real-time detection and filtering of chaff clutter from

Real-Time Detection and Filtering of Chaff Clutter from Single-PolarizationDoppler Radar Data

YONG HYUN KIM AND SUNGSHIN KIM

Department of Electronics and Electrical Engineering, Pusan National University, Busan, South Korea

HYE-YOUNG HAN, BOK-HAENG HEO, AND CHEOL-HWAN YOU

Korea Meteorological Administration, Seoul, South Korea

(Manuscript received 1 August 2012, in final form 26 November 2012)

ABSTRACT

In countries with frequent aerial military exercises, chaff particles that are routinely spread by military

aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost-

effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler

radar readings in order to enhance the reliability of observed meteorological data. The proposed quality

control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image

elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification

and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure

were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteoro-

logical Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of

thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were re-

trieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision

support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated

from one another with no overlapping areas.

1. Introduction

The network of ground-based weather radars is an

essential tool for real-time monitoring of rapidly de-

veloping weather events, and in assessing the near-term

potential threat level posed by these events. Thus, the

ability to accurately forecast the course of weather

events critically depends on the reliability of measure-

ments coming from these sources. However, weather

radar readings are subject to nonweather noise, such as

normal ground (NG), anomalous propagation (AP),

chaff, contrail, and hazardous clutter sources (fire, ash

plumes). These sources are often confused with weather

echoes, and significantly compromise the reliability of

radar data. In particular, chaff clutter represents one of

the main noise sources among nonweather noise echoes

in countries with frequent aerial military exercises.

a. Problem posed by chaff echoes and theproposed approach

Chaff is a radar countermeasure commonly com-

posed of metallized glass fibers or other lightweight

strips of reflecting material that are released by aircraft

to distract radar-guided missiles from their targets. The

chaff bands are very shallow and tend to show a distinct

vertical tilt from the vertical viewpoint after a given

amount of time has elapsed from the chaff’s initial re-

lease. In a country such as South Korea where very

frequent aerial military exercises are held in a relatively

confined air space, chaff clutters with a sizeable volume

and coverage area (several hundred square kilometers)

were observed on 231 out of 365 days in 2010 (see

Fig. 1). This represents 63.3% of the total observation

days (Han et al. 2011). These dispersed nonweather

agents easily extend to several tens of kilometers over

a large area. They make it very difficult to obtain reli-

able weather condition estimates in an affected area in

the hours following the exercise—chaff remains air-

borne for roughly 8–15 h.

Corresponding author address: Sungshin Kim, Department of

Electronics andElectrical Engineering, PusanNationalUniversity,

Jangjeon-dong, Geumjeong-gu, Busan 609-735, South Korea.

E-mail: [email protected]

MAY 2013 K IM ET AL . 873

DOI: 10.1175/JTECH-D-12-00158.1

� 2013 American Meteorological SocietyUnauthenticated | Downloaded 01/03/22 05:58 AM UTC

As a result, cheap and robust methods for identifying

and removing chaff echoes from radar data are being

actively investigated by national weather forecasting

agencies in a number of countries in order to improve

the quality of meteorological data. However, difficulty

arises from the fact that with a current nationwide network

of single-polarization-basedC- and S-bandDoppler radars

installed in the majority of affected weather observation

centers, residual chaff clutter is not easily distinguished

from weather echoes because of their overlapping reflec-

tivity range.

In an attempt to solve this problem, analyses of time

series 3D radar constant altitude plan position indicator

(CAPPI) data are being conducted in this study to iden-

tify key discriminating characteristics that can differen-

tiate chaff echoes from weather echoes to a reasonable

degree of accuracy.

b. Previous works on tracking amorphousweather objects

There are several algorithms described in the current

literature that are designed to automatically track de-

formable weather structures (usually storm cells). They

include a spatial relaxation-labeling algorithm (Barnard

and Thompson 1980) that uses disparity analysis to de-

termine the correspondence between a set of feature

points selected from a stereogram, and a temporal re-

laxation algorithm (Zhang 1991) that tracks Euclidean

storm centers over time and handles the merging and

splitting of storms by allowing single storms to bematched

to several storms in previous or subsequent image frames.

Krezeski et al. (1994) improved Zhang’s tracking algo-

rithm by adding the concept of pseudostorms and property

coherence, which allows multiple features of a storm

(such as average intensity, storm size, velocity variance,

storm shape, and orientation) to be tracked over time

in addition to the storm center’s location. Johnson et al.

(1998) used an enhanced Weather Surveillance Radar-

1988 Doppler (WSR-88D) centroid tracking algorithm

called StormCell Identification and Tracking (SCIT) to

track and identify storm cells (isolated, clustered, or

line storms) at various levels of maximum threshold

reflectivity.

c. Previous works on chaff characterization

Previous works on chaff characterization are pri-

marily concerned with monostatic and bistatic radar

cross sections (RCSs). For monostatic RCSs, Marcus

(2004) developed a chaff model based on aerodynamic

principles that determines density and orientation dis-

tributions of reflective fibers within chaff clutters. For

bistatic RCSs, Guo and €Uberall (1992) proposed a

variational method of computing radar-scattering cross

sections for a bistatic-scattering cross section for chaff

dipoles.

The terminal chaff fall velocity was calculated by es-

timating a chaff drag coefficient as described by Jiusto

and Eadie (1963). Vasiloff and Struthwolf (1997) briefly

detailed the time-evolution characteristics of chaff echoes

and their dissipation pattern within convective cloud cells,

resulting in inaccurate estimation of the amount of pre-

cipitation for observed cloud structures. Through the

analysis of the chaff’s polarimetric properties, Zrni�c and

FIG. 1. KMA’s 2010 record of monthly variation of the number of

days that chaff echoes are observed. (bottom) Number of days that

chaff echoes were observed, and (top) their percentage of the total

number of days in each month (readapted from Han et al. 2011).

FIG. 2. The network of ground-based radars used by the KMA.

