311 and 312: Network Connectivity. The US Depart
Page 313 and 314: Airport Improvement Program (AIP) f
Page 315 and 316: 9 REFERENCES Anderson, C., and S. O
Page 317 and 318: APPENDIX A. POINTS OF CONTACT POINT
Page 319 and 320: POINT OF CONTACT Name Ryan King Mat
Page 321 and 322: APPENDIX B. ANALYTICAL METHODS SUPP
Page 323 and 324: METHOD #2: VALIDATION BY IMAGE COMP
Page 325 and 326: The pattern of yellows and blues in
Page 327 and 328: Radar Team (RT) Site Selection Sinc
Page 329 and 330: will measured the magnetic bearing
Page 331 and 332: Figure B-6. Using a GPS to record s
Page 333 and 334: • Visual Observer - observes bird
Page 335 and 336: Figure B-9. Flowchart of target con
Page 337 and 338: VT: “Radar, Team 2-Alpha copies T
Page 339 and 340: eam and the VTs have difficulty loc
Page 341 and 342: why it was changed, who made the ch
Page 343 and 344: METHOD #4: CONFIRMATION OF BIRD TAR
Page 345 and 346: Figure B-12. Duplexed image showing
Page 347 and 348: Table B-2. Dates, times, and antenn
Page 349 and 350: For the analysis of the simultaneou
Page 351 and 352: Table B-3. Altitude of the radar be
Page 353: Figure B-17. Diagram of sampling
Page 357 and 358: APPENDIX C. DATA QUALITY ASSURANCE/
Page 359 and 360: APPENDIX D. SUPPORTING DATA D.1 Par
Page 361 and 362: Table D-1 (cont.). Date Track ID Up
Page 367 and 368: D.2 Listing of One-Hour Track Histo
Page 369 and 370: Table D-2 (cont.). 07:00-08:00 WI_A
Page 371 and 372: APPENDIX E. SURVEY OF AIRPORT PERSO
Page 373 and 374: used as the real-time target becaus
Page 375 and 376: Table E-1. Remote radar display lat
Page 377: 7. Please rate the following where
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