# Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

_{DR}) are defined and combined for clutter detection with improved results.

## 2. Discriminant Functions

_{DR}), the cross-correlation coefficient between two scans (${\rho}_{12}$), and the copolar cross-correlation coefficient between dual-polarization (H and V-polarization) (${\rho}_{\mathrm{hv}}$) signals.

^{−1}and CNR (Clutter to Noise Ratio) < 3 dB. This deletion provided a clutter field not contaminated from moving objects on the ground or airborne (e.g., birds and aircraft).

#### A. Co-Polar Cross-Correlation Coefficient (${\rho}_{\mathrm{hv}}$)

- Clutter (“C”) (narrow band zero Doppler velocity)
- Narrow-Band Zero Velocity Weather (“W
_{0}”) (i.e., $\left|{\mathit{v}}_{\mathbf{r}}\right|$ ≤ 2 m·s^{−1}and ${\sigma}_{\mathrm{v}}$ ≤ 2 m·s^{−1} - Non-Zero Velocity Weather (“W”) (i.e., $\mathbf{\left|}{\mathit{v}}_{\mathbf{r}}\mathbf{\right|}$ > 2 m·s
^{−1}and ${\sigma}_{\mathrm{v}}$ > 2 m·s^{−1})

_{0}as a separate weather class because their properties are mostly similar to clutter signals, and thus are the most challenging to distinguish. Weather signals W

_{0}are echoes from resolution volumes where the turbulence and the mean wind radial shear are weak, with a mean radial velocity close to zero; thus, these weather signals commonly have a longer correlation time ${\mathsf{\tau}}_{\mathrm{c}}$ compared with the other weather signals (i.e., W).

#### B. Dual-Scan Cross-Correlation Coefficient

#### C. Differential Reflectivity (Z_{DR})

_{DR}values for clutters have a wider range in comparison to weather signals [4,5]. Because we conducted two consecutive scans, there are two differential reflectivities (i.e., ${Z}_{\text{DR}}{}_{1}$ and ${Z}_{\text{DR}}{}_{2}$); and both of them most likely have the same PDFs; thus, we used the averaged value for Z

_{DR}. It can be seen from Figure 5 that both PDFs center near zero, but that clutter has a wide distribution, whereas the PDF for weather signals is narrow. Because the PDF of Z

_{DR}for weather is much larger than that of the clutter in the region near zero, it is clear that clutter with the near-zero Z

_{DR}values would most likely be detected as weather instead of clutter. Therefore, in order to decrease the detection error rate, Z

_{DR}in the interval within the pair of vertical lines is not considered for clutter detection. Z

_{DR}does not contribute as much as the other discriminants. However, from our experience, there is around a 3%–5% performance improvement for P

_{D}when we use ZDR as a discriminant.

## 3. Bayes Optimal Decision

**X**° = (Z

_{DR}, ${\rho}_{\mathrm{hv}}$, ${\rho}_{12}$).

**X**°), the classifier will identify that

**X**=

**X**° belongs to the class ${\mathsf{\omega}}_{\mathrm{i}}$, if and only if p(${\mathsf{\omega}}_{\mathrm{i}}$|

**X**°) > p (${\mathsf{\omega}}_{j}$|

**X**°) for i, j $\in $ [C, W

_{0}, W], j ≠ i. According to the Bayes rule, the probability of ${i}^{th}$ class given the observed

**X**°, can be calculated as [21]:

**X**=

**X**°) is the same for all classes, Equation (4) can be rewritten as:

**X**=

**X**°, i.e., $p(\mathit{X}={\mathit{X}}^{O}|{\mathsf{\omega}}_{\mathrm{i}})$. The likelihood function can be obtained from training data for all classes by using the PDFs that are shown in Figure 4 and Figure 5. Thus we have:

