Ad-hoc Kalman Filter Based Fusion Algorithm for Real-Time Wireless Sensor Data Integration

2015 ◽  
pp. 151-159 ◽  
Author(s):  
Alexander Alexandrov
Keyword(s):  
Kalman Filter ◽  
Data Integration ◽  
Real Time ◽  
Ad Hoc ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Fusion Algorithm

scholarly journals Development of Liquid Capacity Measuring Algorithm Based on Data Integration from Multiple Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Kiwoong Park ◽  
Si-Kyoung Lee ◽  
Hyeon Cheol Kim
Keyword(s):  
Kalman Filter ◽  
Data Integration ◽  
Real Time ◽  
Rotation Angle ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Complementary Filter ◽  
Level Sensor

This research proposes an algorithm using a process of integrating data from multiple sensors to measure the liquid capacity in real time regardless of the position of the liquid tank. The algorithm for measuring the capacity was created with a complementary filter using a Kalman filter in order to revise the level sensor data and IMU sensor data. The measuring precision of the proposed algorithm was assessed through repetitive experiments by varying the liquid capacity and the rotation angle of the liquid tank. The measurements of the capacity within the liquid tank were precise, even when the liquid tank was rotated. Using the proposed algorithm, one can obtain highly precise measurements, and it is affordable since an existing level sensor is used.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

scholarly journals An Integrated Adaptive Kalman Filter for High-Speed UAVs

2019 ◽  
Vol 9 (9) ◽  
pp. 1916 ◽  
Author(s):  
Tiantian Huang ◽  
Hui Jiang ◽  
Zhuoyang Zou ◽  
Lingyun Ye ◽  
Kaichen Song
Keyword(s):  
Kalman Filter ◽  
High Speed ◽  
Sensor Data ◽  
Adaptive Kalman Filter ◽  
Fusion Algorithm ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle ◽  
Robust Adaptive ◽  
Tracking Filter ◽  
Filter Algorithms

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

scholarly journals A Cloud Based Disaster Management System

2020 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Omar Cheikhrouhou ◽  
Anis Koubaa ◽  
Anis Zarrad
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Game Engine ◽  
New Paradigm ◽  
Lossy Networks ◽  
Training Environment ◽  
3D Environment

The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.

scholarly journals A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3638 ◽  
Author(s):  
Yan Wang ◽  
Huihui Jie ◽  
Long Cheng
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Extended Kalman Filter ◽  
Wireless Sensor ◽  
Multiple Model ◽  
Fusion Algorithm ◽  
Time Step ◽  
Localization Method ◽  
Track Quality

As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.

scholarly journals A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2555 ◽  
Author(s):  
Tengyue Zou ◽  
Yuanxia Wang ◽  
Mengyi Wang ◽  
Shouying Lin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Real Time ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Weighted Data

scholarly journals Design and Simulation of a Wireless Sensor Network Greenhouse-Monitoring System Based on 3G Network Communication

2016 ◽  
Vol 12 (05) ◽  
pp. 48 ◽  
Author(s):  
Y. H. Zhou ◽  
J. G. Duan
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Communication ◽  
Target Area ◽  
Wireless Communication Network ◽  
Environmental Monitoring System

A greenhouse provides a stable and suitable environment for the growth of plants. Temperature and humidity are closely related to plant growth. These factors directly affect the water content of plants and the quality of fruits. To solve the problems in the current monitoring system of greenhouse cultivation, such as complicated wiring, large node power consumption, and so on, this study proposes a wireless sensor network greenhouse-monitoring system based on third-generation network communication for the real-time monitoring of the temperature, humidity, light, and CO<sub>2</sub> concentration in a greenhouse. GS1011M is regarded as the core in developing wireless terminal nodes. PC software is used to build a real-time observation platform. Sensor data are received in real time through a wireless communication network to complete the monitoring of the target area. A simulation research is also conducted. Results show that the power dissipation of the greenhouse environmental monitoring system is low, its data accuracy is high, and its operation is stable.

scholarly journals Real-time Resource Positioning System based on Wireless Sensor Network in Manufacturing Workshop

2017 ◽  
Vol 13 (06) ◽  
pp. 96
Author(s):  
Li Shaobo ◽  
Qu Jinglei ◽  
Zhang Chenglong
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Electromagnetic Interference ◽  
Evidence Theory ◽  
Wireless Sensor ◽  
Prototype System ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Time Resource

Discrete manufacturing enterprise has a complex and varied production process, which causes manufacturing resources have dynamic characteristics. Aiming at the efficient collect and management of manufacturing resource information, improve the enterprise’ intellectualization, a real-time resource positioning system based on wireless sensor network was proposed. Firstly, a perceptual model for resource positioning was designed, which can collect and analysis real-time resources information in the workshop. Meanwhile, the architectural structure of real-time resources positioning system was designed based on wireless sensor network and the resources positioning flow was illustrated. Aiming at the low positioning accuracy caused by electromagnetic interference and obstacle in manufacturing workshop environment, a multi-sensor positioning data fusion algorithm based on fuzzy evidence theory was proposed. Finally, a prototype system is implemented to demonstrate the validity of the method in practice.

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