1. LCDR Thomas Keefer OC3570 08SEP2006
Effects of Environmental Variability on an X-Band Phased Array Radar: AREPS Case Study
LCDR Thomas Keefer
OC3570
08SEP2006

2. Steadily increasing focus on littoral MIW and NSW operations
Motivation
Steadily increasing focus on littoral
MIW and NSW operations
Range Prediction problematic
environmental factors
Confidence?

3. Project Goals Track Pt Sur using X-Band Radar
Gather data set
Good simulation of a medium sized craft
Run AREPS using 3 environmental profiles
Standard, More Permissive, Less Permissive
Compare and discuss observed and predicted detection ranges
Gain insight into the confidence level of range prediction in a littoral environment
Is the product high quality or dangerous??

4. Procedure EXAMINE AREA OF OPERATIONS DEVELOP A PRODUCT
gather data from multiple sources
Characterize the environment
Profile the ‘target’ and the sensor
DEVELOP A PRODUCT
Calculate Radar Horizon
Run AREPS on 3 different cases
Determine confidence in the product
COMPARE PRODUCT TO OBSERVATIONS
Discuss product limitation/inaccuracy

5. Background/Literature Review
The range of a radar system is limited by the curvature of the earth (ignoring ducting), antenna height, power and RCS of target
Ducting regularly occurs over the ocean
In order to determine the presence of ducts, ‘M Profiles’ are particularly useful
Normally occur when Rh decreases rapidly with height or Temp increases rapidly with height
Evaporation duct formation is favorable over the ocean due to humidity change

6. Data Set Description 12z and 24z Oakland, CA soundings
Fort Ord Profiler for 12z and 24z
Tair, RH%, Wind Speed/Direction, SST and Atmospheric Pressure measured onboard
Several SST measurements available
Radar Output

7. Gather Data
Ideally, the profile used to predict radar ranges would be available at multiple points between the radar and target
Since this data was not available, a composite profile was created
This profile was assumed to represent the entire path from sensor to target Z
Temp
* Illustrative only: NOT TO SCALE

8. Gather Data
This assumption, while not truly accurate, leads to a good first guess.
Good starting point
Allows sensitivity tests
Sensitivity is a tool to evaluate product confidence Z
Temp
* Illustrative only: NOT TO SCALE

9. Gather Data
Using various sensors to put together a best guess is always possible.
Composite could be an operational necessity. Z
Temp
* Illustrative only: NOT TO SCALE

10. Composite Profile Upper Air Profile: Derived from Oakland SoundingZ
Inversion: Bottom and Top parameters derived from Ft Ord Profiler (Constant Lapse rate assumed between)
Lower height is critical
Lower Profile: Input of ship bulk parameters and match to bottom of inversion
Evap Duct done via param.
Temp
* Illustrative only: NOT TO SCALE

11. Does the data indicate a fairly homogenous profile through the AOR?
How do the humidity, SST and Tair combine to give an idea of duct presence
Modified Ref. Index

12. Not Homogenous!!

13. Degrades confidence in model output and therefore prediction
AREPS output can be discussed and adjusted to suit risk tolerance of warfighter
SST and Tair Sensitivity tests are needed based on data gathered in AOR

14. Examine Surface, elevated and evaporative ducting
Best tool is the “M Profile”
For this case, Sfc and elevated ducts are unlikely : Modified Refract. Index is always ‘+’
Evap ducting possible

15. Develop a Product

16. Calculation of Radar Horizon
R = “Radar Horizon”
Re = Earths Radius
H = height of antenna
*4/3 Earth Approximation is an empirical effort to capture the refractive effects of a standard atmosphere and neglects RCS and Power of Radar

17. Calculation of Radar Horizon
* Need to sum the radar horizon for the system and the target

18. Environment Comparisons
RH% 97 95
SST (C)
16.0
16.5
Tair 14
13.5
Wind spd 4
Env 1 = evap ducting less likely; Env 2 = evap ducting more likely

19. AREPS is Sensitive to Radar/TGT
Power output and Gain
Target RCS critical
Sea State/Intel
Assumed good and held constant

20. RV Pt Sur: Radar Cross Sect.
Power density return is a function of RCS
No definitive value for Pt Sur
Used 2000m^2
Varies greatly by beam vs bow vs stern aspect
Sea State can ‘silhouette or ‘shade’ the target

21. AREPS: Environment 1
50% Probability of Detection likely to occur at 20.5km
100% Probability of Detection Likely to occur at 15km

22. AREPS: Environment 2
50% Probability of Detection likely to occur at 24km
100% Probability of Detection Likely to occur at 16km

23. AREPS: Std Atmosphere
50% Probability of Detection likely to occur at 20km
100% Probability of Detection Likely to occur at 14km

24. Results of Radar Tracking
Acquired on an inbound run with coordination
First run was outbound:
100% Prob. Of Detection to 29.5km
Second run was inbound:
100% Prob of Detection at 27.5km
Ducting has certainly occurred: 100% POD range doubled from predicted!!!

25. Product Usefulness
Given the extreme variability in the local environment, confidence should have been expressed as fairly low
Under prediction, even for the conservative case, can be considered marginally successful provided confidence is discussed
Model very sensitive to SST and RH%
RCS of target is highly variable and critical
Sensors are rarely coincident with AOR
Sensors may give erroneous readings…

26. Sensitivity Discussion: Temp
Evaporation duct height is highly sensitive to SST
SST was measured 5 diff. ways onboard
IR with the gun
Bucket
Sea chest injection

27. Conclusions Radar range prediction a function of many input variables
differing sensitivities
Evaluating the confidence in the prediction is as important, yet likely not as popular, as the actual prediction itself
The confidence in your prediction should be based on an assessment of the data quality and the environmental variability over the AOR and should include a discussion on the sensitivity of the most influential variables

28. Future Research Construct fall-off slide from existing data
Not yet available
Correlate Sea Swell to RCS
Noticed cyclical fluctuation in power density returned over short period

29. Peter Guest Ken Davidson Jeff Knorr Paul Buczynski
Acknowledgments
Peter Guest
Ken Davidson
Jeff Knorr
Paul Buczynski

30. Questions?