874 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 30

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Ryzhkov (2004) concluded that polarimetric radars could

provide a simple and effective way to detect chaff based

on experimental evidence obtained with circularly polar-

ized radars.

d. Previous works on identification of various typesof radar clutters

In the past several years, various techniques have been

proposed on radar data quality control (QC). Many of

these studies were primarily concerned with the detec-

tion of NG and AP clutter (Pratte et al. 1993; Pamment

and Conway 1998; Grecu and Krajewski 1999; Kessinger

et al. 1999; Steiner and Smith 2002; Berenguer et al. 2006;

Cho et al. 2006; Lakshmanan et al. 2007; Hubbert et al.

2009), while others were concerned with the detection of

less routinely occurring hazardous clutter, such as fire

plumes (Rogers and Brown 1977; Melnikov et al. 2008)

and volcanic ash clutter (Marzano et al. 2006). Many of

these detection techniques adopt in some way fuzzy in-

ference (FI) or neural network (NN)-based clutter dis-

crimination schemes at the range gate level of one or

more radar moments (reflectivity, velocity, spectrum

width, etc.). In addition, many of these studies apply

a more holistic and crisp approach of filtering data using

radarmoment thresholding during the preprocessing and

postprocessing QC stages.

2. System overview

a. Radar data used

The Korea Meteorological Administration (KMA)

primarily collects weather information from 11 ground-

based radar observation centers with a combined coverage

area large enough to cover the entire South Korean pen-

insula, as illustrated in Fig. 2. Three of these centers—

Baengyeongdo, Youngjongdo, and Myeonbongsan—use

single-polarization C-band Doppler radars. Eight other

centers use single-polarization S-band Doppler radars.

Theobservation range for each site varies between 200 and

256 km. Radars at each site feature 10–16 elevation angles

for each sweep and generate 3D radar volume data in

universal format (UF) at 10-min intervals. The radar vol-

ume data used within UF data structures for the QC pro-

cedure are ground echoes-removed (CZ) reflectivity data

that are the by-product of Gaussian model adaptive pro-

cessing (GMAP) and infinite impulse response (IIR) fil-

tering (Siggia and Passarelli 2004). From these volume

data, 3D CAPPI data with 1-km horizontal and 0.25-km

vertical resolution are constructed for an altitude range of

0.25–10 km. Our study features two test simulation cases

dated 3 and 15 June 2010 at the Oseongsan (KSN) site

that were chosen among 116 cases representing different

sample dates, times, and sites between 2010 and 2011.

FIG. 3. Proposed CEDR QC algorithm.



Since weather events are monitored in real time at

10-min intervals, the QC procedure for all 11 sites must

be completed within the short time frame of 10 min or

less. The algorithm thus takes into account this time re-

striction and limited computational resources that impose

significant constraints on the choice of QC model and

representing procedures.

b. Proposed chaff echo detection and removal(CEDR) QC algorithm

In the proposedQC algorithm, illustrated in Fig. 3, the

original time series radar data undergo a series of

transformations, sorting, and image element regrouping

procedures (these procedures apply to all time series

images or frames within a user-defined time period) be-

fore being subjected to the image element tracking and

inferring procedures designed to detect and remove chaff

signatures.

As a first step, the algorithm performs a coordinate

system transformation of the original site-specific reflec-

tivity volume data from polar to Euclidean coordinates

before subjecting these data to an image decomposition

(data partitioning) procedure. This transformation is

achieved by retrieving interpolated 2D CAPPI layers

from original CZ reflectivity volume data for regularly

spaced altitudes. The layers are then stacked one upon

FIG. 4. 2D example of NS clustering procedure with hypersphere (dotted square) scan radius of 1 grid distance resulting in three clusters.



the other, thereby resulting in 3D Euclidean reflectivity

(corrected) volume data (LE).

Subsequently, a preliminary data sorting procedure is

applied to all individual data points constituting this newly

created volume data. The procedure categorizes these data

into two reflectivity range groups: below-threshold and

above-threshold reflectivity groups. Threshold reflectivity

Zth is chosen between a 230- and 5-dBZ value range

(the default is 0 dBZ); typical chaff clutters exhibit re-

flectivity within a 10- and 35-dBZ value range. This step

can dramatically speed up the overall tracking procedure

by limiting the clustering procedure only to data points

in the selected group. The output of this sorting pro-

cedure is a set of 3-tuple data points (x, y, z) that has

reflectivity values above a threshold minimum. This

sparse set (B) and the 3D Euclidean reflectivity volume

data (LE) are then passed as inputs to the spatial clus-

tering procedure.

The spatial clustering procedure decomposes the sparse

set B (sparse in the sense that B is selected from larger

input dataset) into multiple subsets or partitions by a dis-

tance proximity criteria. Spatial clustering used in this

model does not require specifying the number of clusters

a priori. Resulting partitions from this procedure form

individual cluster sets containing spatially connected or

neighboring data points. Once the spatial clustering pro-

cedure that is repeated for all time series images within

a specified inference time period is completed, in order

to further reduce the time taken by the overall tracking

algorithm, the size of each cluster (the cardinality of the

cluster set) formed from the previous step is computed.

Clusters only in selected size categories are chosen for the

temporal clustering procedure, which associates identical or

similar clusters between time series images. The output of

the temporal clustering procedure forms an isocluster

subspace where member elements are 2-tuple cluster

identifiers (t, k) for clusters in different time frames that

are determined to be similar by criteria of cluster size and

cluster centroid location (t denotes a time frame index and

k denotes cluster ID). The establishment of the corre-

spondence between clusters in subsequent 3D radar im-

ages enables the retrieval of clusters’ time-evolution

characteristic profiles, provided that the number of asso-

ciated (linked) clusters between image frames is above

a threshold minimum (Rth). Otherwise, the inference sys-

tem for target cluster identification is not applied to these

disjoint normal clusters.

Retrievable dynamic characteristic profiles include

a cluster’s time-dependent altitude (the z component of

the cluster centroid), its size expansion (the volume or

projection area) profile, and the mean reflectivity pro-

file. The static characteristic profiles include the long-

ness factor (the degree to which a cluster looks long and

thin) for a fixed time frame.

