Research on Underground Personnel Positioning Method Based on PSO-GSA Optimization

2016 ◽  
Vol 8 (4) ◽  
pp. 21-74
Author(s):  
Ye Liu ◽  
Tianze Li ◽  
Tao Gao ◽  
Yuhan Wang ◽  
JiaHui Chen
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Path Loss ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Experiment Platform ◽  
Positioning Method ◽  
Unknown Node ◽  
Weighted Centroid Localization ◽  
On Chip

In the case of coal mine accidents, in order to ensure timely rescue of the suffering people in a complex environment of underground localization, focusing on Received Signal Strength Indicator (RSSI) in underground personnel positioning accuracy is low and the problem of dynamic tracing parameters changes. Therefore, using an improved gravitational search algorithm (GSA) for the weighted centroid localization that based on RSSI. Utilizing the log distance path loss model gets the distance between the beacon nodes and unknown nodes, and then through the weighted centroid localization algorithm perform the unknown node positioning. Finally, the improved GSA-PSO optimizes the preliminary location results and parameters. Proposed solutions to establish simulation model is verified in MATLAB, and use the on-chip system CC2430 chips experiment platform is established. Experimental results show the proposed method can improve both the positioning accuracy effectively and the adaptive ability of changeful environment.

Research on Underground Personnel Positioning Method Based on PSO-GSA Optimization

2020 ◽  
pp. 1387-1439
Author(s):  
Ye Liu ◽  
Tianze Li ◽  
Tao Gao ◽  
Yuhan Wang ◽  
JiaHui Chen
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Experiment Platform ◽  
Positioning Method ◽  
Unknown Node ◽  
Weighted Centroid Localization ◽  
On Chip ◽  
Adaptive Ability

In the case of coal mine accidents, in order to ensure timely rescue of the suffering people in a complex environment of underground localization, focusing on Received Signal Strength Indicator (RSSI) in underground personnel positioning accuracy is low and the problem of dynamic tracing parameters changes. Therefore, using an improved gravitational search algorithm (GSA) for the weighted centroid localization that based on RSSI. Utilizing the log distance path loss model gets the distance between the beacon nodes and unknown nodes, and then through the weighted centroid localization algorithm perform the unknown node positioning. Finally, the improved GSA-PSO optimizes the preliminary location results and parameters. Proposed solutions to establish simulation model is verified in MATLAB, and use the on-chip system CC2430 chips experiment platform is established. Experimental results show the proposed method can improve both the positioning accuracy effectively and the adaptive ability of changeful environment.

An Improved Weighted Centroid Localization Algorithm in WSN

2014 ◽  
Vol 513-517 ◽  
pp. 3496-3499 ◽  
Author(s):  
Dong Yao Zou ◽  
He Lv ◽  
Dao Li Zheng ◽  
Teng Fei Han
Keyword(s):  
Localization Algorithm ◽  
Distributed Environment ◽  
Positioning Accuracy ◽  
Ranging Error ◽  
Beacon Node ◽  
Simulation Results ◽  
Unknown Node ◽  
Weighted Centroid Localization ◽  
Improved Algorithm

This paper presents a weighted centroid localization algorithm which based on the nearest beacon node. It utilizes Beacon nodes From the unknown node nearest to correct ranging error caused by RSSI distance, Increased unknown node capability which adapt neighboring nodes distributed environment. Improved the positioning accuracy,. Simulation results indicate that the Positioning accuracy of Improved Algorithm which is Proposed in this article has been significantly improved, the highest Enhanced can up to 30%.

scholarly journals Research on 3D Fingerprint Positioning Based on MIMO Base Station

2021 ◽  
Author(s):  
Chang Xia ◽  
Yijie Ren ◽  
Xiaojun Wang ◽  
Weiguang Sun ◽  
Fei Tang ◽  
...  
Keyword(s):  
Reference Point ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Base Station ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
High Base ◽  
Effective Use ◽  
Three Dimensional Space

