Identification and removal of non-meteorological echoes in dual-polarization radar data based on a fuzzy logic algorithm

2015 ◽  
Vol 32 (9) ◽  
pp. 1217-1230 ◽  
Author(s):  
Bo-Young Ye ◽  
GyuWon Lee ◽  
Hong-Mok Park
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Dual Polarization ◽  
Fuzzy Logic Algorithm

scholarly journals Fuzzy Logic Algorithms to Identify Birds, Precipitation, and Ground Clutter in S-Band Radar Data Using Polarimetric and Nonpolarimetric Variables

2019 ◽  
Vol 36 (12) ◽  
pp. 2401-2414 ◽  
Author(s):  
Basivi Radhakrishna ◽  
Frédéric Fabry ◽  
Alamelu Kilambi
Keyword(s):  
Fuzzy Logic ◽  
Target Identification ◽  
Radar Data ◽  
Radar Observations ◽  
Fuzzy Logic Algorithm ◽  
Ground Clutter ◽  
Independent Events ◽  
Radar Echoes ◽  
Radar Echo ◽  
Migrating Birds

AbstractThe statistical properties of the radar echoes from biological, precipitation, and ground targets observed with the McGill S-band dual-polarization radar have been used to devise a polarimetric and a nonpolarimetric fuzzy logic algorithm for pixel-by-pixel target identification. Radar observations of migrating birds show distinctly different polarimetric features during their relative approach and departure from the radar site illustrating the dependency of radar parameters on the canting angle and scattering cross section. The devised algorithms have been tested with two independent events, each consisting of 2 h of radar observations with a 5-min temporal resolution. One event consisted of precipitation without birds while the other contained only birds. The misclassifications were 10.12% and 9.6%, respectively, for the two cases for the nonpolarimetric algorithm, and 1.99% and 0.92% for the polarimetric algorithm. The results indicate that even though nonpolarimetric radar membership functions may be considered adequate for separating radar echo returns from birds, precipitation, and ground targets, they are not sufficiently skilled if a greater accuracy is required. Target identification without polarimetric variables especially fails in the region of zero isodop and in precipitation with an echo top below 4 km.

scholarly journals A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications

2015 ◽  
Vol 32 (1) ◽  
pp. 22-47 ◽  
Author(s):  
Renzo Bechini ◽  
V. Chandrasekar
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Logic ◽  
Dual Polarization ◽  
Auxiliary Data ◽  
Classification Schemes ◽  
Fuzzy Logic Algorithm ◽  
Data Space ◽  
Radar Applications ◽  
Input Variables ◽  
Statistical Sample

AbstractMost of the recent hydrometeor classification schemes are based on fuzzy logic. When the input radar observations are noisy, the output classification could also be noisy, since the process is bin based and the information from neighboring radar cells is not considered. This paper employs cluster analysis, in combination with fuzzy logic, to improve the hydrometeor classification from dual-polarization radars using a multistep approach. The first step involves a radar-based optimization of an input temperature profile from auxiliary data. Then a first-guess fuzzy logic processing produces the classification to initiate a cluster analysis with contiguity and penalty constraints. The result of the cluster analysis is eventually processed to identify the regions populated with adjacent bins assigned to the same hydrometeor class. Finally, the set of connected regions is passed to the fuzzy logic algorithm for the final classification, exploiting the statistical sample composed by the distribution of the dual-polarization and temperature observations within the regions. Example applications to radar in different environments and meteorological situations, and using different operating frequency bands—namely, S, C, and X bands—are shown. The results are discussed with specific attention to the robustness of the method and the segregation of the data space. Furthermore, the sensitivity to noise and bias in the input variables is also analyzed.

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

2009 ◽  
Vol 26 (7) ◽  
pp. 1181-1197 ◽  
Author(s):  
J. C. Hubbert ◽  
M. Dixon ◽  
S. M. Ellis
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Weather Radar ◽  
Radar Data ◽  
Weather Data ◽  
Fuzzy Logic Algorithm ◽  
Zero Velocity ◽  
Ground Clutter ◽  
Phase Alignment ◽  
Real Time Identification

Abstract The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol).

scholarly journals Full-Year Evaluation of Nonmeteorological Echo Removal with Dual-Polarization Fuzzy Logic for Two C-Band Radars in a Temperate Climate