**X**=

**X**° to “C” only if

**X**=

**X**°|C) > p(

**X**=

**X**°|W

_{0}) and p(

**X**=

**X**°|C) > p(

**X**=

**X**°|W)

- Calculate SNR for current gate. If SNR < 20 dB, the current gate is considered not to have a significant weather signal and we compute SNR for the next range gate. Otherwise, go to step 2.
- Compute the three observed discriminant functions for the current gate, and calculate $p(\mathit{X}={\mathit{X}}^{\mathrm{O}}|C\text{\hspace{0.17em}})$, $p(\mathit{X}={\mathit{X}}^{\mathrm{O}}|{W}_{0})$, and $p(\mathit{X}={\mathit{X}}^{\mathrm{O}}|W)$ using Equation (6).
- If $p(\mathit{X}={\mathit{X}}^{\mathrm{O}}|C)>p(\mathit{X}={\mathit{X}}^{\mathrm{O}}|{W}_{0})$ and $p(\mathit{X}={\mathit{X}}^{\mathrm{O}}|C)>p(\mathit{X}={\mathit{X}}^{\mathrm{O}}|W)$ for the current gate, the data is clutter-contaminated. Otherwise, the data is not contaminated and we return to step 1) for the next gate.

## 4. Performance Evaluations of DPDS

_{FA}) for the three controlled testing data sets, collected by KOUN at 13:07, 13:08 and 14:02 UTC on 9 February 2011.

_{FA}, the reason is that there are too many weather signal pixels. This table shows that DPDS algorithm produces the lowest P

_{FA}with the highest P

_{D}.

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Groginsky, H.L.; Glover, K.M. Weather radar canceller design. In Proceedings of the 19th Conference on Radar Meteorology of the American Meteorological Society, Miami Beach, FL, USA, 15–18 April 1980.
- Cao, Q.; Zhang, G.; Palmer, R.D.; Knight, M.; May, R.; Stafford, R.J. Spectrum-Time Estimation and Processing (STEP) for Improving Weather Radar Data Quality. IEEE Trans. Geosci. Remote Sens.
**2012**, 50, 4670–4683. [Google Scholar] [CrossRef] - Siggia, A.D.; Passarelli, R.E. Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation. In Proceedings of the Third European Conference on Radar in Meteorology and Hydrology, Visby, Sweden, 6–10 September 2014.
- Doviak, R.J.; Zrnic, D.S. Doppler Radar & Weather Observations, 2nd ed.; Dover: New York, NY, USA, 2006; p. 562. [Google Scholar]
- Zhang, G. Weather Radar Polarimetry; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
- Meischner, P. Weather Radar Principles and Advanced Applications; Springer-Verlag: Berlin, Germany, 2002. [Google Scholar]
- Hubbert, J.C.; Dixon, M.; Ellis, S.M.; Meymaris, G. Weather Radar Ground Clutter. Part I: Identification, Modeling, and Simulation. J. Atmos. Ocean. Technol.
**2009**, 26, 1165–1180. [Google Scholar] [CrossRef] - Hubbert, J.C.; Dixon, M.; Ellis, S.M.; Meymaris, G. Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering. J. Atmos. Ocean. Technol.
**2009**, 26, 1181–1197. [Google Scholar] [CrossRef] - Ice, R.L.; Wyle Information Systems; Norman, O.K.; Rhoton, R.D.; Krause, J.C.; Saxion, D.S.; Boydstun, O.E.; Heck, A.K.; Chrisman, J.N.; Berkowitz, D.S.; et al. Automatic clutter mitigation in the WSR-88D, design, evaluation, and implementation. In Proceedings of the 34th Conference on Radar Meteorology, Williamsburg, VA, USA, 5–9 October 2009; p. 5.3.
- Yinguang, L.; Guifu, Z.; Doviak, R.J. A new approach to detect the ground clutter mixed with weather echoes. In IEEE Radar Conference (RADAR), Kansas City, MO, USA, 23–27 May 2011.
- Li, Y.; Zhang, G.; Doviak, R.J.; Lei, L.; Cao, Q. A New Approach to Detect Ground Clutter Mixed with Weather Signals. IEEE Trans. Geosci. Remote Sens.
**2013**, 51, 2373–2387. [Google Scholar] [CrossRef] - Li, Y.; Zhang, G.; Doviak, R.J.; Saxion, D.S. Scan-to-Scan Correlation of Weather Radar Signals to Identify Ground Clutter. IEEE Geosci. Remote Sens. Lett.
**2013**, 10, 855–859. [Google Scholar] [CrossRef] - Zhang, G.; Doviak, R.J. Ground Clutter Detection Using the Statistical Properties of Signals Received with a Polarimetric Radar. IEEE Trans. Signal Process.
**2014**, 62, 597–606. [Google Scholar] - OFCM. Doppler Radar Meteorological Observations, Part III—WSR-88D Products and Algorithms. In FEDERAL METEOROLOGICAL HANDBOOK NO. 11; FCM-H11C-2006; Federal Coordinator for Meteorological Services and Supporting Research: Washington, DC, USA, April 2006. Available online: http://www.ofcm.gov/fmh11/fmh11partc/pdf/FMH-11-PartC-April2006.pdf (accessed on 10 November 2015). [Google Scholar]
- Lei, L.; Zhang, G.; Doviak, R.J.; Palmer, R.; Cheong, B.L.; Xue, M.; Cao, Q.; Li, Y. Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise. J. Atmos. Ocean. Technol.
**2012**, 29, 772–795. [Google Scholar] [CrossRef] - Melnikov, V.M.; Zrnic, D.S. Autocorrelation and cross-correlation estimators of polarimetric variables. J. Atmos. Ocean. Technol.
**2007**, 24, 1337–1350. [Google Scholar] [CrossRef] - Golbon-Haghighi, M.-H.; Mahboobi, B.; Ardebilipour, M. Linear Pre-coding in MIMO–CDMA Relay Networks. Wirel. Pers. Commun.
**2014**, 79, 1321–1341. [Google Scholar] [CrossRef] - Golbon-Haghighi, M.-H.; Mahboobi, B.; Ardebilipour, M. Multiple Antenna Relay Beamforming for Wireless Peer to Peer Communications. J. Inf. Syst. Telecommun.
**2013**, 1, 209–215. [Google Scholar] - Golbon-Haghighi, M.-H.; Shirazi, M.; Mahboobi, B.; Ardebilipour, M. Optimal Beamforming in Wireless Multiuser MIMO-relay Networks. In Proceedings of the 21st Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran, 14–16 May 2013; pp. 1–5.
- Qing, C.; Yeary, M.B.; Zhang, G. Efficient Ways to Learn Weather Radar Polarimetry. IEEE Trans. Educ.
**2012**, 55, 58–68. [Google Scholar] [CrossRef] - Papoulis, A. Probability, Random Variables, and Stochastic Processes; Mcgraw-Hill Incorporated: New York City, NY, USA, 1991. [Google Scholar]