The aim of this article is to solve the problem that the accuracy of traditional positioning algorithm decreases in complex environment and to provide some ideas for the few researches of fingerprint localization algorithm in three-dimensional space. This paper builds a system model in a three-dimensional space, provides three reference point distribution methods, and discusses the positioning performance under these distribution methods. After that, based on the high base station deployment density, multi-point fusion positioning method is used to locate the target, which further improves the positioning accuracy and makes more effective use of reference point resources. Finally, a backward-assisted positioning method is proposed, which uses the position information of the positioned points to assist the positioning of the current point. Research shows that this method can improve the positioning accuracy and has good versatility. (Foundation items: Social Development Projects of Jiangsu Science and Technology Department (No.BE2018704).)

scholarly journals Iterative L-M Algorithm in WSN – Utilizing Modifying Average Hopping Distances

2017 ◽  
Vol 13 (10) ◽  
pp. 4 ◽  
Author(s):  
Xin Qiao ◽  
Han-Sheng Yang ◽  
Zheng-Chuang Wang
Keyword(s):  
Data Exchange ◽  
Weighted Average ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Simulation Environment ◽  
Hop Count ◽  
Positioning Accuracy ◽  
Anchor Nodes ◽  
Simulation Results ◽  
Unknown Node

Wireless sensor networks are more and more important for various applications. Localization plays an important role in WSN. In this article,<strong> </strong>Aiming at the large errors that the DV—Hop localization algorithm have in net topology with randomly-distributed nodes, this paper proposed a CLDV-Hop algorithm in DV-Hop based on modifying average hopping distances. Firstly, hop count threshold is set to optimize the anchor node when data exchange. Then, according to the minimum mean square criteria and unknown nodes nearest three anchor nodes weighted average hop distance are selected as its average hop distance. Finally, L-M algorithm is used to optimize the coordinate of unknown node estimated by least squares. The simulation results show that, without increasing the overhead and the same conditions as the simulation environment, CLDV-Hop algorithm has higher positioning accuracy than existing improved algorithms, and compared with DV-Hop algorithm accuracy is improved by about 33% - 41%.

Iterated Hybrid Localization Algorithm for Random Wireless Sensor Networks Based on Centroid and DV-Hop Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1854-1857 ◽  
Author(s):  
Xin Hua Nie ◽  
Zhong Ming Pan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Hybrid Algorithm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Advantages And Disadvantages ◽  
Centroid Algorithm ◽  
Initial Location ◽  
Unknown Node

Localization has been a major challenge in Wireless Sensor Networks (WSNs), especially for the applications requiring the accurate position of the sensed information. In this paper, we propose a new localization algorithm based on the Centroid algorithm and the DV-Hop algorithm to improve the positioning accuracy without increasing any extra hardware for sensor nodes. This paper firstly analyzed the advantages and disadvantages of the centroid algorithm and the DV-Hop algorithm. Then we put forward an iterated hybrid algorithm, which is comprised of three steps. Firstly, obtaining the initial location of each unknown node by using the centroid algorithm; secondly, computing the distances among each unknown node to the anchor nodes based on the DV-Hop algorithm; finally, Taylor Series Expansion (TSE) algorithm is utilized to estimate coordinate of each unknown node. Simulation results show that our iterated hybrid algorithm has better positioning accuracy.

scholarly journals Positioning Method of Weighted Centroid Aided Inertial Measurement

2018 ◽  
Author(s):  
Qiang Liu ◽  
Xiaosu Xu ◽  
Tao Zhang
Keyword(s):  
Inertial Navigation System ◽  
Test Point ◽  
Localization Algorithm ◽  
Combined Model ◽  
Anchor Node ◽  
Inertial Measurement ◽  
Wireless Signal ◽  
Positioning Method ◽  
Anchor Nodes ◽  
Weighted Centroid Localization

Aiming at the defects of low precision and time cumulative error, an external wireless signal weighted centroid localization algorithm aided inertial positioning method is designed in this paper. According to the signal strength of each anchor node received at the test point, the distance between the anchor node and the anchor node is obtained by using the attenuation model of the wireless signal. Three anchor nodes are used to measure the distance between the anchor node and the measured point. We can obtain the area to be measured according to the actual situation, the position of the measured point is obtained by the weighted centroid localization algorithm and a combined model of wireless signal aided inertial navigation system is established. The simulation results show that the method can greatly improve the positioning accuracy and restrain the divergence of the longitude error and latitude error.

