Presentation on theme: "CLUTTER MITIGATION DECISION (CMD) THEORY AND PROBLEM DIAGNOSIS"— Presentation transcript:
1CLUTTER MITIGATION DECISION (CMD) THEORY AND PROBLEM DIAGNOSIS RADAR MONITORING WORKSHOPERAD 2010SIBIU ROMANIAMike DixonNational Center for Atmospheric Research Boulder, Colorado
2Why 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) clutterThe filters used remove weather power in some circumstances:Velocity close to 0Spectrum width close to 0These conditions occur both in:ClutterStratiform precipitationWe therefore need a technique which identifies likely locations of clutterThe filter is applied only at those gates with a high probability of clutter.
3Problem - weather and clutter combined Reflectivity plot
4Problem - weather and clutter combined Velocity plot
5What happens if we filter everywhere? Weather power removedFiltered reflectivity – applying clutter filter everywhere
6And if we use CMD?Filtered reflectivity using CMD
7Combined clutter and weather. Weather and clutter are distinct.
8Combined clutter and weather spectrum Combined clutter and weather spectrum. Weather and clutter overlap somewhat.
14MotivationAdaptive 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.
15Principal 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.
16TEXTURE 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 gateUsing data from these gates
17TDBZ feature field Example of TDBZ – Denver Front Range NEXRAD - KFTG
18Reflectivity SPINxyFor a point at which a gradient sign change occurs, let x be the reflectivitychange from the previous gate and y be the reflectivity change to thenext gate.ThenSPIN CHANGE = (|x| + |y|) / 2
19SPIN feature fieldExample of SPIN – Denver Front Range NEXRAD – KFTG (SPIN is noisy in low SNR regions)SPINDBZ
20Clutter 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.
23CPA feature fieldCPA 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 0.05.CPA was originally developed as a quality control field for clutter targets used for refractivity measurements.
24Example of CPA – Denver Front Range NEXRAD - KFTG CPA feature fieldExample of CPA – Denver Front Range NEXRAD - KFTGCPADBZ
25Combining 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.
26Steps in computing the single-pol CMD Compute TDBZ and TDBZ-interestCompute SPIN and SPIN-interestCompute CPA and CPA-interestStep 2:Compute Texture-interest = maximum of (TDBZ-interest, SPIN-interest)Step 3:Compute CMD value = fuzzy combination of CPA-interest and Texture-interestCompute CMD flag: true if CMD >= 0.5, false if CMD < 0.5
27Membership functions for single-pol CMD -> (30,1)-> (15,0)
28Membership function combination as used in single-pol CMD Weight=1.0-> (30,1)-> (15,0)Weight=1.01
29Creating combined interest field - CMD TDBZSPINCMDCPA
30Logic for setting the clutter flag 3.CMD> 0.5?1. SNR > 3dB?3. SetFlag.
40THERE ARE 3 MAIN ERROR TYPES 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.MISSED DETECTIONS: the algorithm fails to identify clutter, and the filter is therefore not applied where it should be.FILTER FAILURE: the CMD algorithm works correctly, but the filter fails to work effectively.
41This is the most common error type. FALSE DETECTIONSThis is the most common error type.
56Region 1 – CPAThis region has CPA values with considerable variability, with some values in the clutter region being as low as 0.2.
57Characteristics 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.
58Example - spectrum of clutter point with low CPA CPA = 0.19 A-scope X-axis: range (km)Magenta line shows rangeRed – unfiltered spectrum X-axis: samplesPink – filtered spectrumPower time seriesX-axis: timePhase time seriesX-axis: timePulse-to-pulse phaseDifference time seriesX-axis: timeChange in phase for part of the time series leads to low CPA value
59ERROR TYPE 3: FILTER DOES NOT WORK EFFECTIVELY The clutter filter can have problems dealing with multi-mode spectra.
60Traffic 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.
61Unfiltered reflectivity Note interstate 10 – shown in bold – traversing this area,and smaller roads
62Filtered reflectivity showing gates at which CMD and the clutter filter failed Ellipse high-lights gates for which CMD and/or the clutter filter failed
63Normal-propagation clutter signature CPA = 0.88 A-scope X-axis: range (km)Magenta line shows rangeRed – unfiltered spectrum X-axis: samplesPink – filtered spectrumPower time seriesX-axis: timePhase time seriesX-axis: timePulse-to-pulse phasedifference time seriesX-axis: time
64Spectrum of suspected traffic targets CPA = 0.17 A-scope X-axis: range (km)Magenta line shows rangeRed – unfiltered spectrum X-axis: samplesPink – filtered spectrumPower time seriesX-axis: timeMulti-modal spectrumPhase time seriesX-axis: timePulse-to-pulse phasedifference time seriesX-axis: time
65Spectrum of suspected traffic targets CPA = 0.30 A-scope X-axis: range (km)Magenta line shows rangeRed – unfiltered spectrum X-axis: samplesPink – filtered spectrumPower time seriesX-axis: timeMulti-modal spectrumPhase time seriesX-axis: timePulse-to-pulse phasedifference time seriesX-axis: time