Regardless of whether a cluster being probed is a chaff

cluster, it is then inferred based on these characteristic

profiles. We note that among these retrievable profiles,

a cluster’s time-dependent altitude profile, from which a

particle’s altitude-averaged fall velocity can be calcu-

lated, is of particular importance. This is because a clus-

ter’s structure can be directly inferred from this profile

because of the correlation that exists between the size of

particles making up the cluster and the particles’ time-

averaged fall velocity (Mitchell 1996; Jiusto and Eadie

1963; Matrosov et al. 2002).

This means that by observing a cluster’s vertical move-

ment profile alone, one can determine with reasonable

confidence whether a cluster is chaff. Chaff clusters exhibit

gradually decreasing altitude levels, whereas most typical

weather-related clusters maintain uniform or a somewhat

irregularly fluctuating altitude level, provided that there is

no rapid storm surge situation or other situations that re-

sult in significant vertical air movement. Once chaff clut-

ters have been identified using the proposed inference

method, their coordinates undergo a transformation

TABLE 1. Simulation environment and parameters for NS

clustering.

Environment settings

and parameters Description and values

CPU Intel Core i5 M480 2.67 GHz

RAM 3072 MB

OS Linux Ubuntu 10.10

Radar polarization

type

Horizontal

Test radar site KSN (Oseongsan)

Size of LS R 3 u 3 f: 980 3 360 3 15

(total: 5 292 000 bytes)

Size of LE X 3 Y 3 Z: 480 3 480 3 41

(total: 9 446 400 bytes)

Resolution of LE X 3 Y 3 Z: 1 km 3 1 km 3 0.25 km

Zth 0 dBZ

Sth 190 grid data points or 47.5 km3

tk 2 grid distance

TABLE 2. Simulation time measurement of NS clustering.

Radar sample date

Total

clustering

time (s)

Average

clustering

time (s)

KSN 3 Jun 2010 (1000–1300 KST

at 10-min intervals for a total of

19 inference time frames)

7.79 0.41

KSN 15 Jun 2010 (1000–1300 KST

at 10-min intervals for a total

of 19 inference time frames)

6.27 0.33



FIG. 5. 2D and 3D radar image decomposition for KSN 1130 KST 3 Jun 2010 data: (left) top view and (right) side

angle view. (a) Full echoes. (b) Below-threshold reflectivity (,0 dBZ) echoes removed. (c) Below-threshold re-

flectivity (,0 dBZ) echoes and below-threshold size (,190 grid points or 47.5 km3) clusters removed.



procedure (back to polar coordinates) and are subse-

quently removed from the original LS reflectivity volume

data to generate chaff-removed LS data (LS0 ).

The proposed method has a limitation in its ability to

detect chaff echoes at an early stage of their development

because there are not enough data points fromwhich key

dynamic profiles can be extrapolated. The method also

has a limitation of when chaff clutter overlaps with other

weather echoes, since the mixed presence of chaff clutter

cannot be resolved or discriminated with CZ reflectivity

information alone, as previously stated. Hence, chaff

clutters have the best chance of being detected in clear-

sky conditions with no interfering amorphous weather

structures.

3. Spatial clustering of radar echo image elements

a. Description of neighborhood-scan (NS)clustering method

Various clustering techniques are used in remote sens-

ing applications and pattern recognition problems to per-

form image decomposition prior to a feature extraction

procedure. The clustering procedures essentially partition

a finite number of objects into a finite number of groups,

so that any two objects belonging to the same group are

more similar than those belonging to different groups. This

section describes theNS image data clusteringmethod that

immediately follows the step (in Fig. 3) that filters out the

above-threshold reflectivity data.

FIG. 6. 2D top view of radar image decomposition for KSN 1230 KST 15 Jun 2010 data. (a) Full echoes. (b) Below-

threshold reflectivity (,0 dBZ) echoes removed. (c) Below-threshold reflectivity (,0 dBZ) echoes and below

threshold size (,190 grid points or 47.5 km3) clusters removed. (d) Target normal cluster being probed (later

identified as chaff cluster).



The NS clustering method shown in Fig. 4 is loosely

based on the single-pass method or the leader method

(K€arkk€ainen and Fr€anti 2007), in that all elements (data

points) are treated as individual clusters at the start and

that at each iteration step, each element (data point) is

compared to clusters formed thus far, and is either added

to the closest cluster or is used to start (lead) a new cluster

if it is insufficiently close to any of the existing clusters.

However, the proposed method diverges from the single-

pass method in its implementation and time complexity.

The single-pass and NSmethods are ofO(n2) andO(Mn)

time complexity, respectively, where M is the number of

data points enclosed by a local neighborhood volume

space (simply termed hypersphere) to be scanned and n is

FIG. 7. Example of EL clustering procedure with T 5 6 resulting in six isocluster sets. Here, T is the total number of inference time

frames andNC is the number of the similarity comparisons performed at each step. Total number of similarity comparison performed in

this example is 49. Starting from the last time frame t6, each normal cluster in this frame is compared to normal clusters in the im-

mediately preceding time frame t5. Clusters with minimum dissimilarity are then linked together as a result of this comparison. Sub-

sequently, starting from linked cluster in t5, the same comparison procedure is repeated with clusters in the immediately preceding time

frame until a cluster is no longer able to find a matching cluster from the preceding time frame. Procedure then goes back with the

remaining clusters in t6, and the whole procedure is repeated until there are nomore clusters left with unassigned isocluster membership

in all inference time frames.



the total number of input data points for which location

similarity comparisons are performed.

In NS clustering, each element (data point) in the input

sparse set B, which points to a specific volume element in

LE, is checked once (with negligible processing time) for

its cluster membership. If no cluster membership is pre-

viously assigned to the element, then the element is used

to start a new cluster. For each element successively

added to the new cluster set, a neighborhood volume scan

is performed to detect and to add more neighboring data

points having reflectivity above a threshold minimum

(i.e.,.0 dBZ), provided that these neighboring data points

have not already been added to the cluster set (added or

flagged members are prevented from being added again).

