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6.5 DATA FUSION 6.5.1 Quantitative Performance Criteria 6.5.1.1 Spatial Alignment for Fusion between Two Radars [SD3.1] Objective Fusion is the process of combining tracks generated by independent radars with overlapping coverage into common tracks. We designed Performance Criterion SD3.1, Spatial Alignment for Fusion Between Two Radars, to demonstrate that two asynchronous radars can be spatially aligned within acceptable error to provide meaningful target data for fusion. The association process of data fusion looks at how well a pair of tracks, suspected to be from the same target, match. Fusing target tracks from two independent radars with overlapping coverage requires alignment in two spatial dimensions (range and azimuth), and one temporal dimension. The combined misalignment in these three dimensions for a pair of tracks must be relatively small in order to successfully associate the separate tracks with the same target. Several IVAR study locations have two operating radars with overlapping coverage, including: SEA, NASWI, and MCASCP. We reviewed recorded tracks from these three pairs of radars in order to identify periods of time when they were tracking the same targets. Our goal was to identify commonly tracked targets (at least one for each radar pair) in order to compute the spatial misalignment errors for each. In order to successfully demonstrate Performance Criterion SD3.1, the misalignment error for each pair of radar tracks must be less than three times the a priori spatial uncertainty (Appendix B defines how the a priori spatial uncertainty is computed). Methods We selected SEA, NASWI, and MCASCP as the IVAR locations to be used in this demonstration. From this set of three we chose the following radar pairs with overlapping coverage: • The SEAAR1m and SEAAR2u at SEA. • The eBirdRad (or WIeBirdRad) and WIAR1 at NASWI. • The MCASCP eBirdRad and a second eBirdRad unit that were both used in an exercise north of MCASCP. We reviewed the target data from each site and selected three datasets. The location and time period of each dataset are summarized in Table 6-33. Table 6-33. Tracking events selected for demonstration of spatial alignment. Location NASWI SEA MCASP Time Period Nov 13 2009, 01:54 – 02:10 GMT Oct 6 2009, 21:15 – 21:37 GMT Feb 14 2006, 20:03 – 20:15 GMT 232
We acquired the appropriate plots and tracks files from the pairs of radars for the specified time periods. We then used the TVW application to view datasets and identify commonly-tracked targets. Finally, we recorded the corresponding track IDs for each target (i.e., both radars had associated separate, unique track IDs with the targets that were common to both). Next we loaded the plots and tracks files into the TDV application to identify appropriate tracks export them to both kml and text formats for comparison. The target tracks were plotted in Google Earth, which facilitated visual comparison of the spatial alignment of the pair of targets. Figure 6-103 is a screen capture of the selected target from SEA, displayed in Google Earth. The tracks are color-coded according to their respective radar, the locations of which are also shown in the figure. Figure 6-103. Track histories of target selected from SEAAR1l and SEAAR1u radars at SEA dataset for spatial alignment analysis. The green symbols represent the targets from SEAAR2u and the red represent SEARR1m. 233
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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
Page 205 and 206: Figure 6-72. TVW application displa
Page 207 and 208: Conclusion Figure 6-74. Oblique vie
Page 209 and 210: Archived Data to a GIS We used the
Page 211 and 212: (SQL). Extracting target data from
Page 213 and 214: Figure 6-78. Daily, unfiltered, tra
Page 215 and 216: Figure 6-79. Time periods selected
Page 217 and 218: Figure 6-82. Hourly track count ove
Page 219 and 220: esults are displayed in Figure 6-83
Page 221 and 222: As can be seen in Figure 6-83, the
Page 223 and 224: Figure 6-84 clearly demonstrates th
Page 225 and 226: Conclusion We have successfully dem
Page 227 and 228: LAN within a facility to the operat
Page 229 and 230: All reference times were recorded i
Page 231 and 232: Description of Remote Display - As
Page 233 and 234: mounted laptop computer (Figure 6-8
Page 235 and 236: very good because the system was sa
Page 237 and 238: operations personnel or their contr
Page 239 and 240: Figure 6-90. Alarm status pane and
Page 241 and 242: Figure 6-92. Alarm triggered as a f
Page 243 and 244: Figure 6-95. Screen capture of the
Page 245 and 246: Figure 6-96. An example from a data
Page 247 and 248: Table 6-29. Differences found betwe
Page 249 and 250: 6.4 DATA INTEGRATION 6.4.1 Quantita
Page 251 and 252: Figure 6-100. Screen captures for S
Page 253 and 254: We used a screen-capture program at
Page 255: Results Table 6-32 records the time
Page 259 and 260: Figure 6-105. Track histories of ta
Page 261 and 262: synchronized, both spatially and te
Page 263 and 264: 0.004687 seconds ahead of the NTP t
Page 265 and 266: Figure 6-108: COP display showing f
Page 267 and 268: Figure 6-110: TVW display showing t
Page 269 and 270: The next example from NASWI involve
Page 271 and 272: Figure 6-114: TVW display showing t
Page 273 and 274: Figure 6-116: COP display showing a
Page 275 and 276: Fusion processing was enabled in th
Page 277 and 278: processing associated with this dat
Page 279 and 280: Figure 6-122: Track IDs shown for b
Page 281 and 282: Conclusion We successfully demonstr
Page 283 and 284: Of course, many other factors contr
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Page 287 and 288: Methods The US military 27 designat
Page 289 and 290: Natural R.esoan:es Br.mch 22541Jolm
Page 291 and 292: Given these considerations, the IVA
Page 293 and 294: Results No maintenance was required
Page 295 and 296: acquired and is testing JRC S-band
Page 297 and 298: areas of overlap. Data fusion algor
Page 299 and 300: 6.8 FUNCTIONAL REQUIREMENTS AND PER
Page 301 and 302: Table 7-1. Cost Model for a Standal
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Page 305 and 306: 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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