An adaptive fusion algorithm based on ANFIS for radar/infrared system

Yuan, Qi; Dong, Chaoyang; Wang, Q.

doi:10.1016/j.eswa.2007.09.026

Cited by 21 publications

(5 citation statements)

References 19 publications

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“…The last one seems to be the best. Also, this technique has been used in the improvement of the tracking for radar/infrared system [6]. These results show that the algorithm can effectively adjust the system and has a capability in resisting uncertain information.…”

Section: Introductionmentioning

confidence: 87%

Locating and Classifying Defects with Artificial Neural Networks

Avilés

Hernández-Gómez

Durodola

et al. 2008

AMM

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Abstract. Locating defects and classifying them by their size was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). Postulated void of three different sizes (1x1 mm, 2x2 mm and 2x1 mm) were introduced in a bar with and without a notch. The size of a defect and its localization in a bar change its natural frequencies. Accordingly, synthetic data was generated with the finite element method. A parametric analysis was carried out. Only one defect was taken into account and the first five natural frequencies were calculated. 495 cases were evaluated. All the input data was classified in three groups. Each one has 165 cases and corresponds to one of the three defects mentioned above. 395 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. This procedure was followed in the cases of the plain bar and a bar with a notch. In the next stage of this work, the ANN output was optimized with ANFIS. The accuracy of the localization and classifications of the defects was improved.

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Section: Introductionmentioning

confidence: 87%

Locating and Classifying Defects with Artificial Neural Networks

Avilés

Hernández-Gómez

Durodola

et al. 2008

AMM

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“…It can be seen that how to accurately complete the 3D dead reckoning of the pipeline mobile robot has become an urgent problem to be solved. Aiming at this problem, people usually use the organic integration of inertial sensor and ranging sensor to achieve multiclass data information fusion (Fan et al, 2019; Yuan et al, 2009), so as to improve the accuracy of dead reckoning of the wall‐climbing robot. However, there are two problems in the dead reckoning system based on the data fusion algorithm of these two kinds of sensors.…”

Section: Introductionmentioning

confidence: 99%

“…On this basis, they used the linear matrix equation matching derived from innovation sequence and residual sequence to solve the noise covariance, and matched the complex nonlinear noise covariance with the linear matrix equation. Three teams led by Q. Yuan et al (2009), Selma et al (2020), and Asar et al (2019) studied the effectiveness of the Adaptive Neuro‐Fuzzy Inference System (ANFIS) in practical application and proved that ANFIS is a special method that organically integrates artificial neural network (ANN) and fuzzy inference system (FIS). It can adjust the rule‐based FIS by using the mathematical characteristics of ANN, and has the learning mechanism of ANN and the linguistic reasoning ability of FIS, which can complement the shortcomings of the two algorithms and effectively solve the complex problems of nonlinear function modeling.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Wei

Zhang

2022

Journal of Field Robotics

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Dead reckoning accuracy determines the operation quality of pipeline mobile robot to a certain extent. With the progress of the industry, the research on the dead reckoning system of pipeline robot is more and more in-depth, but how to improve dead reckoning accuracy is still a difficult problem for pipeline mobile robot. In this paper, to overcome the sensor defects of the traditional dead reckoning system, an error backpropagation neural network (BPNN) is introduced to compensate the sensor error caused by static drift and poor dynamic response. To reduce the interference of noise information, the covariance matching technique based on the Adaptive Neuro-Fuzzy Inference System is used to estimate the measurement noise information. And then the two are combined in the algorithm based on the extended Kalman Filter (EKF) framework for data fusion. An EKF algorithm based on adaptive neural fuzzy combined with BPNN is proposed. Finally, a complete three-dimensional dead reckoning system is established by coordinate transformation. The motion experiment of the quadruped wall-climbing robot in the pipeline proves that the proximity L of the calculated trajectory is not more than 6.00%, and the lowest is 1.07%, which effectively improves the reliability, robustness, and positioning accuracy of the dead reckoning.

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“…The literature [9] examines several track association methods with different assumptions on the target distribution in a surveillance region and modifies the existing track fusion algorithm to account for the model mismatch among some of the local tracks. The literature [10] proposes an adaptive fusion algorithm based on adaptive neuro-fuzzy inference system for radar/infrared system to improve tracking ability, which combines the merits of fuzzy logic and neural network.…”

Section: Introductionmentioning

confidence: 99%

Track Association and Fusion Based on Information Demand Analysis

2009

2009 Fourth International Conference on Internet Computing for Science and Engineering

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The paper presents a track association and fusion algorithm based on information demand analysis in the multisensor and multi-target environment. Track association first is done to judge whether the tracks from the different sensors derive from the same target by the method of the nearest neighborhood. Then the algorithm discards the tracks with poor quality by analyzing information demand for the fusion and evaluating the quality of each track using the method of statistical variance analysis. Last, Kalman filter and Simple Fusion strategy are used for the state estimation fusion. Experiment results show the algorithm improves the precision of the final track during the process of track fusion.

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An adaptive fusion algorithm based on ANFIS for radar/infrared system

Cited by 21 publications

References 19 publications

Locating and Classifying Defects with Artificial Neural Networks

Locating and Classifying Defects with Artificial Neural Networks

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Track Association and Fusion Based on Information Demand Analysis

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