A Weighted Centroid Localization Algorithm Based on RSSI and Virtual Static Beacon Nodes

2014 ◽  
Vol 1077 ◽  
pp. 252-256
Author(s):  
Ying Kang Li ◽  
Hua Gang Shao ◽  
Sheng Zhe Ni
Keyword(s):  
Weight Coefficient ◽  
Localization Algorithm ◽  
Hardware Cost ◽  
Simulation Experiments ◽  
Positioning Accuracy ◽  
Centroid Algorithm ◽  
Weighted Centroid Localization

A large number of beacon nodes will be applied to the weighted centroid localization algorithm, but too much hardware facilities will affect the efficiency of the algorithm. Using the cosine theorem, On the basis of the original beacon nodes, add some virtual static beacon nodes to participate in orientation, It can reduce the hardware cost required for positioning. In addition, the math on and analysis of the localization algorithm, by derivative principle explaining the weight coefficient and RSSI ranging influence on localization algorithm. Simulation experiments show that the weighted centroid algorithm based on VSBN needed fewer beacon nodes, high positioning accuracy.

scholarly journals An Improved Three-dimensional DV-Hop Localization Algorithm Optimized by Adaptive Cuckoo Search Algorithm

2017 ◽  
Vol 13 (02) ◽  
pp. 102 ◽  
Author(s):  
Lieping Zhang ◽  
Fei Peng ◽  
Peng Cao ◽  
Wenjun Ji
Keyword(s):  
Search Algorithm ◽  
Three Dimensional ◽  
Cuckoo Search ◽  
Adaptive Strategy ◽  
Cuckoo Search Algorithm ◽  
Position Estimation ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Unknown Node

Aiming at the low accuracy of DV-Hop localization algorithm in three-dimensional localization of wireless sensor network, a DV-Hop localization algorithm optimized by adaptive cuckoo search algorithm was proposed in this paper. Firstly, an improved DV-Hop algorithm was proposed, which can reduce the localization error of DV-Hop algorithm by controlling the network topology and improving the method for calculating average hop distance. Meanwhile, aiming at the slow convergence in traditional cuckoo search algorithm, the adaptive strategy was improved for the step search strategy and the bird's nest recycling strategy. And the adaptive cuckoo search algorithm was introduced to the process of node localization to optimize the unknown node position estimation. The experiment results show that compared with the improved DV-Hop algorithm and the traditional DV-Hop algorithm, the DV-Hop algorithm optimized by adaptive cuckoo search algorithm improved the localization accuracy and reduced the localization errors.

scholarly journals Jammer Localization in Multihop Wireless Networks Based on Gravitational Search

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tongxiang Wang ◽  
Xianglin Wei ◽  
Jianhua Fan ◽  
Tao Liang
Keyword(s):  
Wireless Networks ◽  
Search Algorithm ◽  
Fitness Function ◽  
Gravitational Search Algorithm ◽  
Multihop Wireless Networks ◽  
Wireless Channel ◽  
Localization Algorithm ◽  
Multihop Wireless ◽  
Gravitational Search ◽  
Range Free

Multihop Wireless Networks (MHWNs) can be easily attacked by the jammer for their shared nature and open access to the wireless medium. The jamming attack may prevent the normal communication through occupying the same wireless channel of legal nodes. It is critical to locate the jammer accurately, which may provide necessary message for the implementation of antijamming mechanisms. However, current range-free methods are sensitive to the distribution of nodes and parameters of the jammer. In order to improve the localization accuracy, this article proposes a jammer localization method based on Gravitational Search Algorithm (GSA), which is a heuristic optimization evolutionary algorithm based on Newton’s law of universal gravitation and mass interactions. At first, the initial particles are selected randomly from the jammed area. Then, the fitness function is designed based on range-free method. At each iteration, the mass and position of the particles are updated. Finally, the position of particle with the maximum mass is considered as the estimated jammer’s position. A series of simulations are conducted to evaluate our proposed algorithms and the simulation results show that the GSA-based localization algorithm outperforms many state-of-the-art algorithms.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&amp;rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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