AN APPROACH TO SOLVE THE PROBLEM OF DATA FUSION FOR MULTI-TARGET TRACKING USING FUZZY LOGIC

2020 ◽  
pp. 16-24
Author(s):  
Dang Quang Hieu
Keyword(s):  
Fuzzy Logic ◽  
Data Fusion ◽  
Target Tracking ◽  
Joint Probability ◽  
Identification Algorithm ◽  
Fuzzy Data ◽  
Fusion Algorithm ◽  
Logic Simulation ◽  
Multi Target Tracking ◽  
Better Than

This paper presents a multitarget data fusion (identification) algorithm called the Fuzzy data fusion algorithm (FDFA) for radar target tracking. This approach is formulated using the Kalman filter and FDF, the algorithm is accomplished by using fuzzy logic. Simulation results for cluttered conditions show that the proposed algorithm's performance is better than the probability data fusion (PDF) and the joint probability data fusion (JPDF) filters, which were presented in [2-4, 6].

Research of Asynchronous Multi-Type Sensors Data Fusion

2010 ◽  
Vol 142 ◽  
pp. 16-20
Author(s):  
Y. Qin ◽  
Xue Hui Wang ◽  
Ming Jun Feng ◽  
Zhen Zhou ◽  
L.J. Wang
Keyword(s):  
Data Fusion ◽  
Target Tracking ◽  
Tracking System ◽  
Control Process ◽  
Good Control ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Control Effect ◽  
Fusion Algorithm ◽  
Multi Target Tracking

A data fusion algorithm was established for estimating the state of target tracking system with multi-type sensor. Through Kalman filter regarding the multi-sensors to computer goal estimated value, it can obtain estimation value of goal at moment. And mean square deviation of fusion estimation value was smaller than single sensor's mean square deviation. The simulation results indicated that synchronisms data fusion method was effective to the multi-target tracking problem. Asynchronous multi-sensor fusion process can obtain good control effect in the practice control process.

Research on Moving Target Tracking Algorithm Based on Lidar and Visual Fusion

2018 ◽  
Vol 22 (5) ◽  
pp. 593-601
Author(s):  
Xiaoxiao Guo ◽  
Yuansheng Liu ◽  
Qixue Zhong ◽  
Mengna Chai ◽  
◽  
...  
Keyword(s):  
Target Tracking ◽  
Autonomous Vehicles ◽  
Joint Probability ◽  
Moving Target ◽  
Position Information ◽  
Moving Target Tracking ◽  
Decision Level ◽  
Multi Target Tracking ◽  
Single Sensor ◽  
Awareness System

Multi-sensor fusion and target tracking are two key technologies for the environmental awareness system of autonomous vehicles. In this paper, a moving target tracking method based on the fusion of Lidar and binocular camera is proposed. Firstly, the position information obtained by the two types of sensors is fused at decision level by using adaptive weighting algorithm, and then the Joint Probability Data Association (JPDA) algorithm is correlated with the result of fusion to achieve multi-target tracking. Tested at a curve in the campus and compared with the Extended Kalman Filter (EKF) algorithm, the experimental results show that this algorithm can effectively overcome the limitation of a single sensor and track more accurately.

Comparisons on Reliability of Likert Scale between Crisp and Fuzzy Data

2014 ◽  
Vol 635-637 ◽  
pp. 874-877
Author(s):  
Yuan Horng Lin ◽  
Jeng Ming Yih
Keyword(s):  
Fuzzy Logic ◽  
Survey Data ◽  
Likert Scale ◽  
Future Research ◽  
Fuzzy Data ◽  
Questionnaire Data ◽  
Human Thinking ◽  
Better Than

The purpose of this study is to compare the reliability of Likert scale between crisp and fuzzy data. The survey data is simulated based on two kinds of questionnaire data. They are questionnaire of crisp data and fuzzy data respectively. According to the viewpoints of fuzzy logic, human thinking is multi-value and fuzzy data will be more appropriate for survey. Therefore, it is proposed that the reliability from fuzzy data will be higher. Results of the simulation show that reliability of fuzzy data performs better than crisp data. Based on the findings of this study, some suggestions and recommendations are discussed for future research.

Class-Dependent Gating Algorithm in Data Association

2012 ◽  
Vol 546-547 ◽  
pp. 446-451
Author(s):  
Shu Yan Yu ◽  
Hong Wei Quan
Keyword(s):  
Target Tracking ◽  
Traditional Method ◽  
Measurement Information ◽  
Identity Information ◽  
Kinematic Measurement ◽  
Multi Target Tracking ◽  
Track Association ◽  
Simulation Scenario ◽  
Class Information ◽  
Better Than

Most conventional tracking gate algorithms only use the targets’ kinematic measurement information, which is typically resulted in great uncertainties of measurement-to-track association for multi-target tracking in clutter. The problem of constructing tracking gates using targets' class information is considered. The proposed algorithm integrates targets' identity information into the traditional tracking gating techniques. First, a class-dependent gate corresponding to each class of targets is developed. Second, the algorithm for constructing the class-dependent gate is given. Simulations are carried out to examine the proposed algorithm, where the simulation scenario shows that the measurement-to-track association using the class-dependent gating algorithm is significantly better than traditional method.

scholarly journals A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
ZiQi Hao ◽  
ZhenJiang Zhang ◽  
Han-Chieh Chao
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Data Fusion ◽  
Event Detection ◽  
Base Station ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Local Decision ◽  
Fuzzy Logic Method

As limited energy is one of the tough challenges in wireless sensor networks (WSN), energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We usek-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

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