Once no additional members are detected and added

to the current cluster set, the process moves to the next

cluster and the same procedure is repeated until all ele-

ments (data points) in the input sparse set B are processed.

The key feature of this algorithm is that the neigh-

borhood volume scan for the proximity check of above-

threshold reflectivity data elements is limited to data

points within the hypersphere and to data points that

have not yet been flagged (assigned a membership).

These key features dramatically improve the speed of

the clustering procedure.

b. Simulation result of NS clustering

To evaluate the performance of the NS clustering

method and to determine if it meets the execution time

requirement imposed by the QC system (50 s or less for

each incoming radar site data), the algorithm was run

for 19 successive time frames with the simulation envi-

ronment settings and parameters shown in Table 1. The

average clustering time per time frame was then calcu-

lated by taking the ratio of the total clustering time

taken by all frames to the number of frames. The result

is shown in Table 2.

The overall data partition or decomposition of a 3D

radar image that results from reflectivity range and size

range filtering is illustrated in Figs. 5 and 6.

4. Temporal clustering of radar echo imageelements

a. Description of evolution-linkage (EL)clustering method

The EL clustering procedure is in essence an object-

tracking procedure that associates similar clusters between

images to form an isocluster set, as defined in section 2b.

The tracking procedure is fundamentally based on the as-

sumption that it is substantially unlikely that a tracked ob-

ject will undergo large displacement within the relatively

small time window defined by the image frame rate. The

EL clustering procedure, an example of which is shown

TABLE 3. Simulation time measurement of EL clustering.

Radar sample date

Total

clustering

time (s)

KSN 3 Jun 2010 (1000–1300 KST at 10-min intervals

for a total of 19 inference time frames)

0.17

KSN 15 Jun 2010 (1000–1300 KST at 10-min intervals

for a total of 19 inference time frames)

0.23

FIG. 8. Two input fuzzy variables, d and h, in the antecedent with

their MFs to represent different degrees of the similarity between

two temporally separated clusters.

FIG. 9. ANFIS for the determination of the degree of similarity

between two temporally separated clusters. The zeroth layer of this

NN represents two fuzzy input variables: d and h. First layer repre-

sents linguistic values associated with each of these two input vari-

ables (IF-part). Second and third layers represent rules and norm

(AND-logic with attribution of weights for each rule). Fourth layer

represents the THEN-part of the rules where each rule makes their

own contribution to the overall output. Fifth and sixth layers repre-

sent weight normalization of the output and the final output itself.



FIG. 10. Time series images of selected target cluster for KSN 3 Jun 2010: (left) top view and

(right) side angle view for (a) 1010, (b) 1040, (c) 1110, and (d) 1210 KST.



in Fig. 7, uses the distance that separates clusters’ centroids

and clusters’ size difference (either volume or the planar

projection area difference) as the base criterion for a simi-

larity comparison. Other arbitrary features such as a mor-

phological feature or mean reflectivity can be used as

comparison criteria. However, for the simultaneous moni-

toring of many clusters at optimum frame rates, these fea-

tures could be redundant. The EL clustering procedure has

O[(T 2 1)(q2 1 q)/2] or simply O(q2) time complexity

(considering the worst-case scenario, where the cluster-

tracking process goes all the way to the first frame), where

q is the average count of normal clusters within each time

frame and T is the number of inference time frames.

The algorithm essentially makes linkage chains connect-

ing similar clusters (represented by dot-connected cells in

Fig. 7) in adjacent time frames until there are no more

similar clusters left to be connected (the clusters are tracked

as long as they do not undergo large displacement, size

expansion, or shrinkage). Linkages are formed starting

from the last time frame and continuing in reverse time

order. At each linkage junction, a selected cluster at

a particular time frame is compared with all clusters in

the immediately preceding time frame and the one that

most closely matches is linked. This process is repeated

until every cluster within the inference time period is

assigned an isocluster membership. No two clusters

within a single time frame will have the same isocluster

membership.

b. Similarity measure

Given two clusters C1, C2 in two adjacent time frames

and their respective centroids p1 and p2, the distance that

separates these two centroids is given by d 5 kp1 2 p2k.

The size difference between two clusters is de-

termined by h 5 1 2 minfV1, V2g/maxfV1, V2g, whereV1, V2 is the cluster’s respective volume (or planar

projection area). These two variables constitute fuzzy

variables with their membership functions (MFs), as

shown in Fig. 8.

Based on these two fuzzy variables, the following two-

input single-output Takagi–Sugeno–Kang (TSK) fuzzy

model with four rules is constructed:

R1: If d is small and h is negligible, then G5

�0:5f21

2 d1 0:5h 0, d,f21 d.f2

R2: If d is small and h is significant, then G5

�0:5f21

2 d1 0:5 0, d,f21 d.f2

R3: If d is large and h is negligible, then G5 0:51 0:5hR4: If d is large and h is significant, then G5 1,

where the small and large are two linguistic values at-

tributed to d representing small and large distance, and

the negligible and significant are two linguistic values

attributed to h representing the negligible and signifi-

cant size difference between clusters.

In this model, the output (G 2 [0, 1]) represents the

degree of dissimilarity between two clusters. The nearer

the degree is to its maximum unity value, the less similar

the two clusters are. Since each rule has a crisp output, the

overall output is obtained via the weighted average. The

preceding TSK fuzzy system can be represented as an

adaptive neuro–fuzzy inference system (ANFIS), as shown

in Fig. 9.

c. Simulation results of EL clustering

Performance tests of EL clustering were carried out

with same simulation environment settings and param-

eters shown in Table 1 for a period defined by 19 in-

ference time frames. KSN radar site data dated 3 June

and 15 June 2010 were used for the test simulation. The

TABLE 4. Notation used throughout section 5a for vertical

movement profile.

