Based on a Fuzzy Logic Technique to Identify Clutter Echoes of Weather Radar in Time Domain Automatically

Xu Wang1, Jianxin He1, Chenghua Xie2

1Department of Electronics Engineering , Chengdu University of Information Technology, Chengdu, Sichuan, 610225, China

2 Chengdu YuanWang Science& Technology CO.,LTD, Chengdu, Sichuan, 610225, China

wanghan811@163.com, hjx@cuit.edu.cn, xchyw@cuit.edu.cn

Abstract

The identification of clutter echoes is one of the most important tasks before mitigating their influences on radar measurements. In this study we describes an algorithm using fuzzy logic concepts for clutter identification based on the analysis of different features of the radar field (the vertical gradient of reflectivity, the spin change of reflectivity and the clutter phase alignment). And every feature is given a weight. Then, the likelihood values that the features match the clutter echoes are calculated by a fuzzy logic engine. By comparing the chosen threshold and weighted average of the likelihood values to decide whether the observed echoes are clutter echoes. And this technique is illustrated with several experimental data. The results demonstrate that this fuzzy logic algorithm has a good performance to identify clutter echoes of Doppler weather radar in the time domain. 1. Introduction

Quality Control is one of the most important problems before implementing radar measurements for quantitative uses. One of the sources that affect radar data is the echoes caused by non-meteorological targets. If these clutter echoes are not identified and removed, they will cause an overestimation of rainfall and this would also affect the performance of automatic algorithms based on radar data.

Ground clutter contamination within radar data has two sources: normally-propagated (NP) ground clutter from stationary targets such as buildings, trees, or terrain, and anomalously propagated (AP) ground clutter that arises from particular atmospheric conditions within the planetary boundary layer (i.e., temperature inversions) that duct the radar beam to the ground [1]. In AP conditions, the size and intensity of mean clutter echoes change and new echoes may appear. AP clutter can evolve and dissipate as atmospheric conditions change, so

AP targets are non-stationary and “changing,” they are relatively difficult to remove with the use of stationary clutter bypass maps.

With the development of fast digital receivers capable of real time spectral processing, the real time identification of clutter is possible. Based on the identification, clutter filters can be applied to only those radar resolution volumes where clutter is present in real time. In this way weather echoes are preserved while clutter echoes are mitigated.

In this study we describes an algorithm using fuzzy logic concepts (see Kosko, 1992; Mendel, 1995) for clutter identification based on the analysis of different features of the radar field (the texture of reflectivity, reflectivity spin change and clutter phase alignment). And this technique is illustrated with experimental data from X-band dual-polarization radar. 2. Background

Weather radars typically operate clutter filters in the time domain, i.e., the digitized I and Q samples (in phase and quadrature) are past through a filter [3]. As weather conditions vary, AP clutter can appear and subsequently disappear. Radar operators have to monitor AP clutter and then turn on a clutter filter when the AP conditions were significant. Later turn off the clutter filter again. Such operates are prone to error. Ideally we want to clutter filter only those gates that are clutter contaminated. Using signal processing to identify those gates that are contaminated by clutter and then to apply a clutter filter in real time can solute this problem. The radar processors now have enough processing power to do that. I and Q samples can be put into a buffer while the data is processed and clutter affected gates are identified. Using this solution can identify both NP and AP clutter without the Radar operators’ monitor. 3. Fuzzy logic identification of the CMD algorithm

2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application

978-0-7695-3490-9/08 $25.00 © 2008 IEEE

DOI 10.1109/PACIIA.2008.237

746

2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application

978-0-7695-3490-9/08 $25.00 © 2008 IEEE

DOI 10.1109/PACIIA.2008.237

748

2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application

978-0-7695-3490-9/08 $25.00 © 2008 IEEE

DOI 10.1109/PACIIA.2008.237

746

Both NP and AP clutter echoes are ground clutter, so

they have the same following characteristics [2]: (1) High degree of spatial variability; (2) Doppler velocities close to 0; (3) Constant backscatter phase angle. According these characteristics of clutter, we can

select several feature fields to identify them. 3.1. Introduction of the feature fields for CMD

To identify the gates that are contaminated with clutter, a Fuzzy Logic base algorithm term CMD (Clutter Mitigation Decision) is employed. CMD inputs are the texture of the reflectivity (TDBZ), the SPIN of the reflectivity (SPIN) and clutter phase alignment (CPA) field around the radar resolution volume of interest ([4], Dixon et al. 2006).

