scholarly journals Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 387
Author(s):  
Krystian Chachuła ◽  
Tomasz Michał Słojewski ◽  
Robert Nowak
Keyword(s):  
Data Fusion ◽  
Wastewater Treatment Plants ◽  
Fusion Algorithm ◽  
Network Nodes ◽  
Heterogeneous Sensors ◽  
Multisensor Data Fusion ◽  
Event Reconstruction ◽  
Adaptive Peak ◽  
And Performance ◽  
Multisensor Data

Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks.

scholarly journals Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huadong Wang ◽  
Shi Dong
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Observational Data ◽  
Measurement System ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Multiple Sensors ◽  
Multisensor Data

In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

scholarly journals A New View of Multisensor Data Fusion: Research on Generalized Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Guo Chen ◽  
Zhigui Liu ◽  
Guang Yu ◽  
Jianhong Liang
Keyword(s):  
Data Fusion ◽  
Heterogeneous Data ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Symbolic Computing ◽  
Multisensor Data Fusion ◽  
Definition Of ◽  
Future Work ◽  
Multisensor Data

Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

scholarly journals Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Waleed A. Abdulhafiz ◽  
Alaa Khamis
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Data Uncertainty ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Combining Data ◽  
Bayesian Fusion ◽  
The Individual ◽  
Multisensor Data

Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data.

Multisensor Data Fusion Applied to Marine Integrated Navigation Systems

2005 ◽  
Vol 219 (3) ◽  
pp. 121-130
Author(s):  
A Tiano ◽  
A Zirilli ◽  
M Cuneo ◽  
S Pagnan
Keyword(s):  
Data Fusion ◽  
Theoretical Background ◽  
Accurate Estimation ◽  
Positioning System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
State Variables ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Multisensor Data

This paper deals with the problem of designing a flexible and accurate integrated navigation system (INS) for marine craft. The proposed INS is based on the integration of a global positioning system (GPS) with a compass and a speed log. After introducing the scopes and functions of the proposed INS, mathematical models of its main components are presented. Then the development of a new multisensor data fusion algorithm for carrying out an accurate estimation of the main state variables is presented. The theoretical background for the sensor fusion is based on the classical Kalman filter theory, which allows to update an a priori position estimate, given by a dead-reckoning system, with the information supplied by a GPS positioning system. Finally the filtering algorithm is extended in the framework of interval analysis and fuzzy set theory in order to improve the reliability and robustness of the estimation algorithm. The validity of the proposed approach is demonstrated by simulation examples applied to a container ship navigating in realistic conditions.

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