(IVAR) - Final Report - Strategic Environmental Research and ...

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at several geographic locations and at different times of the year to sample differences in species abundance, physiography, weather conditions, etc. The best judge of whether a target being tracked by a radar is a bird is a human observing the same target visually. On the other hand, several members of the IVAR team who participated in previous informal “visual confirmation” studies reported these observations were subject to a long list of confounding factors: the short period of time the bird may be in the radar beam; not knowing the precise location of the radar beam relative to the observer; the difficulty of judging distances when looking up at the sky; differences in the bird-to-background contrast due to the bird’s coloration, weather conditions, light levels, sun angle; the fact that radar can detect birds at far greater distances than the human eye, even with the aid of binoculars or a spotting scope. Based on these considerations, the IVAR team chose to use multiple ground-truthing methods and the different lines of evidence the produce to confirm the targets the digital radar tracks are birds. These ground-truthing methods included: Visual Confirmation – at four geographic locations and both during the spring and fall, deploy teams of visual observers at various ranges and azimuths from the radar during the daylight hours and ask them to confirm visually whether a target that was being tracked by the radar was a (flock of) bird [Section 6.1.1.1.1]. Thermal Confirmation – use a thermal imager, which can detect the heat signatures of biological targets, to confirm that the targets tracked by both the imager and the radar at night are birds [Section 6.1.1.1.2]. Unmanned Aerial Vehicle (UAV) – fly a remote-control helicopter with a recording GPS in the radar beam to simulate the flight path of a bird and then compare the spatial coordinates from the GPS with those of the avian radar [Section 6.1.1.1.3]. Synthetic Targets – input software-generated target data, with known attributes and flight dynamics, to the radar’s DRP and compare the radar tracks with the programmed characteristics of the targets [Section 6.1.2.1]. Image Comparisons – capture screen images of the same scene generated by an analog avian radar and a digital avian radar to determine how closely the digital renderings of the scene match those of the analog display [Section 6.1.2.1]. Table 5-1 lists the locations, types, and dates of the field tests conducted to demonstrate the automatic tracking capabilities of the avian radar systems deployed at these facilities. Table 5-1. Dates and types of field tests of automatic tracking. Location Test Type Study Dates MCAS Cherry Point, North Carolina Visual Mar-Apr 2007 Oct 2008 NAS Patuxent River, Maryland Visual Apr 2007 Sep 2008 NAS Whidbey Island, Washington Visual Apr 2007 Sep 2008 Elmendorf AFB, Alaska Visual Aug 2008 Edisto Island, South Carolina Thermal Oct 2008 Sea-Tac International Airport, Washington UAV Sep 2008 61
Assuming these lines evidence confirmed that digital avian radars can automatically track birds, we devised the following tests to demonstrate the additional capabilities and benefits of the automatic real-time tracking capabilities of the digital avian radar systems evaluated by the IVAR project: Parametric Data – document the types of parametric data, particularly the 4D spatialtemporal coordinates, digital avian radars can generate for each tracked target during each scan of the radar [Section 6.1.1.2]. Number of Simultaneous Targets – demonstrate the software can simultaneously track a realistic number of targets [Section 6.1.1.3]. Representative Ranges to Targets – confirm that the radar system can sample targets over distances that would include most military bases and ranges [Section 6.1.1.4] and beyond [Section 6.1.1.5]. Sampling Protocols Before avian radar systems can be used to augment other methods of sampling birds, it was necessary to demonstrate that they can operate in the locales and under the conditions in which they would be expected to perform. Specifically, we designed tests to demonstrate that the digital avian radar systems IVAR evaluated could: Coverage – in addition to sampling over ranges that would encompass most military facilities (see above), sample through 360° of azimuth [Section 6.2.1.2], up to altitudes at which birds are known to fly [Section 6.2.1.6], out to the “near” distances (i.e., 11 km) [Section 6.2.1.3], and do so continuously night and day [Section 6.2.1.1]. Storage – record on conventional COTS mass storage devices the amount of data generated during these periods of continuous operations [Section 6.2.1.4]. Sample More Birds – count more birds than a conventional sampling method during the same time period, plus count more birds overall because the avian radar can sample 24/7, while most conventional sampling methods cannot. Data Streaming Having established that the digital avian radars the IVAR project evaluated could automatically detect and track birds under typical operating conditions, we then devised a series of tests to demonstrate that those data can be reliably delivered where they are needed, in near-real time, and in a form that has value for the end-users. These tests were designed to: Network Availability – demonstrate that the availability of wireless [Section 6.3.1.7] and wired [Section 6.3.1.2] LANs and wired WANs [Section 6.3.1.6] is high enough both within and between locations to deliver the requisite plots & tracks data for local, remote, and historical applications. Network Reliability – demonstrate that the data can be delivered intact to the end-user applications [Section 6.3.1.1]. Storage & Redistribution – demonstrate that the data can be streamed to a database and redistributed in near-real time to other users and applications without little or no loss in data quality [Section 6.3.1.3]. 62
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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
Page 39 and 40: The original BirdRad system was des
Page 41 and 42: 2.4 TECHNOLOGY/METHODOLOGY DEVELOPM
Page 43 and 44: Figure 2-2. Configuration of BirdRa
Page 45 and 46: 2.4.3 eBirdRad - Digital Radars wit
Page 47 and 48: Figure 2-5. Interior view of the eB
Page 49 and 50: accuracy and target continuity over
Page 51 and 52: permanently installed at SEA. Prior
Page 53 and 54: 2.5 ADVANTAGES AND LIMITATIONS OF T
Page 55 and 56: 3 PERFORMANCE OBJECTIVES Table 3-1
Page 57 and 58: Table 3-1 (cont.). Performance Obje
Page 65 and 66: For the third test, Validation Usin
Page 67 and 68: temporal domain by recording all bi
Page 69 and 70: 3.1.2.2.7 Provides Bird Abundance o
Page 71 and 72: difference in the time (i.e., the l
Page 73 and 74: 3.1.6.2.4 Maintenance [SE4.2] We es
Page 75 and 76: Table 4-1. Summary of the IVAR stud
Page 77 and 78: 4.1 MCAS CHERRY POINT Marine Corps
Page 79 and 80: 2006 the primary eBirdRad unit from
Page 81 and 82: (directional WiFi) to the Internet.