Symbol Description

t Time (s)

yc Chaff fall velocity (m s21)

yT Terminal chaff fall velocity (m s21)

mc Chaff particle mass (kg)

g Acceleration of a body within the earth’s

gravitational field (g 5 9.8 m s22)

rair Air density (kg m23)

rc Chaff density (kg m23)

fc Chaff cross-sectional area (m2)

kc Chaff drag coefficient

gR Reynolds number

dc Chaff dipole diameter (m)

mair Dynamic viscosity of air (s kg m21)

P Pressure (Pa)

T Temperature (K)

R Ideal gas constant (R 5 8.314 47 J mol21 K21)

mair Molar mass of dry air (mair 5 0.028 964 4 kg mol21)

z Temperature lapse rate (z 5 0.0065 K m21)

P0 Sea level standard atmospheric pressure

(P0 5 101.325 kPa)

T0 Sea level standard temperature (T0 5 288.15 K)

ha Altitude (km)

ap p/21



simulation time measurements of EL clustering are

shown in Table 3. The time-evolution images of a target

cluster, characteristics of which are yet to be determined,

are shown in Fig. 10.

5. Profiling of cluster characteristics

a. Vertical movement profile

Like any airborne solid body with a bulk density that

is greater than the air density, chaff particles with a typical

nominal density of rc 5 2.34 g cm23 or higher are char-

acterized by their gradual descent within the atmosphere.

Specific fall velocities are dependent on the air density,

air viscosity, particle density, and particle size. Although

chaff particles are made as light and small as possible

so they can remain in the air for a significant length of

time, they eventually fall to the ground. This is one key

characteristic that differentiates chaff clusters from other

typical weather clusters that maintain at a relatively con-

stant altitude under normal weather conditions.

Chaff dipoles, similar to any other particles in a fluid

environment, obey the following equation of motion

(the notations for variables used throughout section 5a

are shown in Table 4):

mc

dycdt

5mcg2 (1/2)kcfcrairy2c , (1)

where the chaff drag coefficient kc (Jiusto and Eadie

1963) is derived by assuming the chaff dipole to be

a horizontally oriented cylinder with infinite length and

kc is related to gR by the following power law:

kc5 jg2qR (0:5# gR # 10), (2)

where j 5 10.5 and q 5 0.63.

FIG. 11. Vertical movement profiles of target clusters (subsequently identified as chaff). (a) Mean altitude (left)

and fall velocity (right) profile of the centroid of the cluster in Fig. 10 for KSN 3 Jun 2010. (b)Mean altitude (left) and

fall velocity (right) profile of the centroid of the cluster in Fig. 6d for KSN 15 Jun 2010.



The Reynolds number gR is given by

gR 5 (m21air )rairdcyT . (3)

The terminal velocity of the chaff dipole can then be

derived from (1) to (3) as

yT 5 d1:2c (r0:3airm0:5air )

21(apgrc)0:7 . (4)

Note that the terminal velocity is the velocity attained

when dyc/dt becomes zero. Changing the thermody-

namic environment (the air becomes less dense at higher

altitude) requires that the air density be corrected for

pressure and temperature using the ideal gas law:

rair 5mairP/RT . (5)

The air density can also be related to altitude variation

by the following expressions:

rair5mairP0

RT

�12

zhaT0

�(mairg/Rz)

(6)

ha5 4:433 104�12

rairT

353:4

�0:19

. (7)

Terminal chaff fall velocities have previously been

measured as a function of air density, air viscosity (Jiusto

and Eadie 1963), and altitude for a fixed chaff dipole

density and diameter (i.e., yT takes values in the range

of 0.25–0.26 m s21 for rair ’ 0.8 kg m23, mair ’ 1.7 31025 s kg m21, T ’ 262 K, dc 5 28.4 mm, rc 52.34 g cm23, and ha ’ 4.0–6.0 km). The vertical move-

ment profiles of target clusters obtained using the

FIG. 12. Size expansion profiles of target clusters (subsequently identified as chaff). (a) Volume [(left) slope 51.33 3 104 km3 h21] and coverage area [(right) slope 5 3.33 3 103 km2 h21] profile of the cluster in Fig. 10 for

KSN 3 Jun 2010. (b) Volume [(left) slope for 1000–1220 KST interval 5 3.75 3 103 km3 h21] and coverage

area [(right) slope for 1000–1220 KST interval 5 1.35 3 103 km2 h21] profile of the cluster in Fig. 6d for KSN

15 Jun 2010.



proposed method are shown in Fig. 11. Here, the z

component of the cluster’s centroid is used to track its

vertical movement.

b. Size expansion profile

The size expansion profile of a given cluster, in es-

sence, provides information on the extent of the dis-

persion of the cluster’s constituent particles over time,

from which various deductions concerning the nature of

the cluster can be drawn. Unlike typical amorphous

cloud structures in a nonstorm surge situation that ir-

regularly undergo a succession of relatively mild ex-

pansion and contraction processes, chaff clusters are

characterized by their continually expanding volume (or

projection coverage area) from a very early stage of

their life cycles. Then, they slowly shrink in a final

collapsing stage as particles start to hit the ground in

increasing numbers.

The chaff dipoles are tiny cylindrical solid particles.

They are not subject to thermal expansion and con-

traction like air or water vapor molecules. Their con-

tinual expansion or spreading is mainly due to the simple

effect of gravity pulling them downward and the random

movement of air, which carries them around and dis-

perses them in all directions.

Two parameters that are alternatively used to de-

termine the size of a given cluster are the volume and the

projection coverage area. Here, the volume is the total

number of data points within cluster set C (the cardi-

nality of cluster set C) divided by a z-direction scaling

factor. The projection coverage area is the total number

of data points within the Cp set, which is defined as Cp5projU(C), where U is the 2D horizontal plane upon

which cluster C is projected.

The size expansion profiles of the target clusters fea-

tured in Figs. 10 and 6d are shown in Fig. 12.

c. Mean reflectivity profile and longness factor

Mean reflectivity time series profiles, as shown in Fig. 13

(featuring two selected clusters in Figs. 10 and 6d), can be

used to identify, within a reasonable accuracy, potential

candidate chaff clusters. This is because clusters composed

of tiny solid particles such as chaff characteristically exhibit

gradually decreasing mean reflectivity values due to their

decreasing bulk density as solid particles continually fall

and spread out in the air.

