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1 CLUTTER MITIGATION DECISION (CMD) THEORY AND PROBLEM DIAGNOSIS RADAR MONITORING WORKSHOP ERAD 2010 SIBIU ROMANIA Mike Dixon National Center for Atmospheric.

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Presentation on theme: "1 CLUTTER MITIGATION DECISION (CMD) THEORY AND PROBLEM DIAGNOSIS RADAR MONITORING WORKSHOP ERAD 2010 SIBIU ROMANIA Mike Dixon National Center for Atmospheric."— Presentation transcript:

1 1 CLUTTER MITIGATION DECISION (CMD) THEORY AND PROBLEM DIAGNOSIS RADAR MONITORING WORKSHOP ERAD 2010 SIBIU ROMANIA Mike Dixon National Center for Atmospheric Research Boulder, Colorado

2 2 Why do we need to know where clutter is in order to filter it effectively?  We need to filter normal-propagation (NP) clutter and anomalous- propagation (AP) clutter  The filters used remove weather power in some circumstances: –Velocity close to 0 –Spectrum width close to 0  These conditions occur both in: –Clutter –Stratiform precipitation  We therefore need a technique which identifies likely locations of clutter  The filter is applied only at those gates with a high probability of clutter.

3 3 Problem - weather and clutter combined Reflectivity plot

4 4 Problem - weather and clutter combined Velocity plot

5 5 What happens if we filter everywhere? Filtered reflectivity – applying clutter filter everywhere Weather power removed

6 6 And if we use CMD? Filtered reflectivity using CMD

7 7 Combined clutter and weather. Weather and clutter are distinct.

8 8 Combined clutter and weather spectrum. Weather and clutter overlap somewhat.

9 9 Combined clutter/weather spectrum. Weather and clutter overlap completely.

10 10 Notch filter – removes weather information

11 11 Adaptive filter – does not remove weather Adaptive filter – does not remove weather

12 12 Adaptive filter has a difficult time when clutter and weather overlap

13 13 Doppler Radar CMD

14 14 Motivation  Adaptive spectral clutter filters show great promise for intelligently filtering clutter power while leaving weather power largely unaffected.  However, these filters still remove weather power under the following circumstances: –the weather return has a velocity close to zero; –the weather return has a narrow spectrum width.  This tends to occur with stratiform weather in the region of the zero isodop.  In order to mitigate the problem, information other than that used by the filters must be used to determine whether clutter exists at a gate.

15 15 Principal feature fields for CMD  In order to identify gates with clutter, we use a number of so-called feature fields. These contain information which is independent of that used by the clutter filter.  The feature fields used in the latest CMD version are: –The TEXTURE of the reflectivity field – TDBZ. –The SPIN of the reflectivity field. This is a measure of how often the reflectivity gradient changes sign. –The Clutter Phase Alignment or CPA, which is a measure of the pulse-to-pulse stability of the returned signal.

16 16 TEXTURE of reflectivity - TDBZ  TDBZ is computed as the mean of the squared reflectivity difference between adjacent gates.  TDBZ is computed at each gate along the radial, with the computation centered on the gate of interest.  TDBZ at a gate is computed using the dBZ values for the 4 gates on either side of the gate of interest. Computed for this gate Using data from these gates

17 17 TDBZ feature field DBZ TDBZ Example of TDBZ – Denver Front Range NEXRAD - KFTG

18 18 Reflectivity SPIN For a point at which a gradient sign change occurs, let x be the reflectivity change from the previous gate and y be the reflectivity change to the next gate. Then SPIN CHANGE = (|x| + |y|) / 2 x y x y x y

19 19 SPIN feature field DBZ SPIN Example of SPIN – Denver Front Range NEXRAD – KFTG (SPIN is noisy in low SNR regions)

20 20 Clutter Phase Alignment - CPA  In clutter, the phase of each pulse in the time series for a particular gate is almost constant since the clutter does not move much and is at a constant distance from the radar.  In noise, the phase from pulse to pulse is random.  In weather, the phase from pulse to pulse will vary depending on the velocity of the targets within the illumination volume.

21 21 I,Q data is a complex number I Q phase power

22 22 CPA – theoretical phasor diagrams

23 23 CPA feature field  CPA is computed as the length of the cumulative phasor vector, divided by the sum of the power for each pulse.  CPA is computed at a single gate.  It is a normalized value, ranging from 0 to 1.  In clutter, CPA is typically above 0.9.  In weather, CPA is often close to 0, but increases in weather with a velocity close to 0 and a narrow spectrum width.  In noise, CPA is typically less than  CPA was originally developed as a quality control field for clutter targets used for refractivity measurements.

24 24 CPA feature field DBZ CPA Example of CPA – Denver Front Range NEXRAD - KFTG

25 25 Combining TDBZ, SPIN and CPA  The individual feature fields, TDBZ, SPIN and CPA, are combined into a single interest field using fuzzy logic.  First, each feature field is converted into an interest field, using a membership transfer function.  Interest fields have a range from 0.0 to 1.0.  The interest fields are assigned a weight.  The combined interest field is computed as a weighted mean of the individual interest fields.