**Figure 4.**Probability density function of dual-scan cross-correlation vs. dual-polarization co-polar cross-correlation, obtained from training data (

**a**) SNR threshold value 20 dB and (

**b**) SNR threshold value 5 dB.

**Figure 5.**Probability density function (PDF) of differential reflectivity (Z

_{DR}) for C, W, W0, (i.e., $P({Z}_{\mathrm{DR}}|{\mathsf{\omega}}_{i})$ given ${\mathsf{\omega}}_{\mathrm{i}}$ = C, W

_{0}, and W), obtained from the training data.

**Figure 6.**Pure weather signals (13:08 UTC on 9 February 2011) mixed with pure ground clutter (00:46 UTC on 4 February 2011). (

**a**) Dual-scan cross-correlation coefficient; (

**b**) dual-polarization co-polar cross-correlation coefficient; (

**c**) differential reflectivity; (

**d**) ground truth clutter map.

Data-Time, UTC | CMD | DS | DP | DPDS |
---|---|---|---|---|

13:08 | 0.7770% | 0.3387% | 0.4782% | 0.00% |

13:07 | 0.5525% | 0.2130% | 0.3195% | 0.00% |

14:02 | 0.6346% | 0.2363% | 0.3443% | 0.00% |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Golbon-Haghighi, M.-H.; Zhang, G.; Li, Y.; Doviak, R.J.
Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method. *Atmosphere* **2016**, *7*, 83.
https://doi.org/10.3390/atmos7060083

**AMA Style**

Golbon-Haghighi M-H, Zhang G, Li Y, Doviak RJ.
Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method. *Atmosphere*. 2016; 7(6):83.
https://doi.org/10.3390/atmos7060083

**Chicago/Turabian Style**

Golbon-Haghighi, Mohammad-Hossein, Guifu Zhang, Yinguang Li, and Richard J. Doviak.
2016. "Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method" *Atmosphere* 7, no. 6: 83.
https://doi.org/10.3390/atmos7060083