The longness factor is a measure of the degree to which

a given cluster looks long and thin when viewed from

the top. It is valued between 0 and 1, 1 being maximally

or infinitely long and thin, and 0 being the opposite. Al-

though the longness factor alone does not conclusively

tell whether a given cluster is chaff, it does weigh-in on

the identification process: chaff clusters tend to exhibit

a value in the upper half of the [0, 1] range. The geometry

and calculation procedure of the longness factor are shown

in Figs. 14 and 15, respectively. The resulting longness

factor profiles of the selected clusters featured in Figs. 10

and 6d are shown in Fig. 16.

6. Inference system for chaff echoes detection

a. Description of inference system

The inference system for the detection of chaff ech-

oes uses three dynamic characteristic parameters and

one static characteristic parameter as input variables.

FIG. 13. Mean reflectivity profiles of target clusters (sub-

sequently identified as chaff). (a) Mean reflectivity profile of the

cluster in Fig. 10 for KSN3 Jun 2010 (least squares slope520.0275

dBZ min21). (b) Mean reflectivity profile of the cluster in Fig. 6d

for KSN 15 Jun 2010 (least squares slope 5 20.0386 dBZ min21).



The three dynamic characteristic parameters are the

cluster’s fall trend index hf, volume-expansion trend

index he, and mean reflectivity trend index hr. The

static characteristic parameter includes only the long-

ness factor gk. The calculations of hf, he, and hr are

performed using the algorithms shown in Fig. 17. All

four parameters (hf, he, hr, and gk) have values in the

range of [0, 1].

Based on these input variables, we constructed a sim-

ple four-input single-output single-layer feed-forward

perceptron (Rosenblatt 1958) NN, as shown in Fig. 18.

A single-layer perceptron NN, which is similar to the

Adaline NN by Widrow and Hoff (1960), has an ad-

vantage over other NNmodels for its extreme simplicity

and relatively small number of computations required

for an inference and distributed online learning pro-

cedure that helps to speed up the CEDRQC procedure.

The output of a CEDR QC NN is the activation of a

weighted linear combination of the input parameters

(neurons), and is given by

FI 5F(Y)5F

�4

i51

Qixi �4

i51

Qi

!,

,

5F(q1 � hf 1 q2 � he 1 q3 � hr 1 q4 � gk) , (8)

where xi 2 [0, 1] are the input variables, andQi and qi 2[0, 1] are the individual weights and normalized in-

dividual weights associated with each input variable,

respectively. Note that the intermediate valueY also has

the range of [0, 1]. The final decision that segregates

chaff from nonchaff clusters is based on the following

activation function:

FI 5

�1 if Y .Yth

0 otherwise, (9)

where Yth is the threshold output level of the NN. If

the final output FI is 1, then the sample cluster will be

assigned to class chaff. If the final output is 0, then the

pattern sample cluster will be assigned to class nonchaff.

FIG. 14. (a) Projection operation of 3D cluster in 2D plane using KSN 1300 KST 3 Jun 2010

data. (b) Geometry for the calculation procedure of the longness factor.



FIG. 15. Flowchart of the longness factor calculation procedure. Algorithm simply calculates, in three rotated

frames (one along normal axis, two along diagonal axis), the ratio of the width (fraction of the unity) to the

length (always normalized to the unity) of rectangles enclosing a cluster.After theminimumof the three ratios is

subtracted from the unity, the algorithm takes this difference as the characteristic longness factor of the target

cluster.



NNonline (pattern by pattern) learning is achieved by

a weight and threshold output level adjustment at each

NN evaluation pattern using the perceptron learning

rule. Here, each jth pattern consists of a set ofL training

sample data points (at least 6 h of time-separated radar

data) for which the jth average detection performance

index PI,j (described in the next section) is calculated.

If PI,j is found to be less than the desired performance

index PID, which is set to 0.8 by default, then the new

value for each weight and threshold output level is

computed to add a correction to the old value in the

following way:

qij(t1 1)5qij(t)1Dqij(t) (10)

Yth,j(t1 1)5Yth,j(t)1DYth,j(t) . (11)

However, since there can be as many as 105 combi-

nations of weights and threshold output levels for each

pattern if Dqij and DYth,j are both set to 0.1 (as an ex-

ample), the convergence to optimally adjusted qi and

Yth can take considerable time and cannot feasibly be

implemented in the proposedQCoptimization procedure.

Thus, only heuristically selected combination sets of qiand Yth are considered for the optimization task (i.e.,

q1 5 0.4, q2 5 0.3, q3 5 0.2, q4 5 0.1, and Yth 5 0.6; q1 50.3, q2 5 0.3, q3 5 0.2, q4 5 0.2, and Yth 5 0.7; q1 5 0.5,

q2 5 0.2, q3 5 0.2, q4 5 0.1, and Yth 5 0.7; etc.).

The perceptron learning rule used for the training can

thus be stated as follows:

Given Hq 5 fc1, c2, . . . , cmg, a set of different com-

binations of qi and Yth (denoted cu) for the connections,

where m is the total number of combinations,

1) n ) 1; u ) 1; j ) 1.

2) Obtain input vectors x for different clusters from

nth sample (at time t) of jth pattern.

3) Select cu and compute FI,nj for different clusters of

nth sample of jth pattern.

4) Compute PI,nj.

5) n ) n 1 1.

6) If n # L (where L is the number of training sample

data points of jth pattern), then Goto step 2; else

Goto step 7.

7) Compute average PI,nj over n for jth pattern.

8) If average PI,nj $ PID, then Goto step 12; else Goto

step 9.

9) n ) 1; u ) u 1 1; j ) j 1 1.

10) If u # m, then Goto step 2; else Goto step 11.

11) Select cu for which average PI,nj is maximum

(converging point). Goto step 13.

12) Select cu as the converging point of qi and Yth

combinations.