2020 ◽  
Vol 37 (9) ◽  
pp. 1643-1660
Author(s):  
Aart Overeem ◽  
Remko Uijlenhoet ◽  
Hidde Leijnse
Keyword(s):  
Fuzzy Logic ◽  
Rain Gauge ◽  
Temperate Climate ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Fuzzy Logic Algorithm ◽  
Weather Radars ◽  
Phase Alignment ◽  
Accumulated Rainfall ◽  
Anomalous Propagation

AbstractThe Royal Netherlands Meteorological Institute (KNMI) operates two dual-polarization C-band weather radars in simultaneous transmission and reception (STAR; i.e., horizontally and vertically polarized pulses are transmitted simultaneously) mode, providing 2D radar rainfall products. Despite the application of Doppler and speckle filtering, remaining nonmeteorological echoes (especially sea clutter) mainly due to anomalous propagation still pose a problem. This calls for additional filtering algorithms, which can be realized by means of polarimetry. Here we explore the effectiveness of the open-source wradlib fuzzy echo classification and clutter identification based on polarimetric moments. Based on our study, this has recently been extended with the depolarization ratio and clutter phase alignment as new decision variables. Optimal values for weights of the different membership functions and threshold are determined employing a 4-h calibration dataset from one radar. The method is applied to a full year of volumetric data from the two radars in the Dutch temperate climate. The verification focuses on the presence of remaining nonmeteorological echoes by mapping the number of exceedances of radar reflectivity factors for given thresholds. Moreover, accumulated rainfall maps are obtained to detect unrealistically large rainfall depths. The results are compared to those for which no further filtering has been applied. Verification against rain gauge data reveals that only a little precipitation is removed. Because the fuzzy logic algorithm removes many nonmeteorological echoes, the practice to composite data from both radars in logarithmic space to hide these echoes is abandoned and replaced by linearly averaging reflectivities.

A Preliminary Study of Meteotsunami Using Fuzzy Logic Algorithm over Sunda Strait, Indonesia

2020 ◽  
Author(s):  
Nur Arifin Akbar ◽  
Ema Utami ◽  
Wahyu Sasongko Putro ◽  
Zadrach Ledoufij Dupe ◽  
Andi Cahyadi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Algorithm ◽  
Preliminary Study

scholarly journals A fuzzy logic algorithm derived mechatronic concept prototype for crop damage avoidance during eco-friendly eradication of intra-row weeds

2020 ◽  
Vol 4 ◽  
pp. 116-126
Author(s):  
Satya Prakash Kumar ◽  
V.K. Tewari ◽  
Abhilash K. Chandel ◽  
C.R. Mehta ◽  
Brajesh Nare ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Crop Damage ◽  
Fuzzy Logic Algorithm ◽  
Damage Avoidance

scholarly journals Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization

2019 ◽  
Vol 11 (12) ◽  
pp. 1436 ◽  
Author(s):  
Skripniková ◽  
Řezáčová
Keyword(s):  
Comparative Analysis ◽  
Heavy Rain ◽  
Radar Data ◽  
Severe Weather ◽  
Detection Methods ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Rain Events ◽  
Single Polarization ◽  
Strong Agreement

The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm).

A Kalman filtering fuzzy logic algorithm for recognition of lane departure

2021 ◽  
pp. 1-8
Author(s):  
Kai Ren
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Kalman Filtering ◽  
Traffic Accidents ◽  
Region Of Interest ◽  
Fuzzy Logic Algorithm ◽  
The Road ◽  
Departure Decision ◽  
Lane Departure ◽  
Fusion Analysis

In all kinds of traffic accidents, the unconscious departure of the vehicle from the lane is one of the most important reasons leading to the occurrence of these accidents. In view of the specific problem of lane departure, a lane departure decision-making method is established without calibration relying on the Kalman filtering fuzzy logic algorithm, according to the characteristics of expressway lanes, based on the machine vision and hearing fusion analysis of lane departure, integrating the extraction of the linear lane line model and the region of interest (ROI) in this paper to judge the degree of vehicle departure from the lane by integrating the slope values of the 2 lane lines in the road image. The results show that the system has good lane recognition capabilities and accurate departure decision-making capabilities, and meet the lane departure warning requirements in the expressway environment.

Active control of flexible structures using a fuzzy logic algorithm

2002 ◽  
Vol 11 (4) ◽  
pp. 541-552 ◽  
Author(s):  
Kelly Cohen ◽  
Tanchum Weller ◽  
Joseph Z Ben-Asher
Keyword(s):  
Fuzzy Logic ◽  
Active Control ◽  
Flexible Structures ◽  
Fuzzy Logic Algorithm
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