TDBZ is the mean squared difference of the reflectivity. SPIN is a parameter that is a measure of the inflection changes of reflectivity along a direction in the radar radial (Steiner and Smith 2002). CPA is a direct measure of the defining characteristic of non-moving ground clutter targets: constant backscatter phase angle, i.e., non-moving ground clutter is a coherent target. In contrast, weather is a distributed target and the absolute phase of the received backscatter usually varies significantly from time series data sample to data sample over the dwell time. Thus CPA has proven to be an excellent discriminator of weather and ground clutter.

The "texture" of the reflectivity (TDBZ) field is calculated as shown in (1):

)1/()(2/

1)2/(

21 −⎟⎟

⎠

⎞⎜⎜⎝

⎛−= ∑

+−=− NgDBZDBZTDBZ

Ng

Ngiii

, (1)

Where, Ng is the number of gates in a 1-D computational kernel.

The SPIN variable indicates the number of inflection points within the gate-to-gate reflectivity difference field, expressed as a percentage of all possible differences, that exceed the minimum difference (Zthresh, we used 4dBZ in this paper) allowed is calculated as shown in (2) :

100)1/(2/

1)2/(×−= ∑

+−=

NgMSPINSPINNg

Ngii , (2)

Where, ⎪⎩

⎪⎨⎧

≤−

>−=

−

−

ZthreshZZ

ZthreshZZMSPIN

ii

iii

1

1

,0

,1

CPA is a measure of how constant the absolute return phase (i.e., the phase of a received I and Q sample) remains for the transmitted pulses which comprise a beam of radar data. For a fix, non moving target, CPA is 1. If the target is not completely stationary over the measurement period, the mean velocity will differ from 0 m/s and/or the width of the spectrum of the radar return

signal will increase. Both will decrease CPA from 1. The more constant the absolute phase is, the more likely it is that the gate contains clutter. CPA is defined as:

∑∑==

=N

ii

N

ii xxCPA

11/ , (3)

Where xi is the received time series and N is the time series length. Thus CPA is the magnitude of the vector sum of the individual time series members divided by the sum of the magnitudes of the xi. CPA is an excellent indicator of clutter since by definition it is a measure of the primary characteristic of stationary ground clutter. If the phase of the xi is a constant, CPA will be one regardless of the behavior of the magnitude of the xi. The backscatter phase is a constant since stationary ground clutter is a coherent target.

CPA has the following characteristics: (1) CPA is computed at a single gate; (2) It is a normalized value, ranging from 0 to 1; (3) 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. CPA is typically above 0.95;

(4) In noise, the phase from pulse to pulse is random. CPA is typically less than 0.05;

(5) In weather, the phase from pulse to pulse will vary depending on the velocity of the targets within the illumination volume. CPA is often close to 0, but increases in weather with a velocity close to 0 and a narrow spectrum width. CPA is above 0.9 only for isolated gates. 3.2. Introduction of the CMD algorithm

The CMD algorithm makes use of Fuzzy Logic to combine the information from a number of different fields to derive a single decision making field.

It uses spatial information in range only, i.e., it uses a 1-D computational kernel [4]. The length of the kernel in gates is an adjustable parameter. Figure 1 shows a 1-D computational kernel with a length of 9 gates (in this paper we used 13 gates).

Figure 1 1-D kernel with a length of nine gates

The input data fields are referred to as feature fields

and the units of these data are simply the units of the input

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field. Table 1 lists the feature fields that the CMD algorithm used [4].