13) End.

b. Simulation result of chaff echoes detection

The test simulation for chaff echo detection based on

the aforementioned NN system was carried out using

KSN radar site data dated 3 June 2010. The result is

shown in Fig. 19 and Table 5.

Each isocluster represented in Table 5 (see Fig. 19 for

matching isocluster locations) is generated by associat-

ing similar clusters between images in reverse time frame

order starting at 1300 until 1000 Korean standard time

FIG. 16. Longness factor profile of target clusters (subsequently

identified as chaff). (a) Longness factor profile of the cluster in

Fig. 10 for KSN 3 Jun 2010. (b) Longness factor profile of the

cluster in Fig. 6d for KSN 15 Jun 2010. The generally constant

level of longness factor shown in (a) and (b) can be attributed to

the fact that although chaff is spreadingmostly along the direction

of the aircraft’s flight path, it is also spreading radially with the

random movement of the air carrying its particles.



FIG. 17. Algorithms for calculation of (a) fall trend index, (b) volume-expansion trend index, and

(c) mean reflectivity trend index. Algorithms described in (a) and (b) count the number of beyond-

threshold-level altitude drop instances (Kf) and volume-expansion instances (Ke) within the in-

ference time frames. If the instance count is above a threshold, then the tracked cluster is assigned

a trend degree that is the ratio of the instance count of dynamic state change to the total number

of the inference time frames. Otherwise, it is assigned a value of zero. Algorithm described in

(c) estimates the slope of least squares regression line fitting themean reflectivity profile. If the slope

is found within a particular range (a negative interval), then the cluster is assigned a triangularly

shaped trend degree with its peak (a unity) at Sr2. Otherwise, it is assigned a value of zero.



(KST) at 10-min intervals for a total of 19 time frames.

In Table 5, the longness factor corresponding to a specific

isocluster ID is attributed to the last time frame cluster

belonging in the same isocluster set (in this case, the

cluster at 1300 KST). Other single-frame cluster statistics

for the 1300 KST 3 June 2010 data are shown in Table 6.

The performance index of chaff echo detection PI is

themeasure of the success rate of a correct identification

using the proposed method, and is expressed by

PI 5 12DT

jBj 5 12Dc 1Dnc

jBj , (12)

whereDc is the total number of data points (or cells) that

are wrongly identified as chaff, Dnc is the total number

of data points (or cells) that are wrongly identified as

nonchaff, and jBj is the cardinality of the set of input

data points that have above-threshold reflectivity as

defined in the previous section.

The actual calculation of the performance index is

performed by considering two separate cases: one is the

case in which it is known that only nonchaff echoes are

present, and the other is the case in which both chaff

and nonchaff echoes are known to be present. The

information that reveals whether a given radar image

represents a chaff-contaminated case is provided by

KMA experts who can identify nonweather (such as

chaff) from weather echoes based on their first-hand

knowledge and experience, and also with the assis-

tance of specialized cross-referencing radar image

analysis tools (Han et al. 2011). The radar image ar-

chives in which nonweather clutter (such as chaff) are

separately labeled (censored) from regular weather

echo are provided by the KMA after the long analysis

session is completed. These radar data are essentially

required for the performance evaluation of the proposed

technique because they establish independent referential

truth to which the inference results of the proposed

method can be compared.

The calculation procedure of the performance index is

shown in Fig. 20. Performance index (PI) has a range

between 0 and 1. The closer the index is to 1, the better

the results are. A compilation of case-by-case perfor-

mance results of the overall chaff echo detection pro-

cedure is illustrated in Table 7.

The overall performance index of 0.81 shown in

Table 7 indicates that chaff clutter can be detected with

fair accuracy (within an acceptable level) despite the

fact that there is no obvious way to choose a single set of

optimal parameters (the training routine is only based

on few heuristically selected parameter settings) for the

inference system that is applicable to all cases. We also

note that this result is significantly influenced by the way

the KMAexperts identify the chaff clutter that is used as

the truth reference. The following is the list of some key

FIG. 18. Single-layer perceptron network with threshold acti-

vation function for binary classification of radar echo clusters

(FI 5 0 for nonchaff; FI 5 1 for chaff).

FIG. 19. (a) 2D top view of normal clusters for KSN 1300 KST 3

Jun 2010 data and their corresponding isocluster membership

ID. (b) Identified chaff clusters by the proposed method with

parameter settings displayed in Table 5.



aspects of their identification process that can contribute

to (work in favor of) the performance outcome of the

proposed QC routine.

1) Chaff clusters are always selected among reasonably

sized clusters (small sporadic clusters are exempted

from being chosen). This could significantly impact the

performance index, especially if small sporadic clusters

(always filtered out by the proposedQCmethod)make

up a large percentage of input image data.

2) Candidate clusters are followed through (over many

time frames). Using cluster dissecting tools, the pro-

files of the vertically dissected area are viewed in

time series.

3) The change in the population of image cells exhibit-

ing high reflectivity is noted.

7. Chaff echo removal procedure

The CEDR QC within the framework of the KMA

weather radar system optimization is required to have both

original input and quality controlled output radar data in

specific radar data format. This means that the original ra-

dar data must be mapped to quality controlled output data

through procedures that overwrite the modified portion of

the data back to the original radar data. Thesemapping and

data writing procedures are described in Fig. 21.

We note that all data in the background pool category

(A, Fc, and WS), those that were not subject to the in-

ference system, are not removed from original radar

data.

CEDRQC simulation results for the studied cases are

shown in Fig. 22.

8. Conclusions

This paper outlines the radar QC procedure for the

detection and removal of chaff clutter from reflectivity

data obtained by single-polarization radar measure-

ments. Specifically, we considered ground echo removed

(CZ) reflectivity data. The proposed QC procedure

consists of spatial and temporal image element clustering

algorithms (for cluster tracking); generic cluster charac-

terization routines describing both static and dynamic

characteristics of clusters; and an inference system pred-

icated on four inputs, one binary output single-layer

perceptronNN (with a threshold activation function) that

serves as the decision support mechanism for target

TABLE 5. Results of inference system based on single-layer perceptron NN with activation function applied to normal clusters in KSN

1300 KST 3 Jun 2010 data. (Correctly identified chaff by cluster number: 75%, by volume: 99.3%. The results below are obtained with

the following parameter settings: Rth 5 4, h1 5 0.055 km, h2 5 0.270 km, Uth 5 0.07, Sr1 5 0 dBZ min21, Sr2 520.03 dBZ min21, Sr3 520.15 dBZ min21,Q15 0.5,Q25 0.2,Q35 0.2,Q45 0.1, andYth5 0.7. Also, the corresponding clusters to each isocluster set are found in

Fig. 19. The matching cluster is referenced by its isocluster ID.)

