Tetrahedron-Constraint Least Square Localization Algorithm in Mixed LOS/NLOS Scenario Based on TDOA Measurements

2016 ◽  
Author(s):  
Zhengguo Cai ◽  
Xuguang Yang ◽  
Xukang Wu
Keyword(s):  
Least Square ◽  
Localization Algorithm

Video Nodes Based Collaborative Object Localization Framework in Wireless Multimedia Sensor Networks

2011 ◽  
Vol 317-319 ◽  
pp. 1078-1083 ◽  
Author(s):  
Qing Tao Lin ◽  
Xiang Bing Zeng ◽  
Xiao Feng Jiang ◽  
Xin Yu Jin
Keyword(s):  
Cluster Head ◽  
Computation Time ◽  
Least Square Method ◽  
Least Square ◽  
Object Localization ◽  
Wireless Multimedia ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Multimedia Sensor Networks ◽  
Localization Model

This paper establishes a 3-D localization model and based on this model, it proposes a collaborative localization framework. In this framework, node that observes the object sends its attitude information and the relative position of the object's projection in its camera to the cluster head. The cluster head adopts an algorithm proposed in this paper to select some nodes to participate localization. The localization algorithm is based on least square method. Because the localization framework is based on a 3-D model, the size of the object or other prerequisites is not necessary. At the end of this paper, a simulation is taken on the numbers of nodes selected to locate and the localization accuracy. The result implies that selecting 3~4 nodes is proper. The theoretical analysis and the simulation result also imply that a const computation time cost is paid in this framework with a high localization accuracy (in our simulation environment, a 0.01 meter error).

scholarly journals 3D Localization Algorithm Based on Linear Regression and Least Squares in NLOS Environments

2018 ◽  
Vol 11 (4) ◽  
pp. 1
Author(s):  
Jiang Li ◽  
Zhang Lei
Keyword(s):  
Linear Regression ◽  
Three Dimensional ◽  
Least Square Method ◽  
Mobile Terminal ◽  
Base Station ◽  
Least Square ◽  
Time Of Arrival ◽  
Localization Algorithm ◽  
Positional Accuracy ◽  
The Relationship

Based on the positive bias property of the time of arrival(TOA) measurement error caused by the non-line-of-sight(NLOS) propagation, a simple and effective three dimensional(3D) geometrical localization algorithm was proposed, the algorithm needs no prior knowledge of time delay distribution of TOA, and only linear regression was used to estimate the parameters of the relationship between the NLOS distance error and the true distance, thus, the approximate real distance between mobile terminal (MT) and base station (BS) was reduced, then, the 3D geometric localization of mobile terminal was carried out by the least square method. The experimental results shows the effectiveness of the algorithm, and the positional accuracy is far higher than the required accuracy by E-911 in NLOS environments.

scholarly journals Node Localization of Wireless Sensor Networks Based on Hybrid Bat-Quasi-Newton Algorithm

2015 ◽  
Vol 11 (6) ◽  
pp. 38 ◽  
Author(s):  
Shunyuan Sun ◽  
Baoguo Xu
Keyword(s):  
Least Square Method ◽  
Bat Algorithm ◽  
Least Square ◽  
Localization Algorithm ◽  
Positioning Precision ◽  
Newton Algorithm ◽  
The Third ◽  
Third Stage ◽  
Node Location ◽  
Quasi Newton

Concerning the problem that the least square method in the third stage of DV-Hop algorithm has low positioning accuracy, a localization algorithm was proposed which is the fusion of hybrid bat-quasi-Newton algorithm and DV-Hop algorithm. First of all, the Bat Algorithm ( BA) was improved from two aspects: firstly, the random vector β was adjusted adaptively according to bats fitness so that the pulse frequency had the adaptive ability. Secondly, bats were guided to move by the average position of all the best individuals before the current iteration so that the speed had variable performance; Then in the third stage of DV-Hop algorithm the improved bat algorithm was used to estimate node location and then quasi-Newton algorithm was used to continue searching for the node location from the estimated location as the initial searching point. The simulation results show that, compared with the traditional DV-Hop algorithm and the improved algorithm of DV-Hop based on bat algorithm( BADV-Hop) , positioning precision of the proposed algorithm increases about 16. 5% and 5. 18%, and the algorithm has better stability, it is suitable for high positioning precision and stability situation.

scholarly journals Efficient Iris Localization via Optimization Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qi Wang ◽  
Zhipeng Liu ◽  
Shu Tong ◽  
Yuqi Yang ◽  
Xiangde Zhang
Keyword(s):  
Optimization Model ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Descent Method ◽  
Least Square ◽  
Experimental Result ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Supervised Descent Method

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

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