Table 1 Feature fields for CMD

Name Description Weight TDBZ Texture of reflectivity 1.0 SPIN Reflectivity spin change: the number

of significant dBZ sign reversals in range

1.0

CPA Clutter Phase Alignment 2.0

The feature fields are then transformed into interest fields by applying a piece-wise linear transfer function (known as a membership function), Figure 2 shows the membership functions for TDBZ, SPIN and CPA [4]. Values in the interest fields range from 0.0 to 1.0.

Note the membership function for CPA, because the time series length we used is large (in this paper is 82), for most clutter the CPA is above 0.7 (not 0.95), so we used 0.7 as the inflection.

Figure 2 Membership functions for TDBZ, SPIN and CPA

Using fuzzy logic, the interest fields are combined into

a single decision field, which can be interpreted as the likelihood of clutter at a gate.

A threshold (we selected 0.4 in this paper) is applied to this decision field. A value above 0.4 is interpreted as clutter exists, while values below 0.4 are interpreted as clutter does not exist.

Figure 3 shows the schematic of the CMD algorithm. The general steps of the CMD algorithm are as follows:

(1) Using a 1-D kernel to compute feature fields: dBZ texture (TDBZ) ,and dBZ SPIN;

(2) Compute every gate’s feature fields CPA; (3) Convert features to interest fields by applying

interest mapping; (4) Compute CMD field by applying Fuzzy Logic to

interest fields; (5) Threshold CMD at 0.4 to produce CMD clutter

flag.

Figure 3 Schematic of the CMD algorithm

3.3. Example case study for CMD

The experience data were collected with a D-Pol Doppler weather radar of X-band in Chengdu University of Information Technology of China for the following dates: 2008/01/12 (this data has no weather, collected for compare) and 2008/03/29 (this data contains stratiform precipitation). The times series data were gathered using processor and the CMD algorithm was run during post processing. The x- and y-axes span 125km.

Figures 4 through 9 show the case of 2008/03/29. Figure 4 shows unfiltered reflectivity. The clutter spike to the north-east and south-west of the radar is caused by the mountains. Figure 8 shows the clutter map derived with CMD with yellow marking the regions to be clutter. Note that the clutter spike from the mountains is correctly identified as clutter in Figure 8. Compared the CMD results with the reflectivity data of 2008/01/12, we got the following conclusions: more than 80 percent of the clutter was identified and the miscarriage of justice was less than 10 percent.

Figures 10 and 11 show the case of 2008/01/12. Figure 10 shows unfiltered reflectivity. Figure 11 shows the clutter map derived with CMD with yellow marking the regions to be clutter. Compared the CMD results with the reflectivity data of 2008/01/12, almost 100 percent of clutter was identified.

Figure 4 Un-filtered dBZ, 2008/03/29

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Figure 5 TDBZ, 2008/03/29

Figure 6 SPIN, 2008/03/29

Figure 7 CPA, 2008/03/29

Figure 8 CMD flag field, 2008/03/29

Figure 9 Filtered dBZ in Figure 4, 2008/03/29

Figure 10 Un-filtered dBZ, 2008/01/12

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Figure 11 CMD flag field, 2008/01/12

4. Conclusions

Clutter echoes contaminate the radar precipitation measurements, which results in errors in rainfall estimates and affect the performance of some automatic algorithms based on radar information. In this study we have described and evaluated an algorithm for clutter identification based on fuzzy logic concepts. And the results demonstrator that this fuzzy logic algorithm has a good performance to identify the clutter echoes in the time domain. References [1] Kessinger C. , et al. . The radar echo classifier: a fuzzy logic

algorithm for the WSR-88D, Preprints, 3rd Conf. on Artificial Intelligence Applications to the Environmental Science, AMS , 2003.

[2] M. Berenguer, et al. . Identificaton of clutter echoes using a fuzzy logic technique, Albuquerque-EUA 32nd Conference on Radar Meteorology, 2005.

[3] John C. Hubbert, et al. . Real Time Clutter Identification and Mitigation for NEXRAD, Preprints-CD, IIPS of Annual AMS Meeting, AMS, 13-18 Jan 2007.

[4] Mike Dixon, et al. . Improving NEXRAD Data-Data Quality Algorithm Progress, FY2006 Annual Report, NCAR, 23 Feb 2007.

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