Isocluster

ID jwj 2 1 Kf Ke Sr (dBZ min21) hf he hr gk Y Decision

1 8 2 1 20.155 0.25 0.13 0.00 0.58 0.21 Nonchaff

2 1 0 0 20.020 — — — 0.44 — —

3 18 23 3 0.104 0.00 0.17 0.00 0.12 0.05 Nonchaff

4 18 14 16 20.028 0.78 0.89 0.93 0.77 0.83 Chaff

5 5 4 4 20.048 0.80 0.80 0.85 0.53 0.78 Chaff

6 9 7 4 20.121 0.78 0.44 0.24 0.78 0.60 Nonchaff

7 14 9 12 20.032 0.64 0.86 0.98 0.33 0.72 Chaff

8 11 4 4 0.001 0.36 0.36 0.00 0.72 0.32 Nonchaff

9 4 3 0 20.217 0.75 0.00 0.00 0.63 0.44 Nonchaff

TABLE 6. Normal cluster statistics for KSN 1300 KST 3 Jun 2010 data.

Isocluster

ID X (km) Y (km) Z (km)

Volume

(km3)

Coverage

area (km2)

Maximum

reflectivity (dBZ)

Mean reflectivity

(dBZ)

1 229.2 167.5 0.90 516 145 23.00 10.34

2 230.9 186.8 0.70 92 107 22.00 10.73

3 296.5 270.3 1.70 1921 1027 48.00 18.94

4 268.2 312.4 4.27 41 330 12 437 43.00 14.21

5 153.9 230.1 1.76 595 310 42.00 20.73

6 322.9 223.2 1.50 322 220 32.00 13.34

7 170.1 141.7 2.87 4092 1478 43.00 21.65

8 386.4 136.6 3.96 932 270 42.00 21.26

9 329.7 64.5 4.25 508 182 33.00 13.11



cluster identification. The proposed method was applied

to 116 randomly selected cases, mostly from weekday

sample data from 2010 and 2011.

The main contributions of this work are as follows:

1) The introduction of a novel amorphous radar object-

tracking procedure in real time, based on a spatial

and temporal clustering algorithm with respectiveO(n)

and O(q2) time complexity that is applied only on

selected radar image elements, resulting in dramatic

speed improvement (n is the total number of input

image data points above threshold reflectivity, q is the

average number of input clusters above a specific vol-

ume within a single time frame 3D image); and 2)

a demonstration that a cost-effective detection of chaff

clutter from CZ reflectivity data is achievable at an

81% accuracy level through time series analysis of re-

flectivity image data without resorting to other radar

moment data at the range gate level.

FIG. 20. Procedure for the calculation of the performance index

(PI) of CEDR QC procedure.

TABLE 7. Performance evaluation results for 116 randomly se-

lected cases between 2010 and 2011. Each sample is separated by at

least 6 h of time difference, and was collected from various sites

that include Oseongsan (KSN), Jindo (JNI), Gangneung (GNG),

Gwangdeoksan (GDK), Myeonbongsan (MYN), Seongsan (SSP),

Gwanaksan (KWK), Gudeoksan (PSN), Gosan (GSN), Baengyeongdo

(BRI).

Case

Total

cases

Both chaff

and nonchaff

present

Only

nonchaff

present

Number of cases 80 36 116

Performance index (PI) 0.79 0.87 0.81

Standard deviation 0.163 0.156 0.165

FIG. 21. CEDR QC algorithm featuring chaff clutter removal.



As an extension of the present work, future research

could focus on incorporating other radar moment data

(radial velocity, spectrum width, etc.) into the current

CEDRQC inference and training model. However, this

also implies a potential increase in both the spatial and

time complexity of the CEDR QC procedure. Another

possible approach for improving the detection result

includes the adoption of a new set of feature tracking

and parameterization schemes related to radar cluster

characterization and classification.

Acknowledgments. This research was supported by

the Development Project of Radar System Optimization

grant funded by theKoreaMeteorological Administration

FIG. 22. Chaff echo removal simulation results. (top) Full echoes with below-threshold reflectivity echoes re-

moved. (middle) Identified chaff echoes using proposed inference system. (bottom) Echoes with below-threshold

reflectivity echoes and chaff clutter removed. (a) KSN 1300 KST 3 Jun 2010 (parameter settings: Rth 5 4, h1 50.055 km, h2 5 0.270 km, Uth 5 0.07, Sr1 5 0 dBZ min21, Sr2 5 20.03 dBZ min21, Sr3 5 20.15 dBZ min21,

Q15 0.5,Q25 0.2,Q3 5 0.2,Q4 5 0.1, and Yth 5 0.7). (b) KSN 1300 KST 15 Jun 2010 (parameter settings: Rth 5 4,

h1 5 0.015 km, h2 5 0.270 km, Uth 5 0.02, Sr1 5 0 dBZ min21, Sr2 5 20.03 dBZ min21, Sr3 5 20.15 dBZ min21,

Q1 5 0.4, Q2 5 0.3, Q3 5 0.2, Q4 5 0.1, and Yth 5 0.6).



(KMA). This research was also supported by the Ministry

of Knowledge Economy (MKE) of the government of

South Korea under the Human Resources Development

Program for Special EnvironmentNavigation/Localization

National Robotics Research Center Support Program

supervised by theNational IT Industry PromotionAgency

(NIPA Grant NIPA-2012-H1502-12-1002). The authors

gratefully acknowledge this support.

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