Effects of Environmental Variability on an XBand Phased Array Radar: AREPS Case Study

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Effects of Environmental Variability on an X-Band
Phased Array Radar AREPS Case Study

• LCDR Thomas Keefer
• OC3570
• 08SEP2006

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Motivation

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

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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??

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Procedure

• EXAMINE AREA OF OPERATIONS
• 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

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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

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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

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Characterizing the Environment

• Ideally, measured onboard
• Not available
• Composite of information a good substitute
• Bulk Parameters needed for evaporative duct
  height prediction available from ship

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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

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Temp
Illustrative only NOT TO SCALE
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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
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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
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Composite Profile

• Upper Air Profile Derived from Oakland Sounding

Z

• 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
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• 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

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• 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

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• 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

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Develop a Product
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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
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Calculation of Radar Horizon
Need to sum the radar horizon for the system
and the target
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Sensitivity Testing

• provide a prediction of radar ranges
• Assess accuracy and confidence
• Using 2 composite profiles with their lower
  atmosphere derived from the upper and lower
  bounds of the data recorded on the Pt Sur, AREPS
  runs were conducted
• A third AREPS run was done using standard
  atmosphere for comparison

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Environment Comparisons
Env 1 evap ducting less likely Env 2 evap
ducting more likely
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AREPS is Sensitive to Radar/TGT

• Power output and Gain
• Target RCS critical
• Sea State/Intel
• Assumed good and held constant

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RV Pt Sur Radar Cross Sect.

• Power density return is a function of RCS
• No definitive value for Pt Sur
• Used 2000m2
• Varies greatly by beam vs bow vs stern aspect
• Sea State can silhouette or shade the target

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AREPS Environment 1
50 Probability of Detection likely to occur at
20.5km 100 Probability of Detection Likely to
occur at 15km
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AREPS Environment 2
50 Probability of Detection likely to occur at
24km 100 Probability of Detection Likely to
occur at 16km
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AREPS Std Atmosphere
50 Probability of Detection likely to occur at
20km 100 Probability of Detection Likely to
occur at 14km
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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!!!

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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

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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

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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

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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

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Acknowledgments

• Peter Guest
• Ken Davidson
• Jeff Knorr
• Paul Buczynski

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Questions?