26 26 Steps in computing the single-pol CMD  Step 1: –Compute TDBZ and TDBZ-interest –Compute SPIN and SPIN-interest –Compute CPA and CPA-interest  Step 2: –Compute Texture-interest = maximum of (TDBZ-interest, SPIN- interest)  Step 3: –Compute CMD value = fuzzy combination of CPA-interest and Texture-interest  Compute CMD flag: true if CMD >= 0.5, false if CMD = 0.5, false if CMD < 0.5

27 27 Membership functions for single-pol CMD -> (15,0) -> (30,1)

28 28 Membership function combination as used in single-pol CMD -> (15,0) -> (30,1) Weight=1.01 Weight=1.0

29 29 Creating combined interest field - CMD TDBZ SPIN CPA CMD

30 30 Logic for setting the clutter flag 1. SNR > 3dB? 3. CMD > 0.5? 3. Set Flag.

31 31 EXAMPLE OF APPLYING CMD

32 32 KFTG 2006/10/26, 1200 UTC Reflectivity

33 33 VELOCITY, WIDTH Radial velocitySpectrum width

34 34 TDBZ, SPIN TDBZSPIN

35 35 CPA, CMD CPACMD

36 36 Clutter flag CMD flag

37 37 Unfiltered reflectivity

38 38 Filtered reflectivity

39 39 DIAGNOSING ERRORS

40 40 THERE ARE 3 MAIN ERROR TYPES 1. FALSE DETECTIONS: the algorithm detects clutter incorrectly, so that the filter is applied excessively. This is particularly problematic when it occurs in the region of 0 velocity, since the filter cannot distinguish between clutter and weather. 2. MISSED DETECTIONS: the algorithm fails to identify clutter, and the filter is therefore not applied where it should be. 3. FILTER FAILURE: the CMD algorithm works correctly, but the filter fails to work effectively.

41 41 ERROR TYPE 1: FALSE DETECTIONS This is the most common error type.

42 42 Example 1 - Filtered DBZ Filtered reflectivity using CMD Filtered everywhere

43 43 Example 1 – VELOCITY and WIDTH Radial velocitySpectrum width

44 44 Example 2 : Filtered DBZ Filtered using CMD Filtered everywhere Weather power removed

45 45 Example 2 : VEL, filtered VEL Velocity Filtered velocity

46 46 Example 3 : KFTG 2006/10/10, 1000 UTC Filtered using CMD Filtered everywhere Weather power removed

47 47 Example 3 : VEL, filtered VEL Velocity Filtered velocity

48 48 Example 4 : KFTG 2006/10/09, 2200 UTC Filtered using CMD Filtered everywhere Weather power removed

49 49 Example 4 : VEL, filtered VEL Velocity Filtered velocity

50 50 Example 4 : Operational NEXRAD Filtered reflectivity Filtered velocity

51 51 ERROR TYPE 2: MISSED DETECTIONS It was found that in some clutter regions, CPA values can be lower than expected.

52 52 KEMX reflectivity 2009/04/22 21:22 UTC Also showing counties, interstates and US highways We will investigate the region indicated by the ellipse

53 53 Unfiltered reflectivity

54 54 Filtered reflectivity Ellipses highlight missed detections

55 55 Max interest for TDBZ/SPIN

56 56 Region 1 – CPA This region has CPA values with considerable variability, with some values in the clutter region being as low as 0.2.

57 57 Characteristics of the CPA field  It was noted that in some of the clutter regions, there are a considerable number of gates with low CPA values. The CPA values can vary from high to low in adjacent gates.  Examining the phase time series for these gates shows that the phase can change significantly over a small part of the time series.  It is probable that 2 separate targets are illuminated during the dwell.  This phase change reduces CPA.  However, for the remainder of the time series the phase is stable.

58 58 Example - spectrum of clutter point with low CPA CPA = 0.19 A-scope X-axis: range (km) Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Phase time series X-axis: time Pulse-to-pulse phase Difference time series X-axis: time Magenta line shows range Change in phase for part of the time series leads to low CPA value

59 59 ERROR TYPE 3: FILTER DOES NOT WORK EFFECTIVELY The clutter filter can have problems dealing with multi-mode spectra.

60 60 Traffic clutter spectra  It appears that some of the missed detections and poor clutter filter performance are caused by traffic echoes.  The following slides show spectra from normal clutter echoes and suspected traffic echoes.  The traffic echoes exhibit multi-modal spectra. This makes them both difficult to detect as clutter, and difficult to filter with the current adaptive filters.

61 61 Unfiltered reflectivity Note interstate 10 – shown in bold – traversing this area, and smaller roads

62 62 Filtered reflectivity showing gates at which CMD and the clutter filter failed Ellipse high-lights gates for which CMD and/or the clutter filter failed

63 63 Normal-propagation clutter signature CPA = 0.88 A-scope X-axis: range (km) Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Phase time series X-axis: time Pulse-to-pulse phase difference time series X-axis: time Magenta line shows range

64 64 Spectrum of suspected traffic targets CPA = 0.17 A-scope X-axis: range (km) Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Phase time series X-axis: time Pulse-to-pulse phase difference time series X-axis: time Magenta line shows range Multi-modal spectrum

65 65 Spectrum of suspected traffic targets CPA = 0.30 A-scope X-axis: range (km) Red – unfiltered spectrum X-axis: samples Pink – filtered spectrum Power time series X-axis: time Phase time series X-axis: time Pulse-to-pulse phase difference time series X-axis: time Magenta line shows range Multi-modal spectrum

66 66 THANK YOU


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