Page 83 and 84: • Large (several thousand) flocks
Page 85 and 86: location for demonstrating the capa
Page 87 and 88: 5.1 CONCEPTUAL TEST DESIGN 5 TEST D
Page 89: 5.3 DESIGN AND LAYOUT OF TECHNOLOGY
Page 93 and 94: that dataset [Section 6.5.1.3]. 5.5
Page 95 and 96: 6 PERFORMANCE ASSESSMENT The Perfor
Page 97 and 98: ecord the time, Track ID, and other
Page 99 and 100: Figure 6-1. Percentage of targets c
Page 101 and 102: Comparing the performance of dish v
Page 103 and 104: Figure 6-4. The basic sensor elemen
Page 105 and 106: Figure 6-6. The polygon around the
Page 107 and 108: Figure 6-8. Diagram of sampling “
Page 109 and 110: • It takes several scans (nominal
Page 111 and 112: Session Correla tions 600 500 y = 1
Page 113 and 114: To evaluate the capabilities of the
Page 115 and 116: Figure 6-15. Runway 3 at SEA, showi
Page 117 and 118: comparisons between the RCH-GPS dat
Page 119 and 120: Figure 6-18. Plot of the northing c
Page 121 and 122: Figure 6-20. Plot of the easting co
Page 123 and 124: Conclusion In the quantitative perf
Page 125 and 126: Table 6-8. Column headings of the T
Page 127 and 128: Table 6-9 demonstrates quantitative
Page 129 and 130: Table 6-10. The parameters of targe
Page 131 and 132: Table 6-11. The position of Visual
Page 133 and 134: Table 6-12. Solitary targets at MCA
Page 135 and 136: Figure 6-23. The track of target T2
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Figure 6-29. The track of target T5
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Figure 6-31. 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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additional processors account of th
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Installation Site Assessment - Rada
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e incurred, depending on what infor
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eal time, avian radars and can dete
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Network Connectivity. The US Depart
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Airport Improvement Program (AIP) f
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9 REFERENCES Anderson, C., and S. O
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APPENDIX A. POINTS OF CONTACT POINT
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POINT OF CONTACT Name Ryan King Mat
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APPENDIX B. ANALYTICAL METHODS SUPP
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METHOD #2: VALIDATION BY IMAGE COMP
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The pattern of yellows and blues in
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Radar Team (RT) Site Selection Sinc
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will measured the magnetic bearing
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Figure B-6. Using a GPS to record s
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• Visual Observer - observes bird
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Figure B-9. Flowchart of target con
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VT: “Radar, Team 2-Alpha copies T
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eam and the VTs have difficulty loc
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why it was changed, who made the ch
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METHOD #4: CONFIRMATION OF BIRD TAR
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Figure B-12. Duplexed image showing
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Table B-2. Dates, times, and antenn
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For the analysis of the simultaneou
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Table B-3. Altitude of the radar be
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Figure B-17. Diagram of sampling
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METHOD #6: DEMONSTRATING REAL-TIME
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APPENDIX C. DATA QUALITY ASSURANCE/
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APPENDIX D. SUPPORTING DATA D.1 Par
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Table D-1 (cont.). Date Track ID Up
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D.2 Listing of One-Hour Track Histo
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Table D-2 (cont.). 07:00-08:00 WI_A
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APPENDIX E. SURVEY OF AIRPORT PERSO
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used as the real-time target becaus
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Table E-1. Remote radar display lat
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7. Please rate the following where
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