Air Object Height Estimation with 2-D Radars using Fuzzy Logic

2011 ◽  
Vol 61 (5) ◽  
pp. 485
Author(s):  
SGK Murthy ◽  
M.V.R. Murthy
Keyword(s):  
Fuzzy Logic ◽  
Geometric Method ◽  
Estimation Accuracy ◽  
Geometric Features ◽  
Science Journal ◽  
Tracking Systems ◽  
Height Estimation ◽  
Radar Tracking ◽  
Aerospace Applications ◽  
Different Types

<p>Multi sensor tracking is a widely used technique in aerospace applications to estimate the target kinematics precisely. Particularly naval-based tracking systems utilize, different types of Radars (2-D, 3-D) in multi sensor tracking scenario for robust estimation. As the supplied information from 2-D Radar contains only range and azimuth values, it is difficult to estimate the height of an air object using 2-D Radar. In order to over come the limitation, a geometric method is considered to combine the information obtained from two 2-D Radars located in two different locations. As the solution of the geometric method depends upon certain geometric features, it is not possible to get good results with one pair of sensors. However to obtain better results, it is proposed and experimented more than two 2-D Radars that combined with a fuzzy logic based validation. This paper discusses the issues related to 2-D Radar tracking and the method comprising Triangulation geometry and fuzzy logic based validation method to improve the height estimation accuracy in real time.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.485-490</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.403</strong></strong></p>

scholarly journals INS/GPS for High-Dynamic UAV-Based Applications

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Junchuan Zhou ◽  
Stefan Knedlik ◽  
Otmar Loffeld
Keyword(s):  
Carrier Phase ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Processing Load ◽  
Estimation Errors ◽  
Integration Interval ◽  
Sequential Processing ◽  
Large Velocity ◽  
Different Types ◽  
High Dynamic

The carrier-phase-derived delta pseudorange measurements are often used for velocity determination. However, it is a type of integrated measurements with errors strongly related to pseudorange errors at the start and end of the integration interval. Conventional methods circumvent these errors with approximations, which may lead to large velocity estimation errors in high-dynamic applications. In this paper, we employ the extra states to “remember” the pseudorange errors at the start point of the integration interval. Sequential processing is employed for reducing the processing load. Simulations are performed based on a field-collected UAV trajectory. Numerical results show that the correct handling of errors involved in the delta pseudorange measurements is critical for high-dynamic applications. Besides, sequential processing can update different types of measurements without degrading the system estimation accuracy, if certain conditions are met.

scholarly journals Neural Network Application for Netted Radar Information Processing Systems

2013 ◽  
Vol 347-350 ◽  
pp. 2156-2159
Author(s):  
Jian Hu ◽  
Fan Jun Hu
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Simulation Method ◽  
Tracking Systems ◽  
Radar Tracking ◽  
Radar Systems ◽  
The Neural Network ◽  
Network Method ◽  
Network Application ◽  
Radar Information

This paper discusses the neural network application for the information processing in the netted radar tracking systems compared with the problems of the conventional radar information processing. And then test the neural network using simulation method. The simulation result shows that the neural network method can perfectly solve the target tracking problems in the netted radar systems.

scholarly journals Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 146 ◽  
Author(s):  
Longfei Zhou ◽  
Xiaohe Gu ◽  
Shu Cheng ◽  
Guijun Yang ◽  
Meiyan Shu ◽  
...  
Keyword(s):  
Plant Height ◽  
Maize Yield ◽  
Canopy Height ◽  
Estimation Accuracy ◽  
Yield Reduction ◽  
Lidar Data ◽  
Height Estimation ◽  
Aerial Vehicle ◽  
Yield Quality ◽  
Maize Plants

Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R2 = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.

Different Types of Fuzzy Logic in Obstacles Avoidance of Mobile Robot

2018 ◽  
Author(s):  
Andry Meylani ◽  
Ade Silvia Handayani ◽  
Ciksadan ◽  
Carlos R.S. ◽  
Nyayu Latifah Husni ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Different Types

An approach to identification of variances for radar tracking systems

2002 ◽  
Vol 82 (6) ◽  
pp. 875-879
Author(s):  
Jianping Yao ◽  
Leonard Chin ◽  
Weixian Liu ◽  
Yilong Lu
Keyword(s):  
Tracking Systems ◽  
Radar Tracking

A FUZZY LOGIC APPROACH TO TOPIC EXTRACTION IN TEXTS

2009 ◽  
Vol 17 (supp01) ◽  
pp. 81-112 ◽  
Author(s):  
OURDIA BOUIDGHAGHEN ◽  
MOHAND BOUGHANEM ◽  
HENRI PRADE ◽  
IHAB MALLAK
Keyword(s):  
Fuzzy Logic ◽  
Preliminary Investigation ◽  
Frequency Of Occurrence ◽  
Topic Extraction ◽  
Preliminary Step ◽  
Different Types ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Potential Methods ◽  
General Ontology

The paper presents a preliminary investigation of potential methods for extracting semantic views of text contents under the form of structured sets of words, which go beyond standard statistical indexing. The aim is to build kinds of fuzzily weighted structured images of semantic contents. A preliminary step consists in identifying the different types of relations (is-a, part-of, related-to, synonymy, domain, glossary relations) that exist between the words of a text, using some general ontology such as WordNet. Then taking advantage of these relations, different types of fuzzy clusters of words can be built. Moreover, apart from its frequency of occurrence, the importance of a word may be also evaluated through some estimate of its specificity. A degree of "centrality" is also computed for each word in a cluster. The size of the clusters, the frequency, the specificity and the centrality of their words are indications that enable us to build a fuzzy set of sets of words that progressively "emerge" from a text, as being representative of its contents. The ideas advocated in the paper and their potential usefulness are illustrated on a running example and on two experiments. It is expected that obtaining a better representation of the semantic contents of texts may help in particular to give indications of what the text is about to a potential reader.

ADAPTIVE FUZZY LOGIC-BASED FRAMEWORK FOR HANDLING IMPRECISION AND UNCERTAINTY IN CLASSIFICATION OF BIOINFORMATICS DATASETS

2011 ◽  
Vol 08 (03) ◽  
pp. 513-534
Author(s):  
TAREK HELMY ◽  
ZEHASHEEM RASHEED ◽  
MOHAMED AL-MULHEM
Keyword(s):  
Fuzzy Logic ◽  
Classification Accuracy ◽  
Empirical Studies ◽  
Data Sets ◽  
Training Algorithms ◽  
Classification Framework ◽  
Different Types ◽  
Adaptive Fuzzy Logic ◽  
And Performance ◽  
The Impact

Classification in the emerging field of bioinformatics is a challenging task, because the information about different diseases is either insufficient or lacking in authenticity as data is collected from different types of medical equipments. In addition, the limitation of human expertise in manual diagnoses leads to incorrect diagnoses. Moreover, the information gathered from various sources is subject to imprecision and uncertainty. Imprecision arises when the data is not validated by experts. This paper presents an adaptive Type-2 Fuzzy Logic System-based (FLS) classification framework for multivariate data to diagnose different types of diseases. This framework is capable of handling imprecision and uncertainty, and its classification accuracy and performance are measured by using University of California Irvine (UCI), well-known medical data sets. The results are compared with the most common existing classifiers in both computer science and statistics literatures. This classification is performed based on the nature of inputs (e.g., singleton or nonsingleton) and on whether uncertainty is present in the system or absent. Empirical results have shown that our proposed FLS classification framework outperforms earlier implemented models with better classification accuracy. In addition, we conducted empirical studies on this classifier regarding the impact of various parameters of FLS such as training algorithms and defuzzification methods.

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