1. Multiple Target Localization Based on Alternate Iteration in Wireless Sensor Networks
Zhongyou Song, Jie Li , Yuanhong Zhong, Yao Zhou

2. I am sorry for not attending the conference.
Sorry for Absence
I am sorry for not attending the conference.
My paper was submitted in 18 November 2016, and received in 21 November. It needs time for going abroad application and relevant formalities dealing with. It is too late to handle that, so I miss the data of the conference.
Zhongyou Song
28 November 2016

3. Introduction System Modeling Proposed Method Simulation Analysis
Contents
Introduction
System Modeling
Proposed Method
Simulation Analysis
Conclusion

4. Introduction
Introdu-ction
Target localization based on WSNs has been widely used in robot navigation, geographic routing, environmental monitoring and vehicle tracking, etc.
Compressive sensing theory brings new opportunities to target localization in WSNs and proves an idea of localization via spatial sparsity.
Target localization algorithm based on CS only samples a small number of data, completes data compression simultaneously, and reduces the requirement for sensor nodes to simple and cheap.
System Modeling
Proposed Method
Simulation Analysis
Conclu-sion

5. Introduction System Modeling Proposed Method Simulation Analysis
contents
Introduction
System Modeling
Proposed Method
Simulation Analysis
Conclusion

6. System Modeling
Basic Knowledge
Introdu-ction
Compressive Sensing Theory
Y is the observed matrix, Φ is measurement matrix, X is the signal to be recovered.
When X is with sparsity, X can be recovered only by the design of Φ and a small number of sample signal Y.
It is superior to a large number of high-speed sampling based on Nyquist-Shannon sampling theorem.
System Modeling
Proposed Method
Simulation Analysis
Conclu-sion

7. System Modeling
Basic Knowledge
Introdu-ction
Signal Attenuation Model
Pm,n is the Received Signal Strength received by sensor m and sent by grid target n, Dm,n is Euclidean distance of sensor m and target n in the grid.
xm and ym are the coordinates of sensor m, xn and yn are the coordinates of target n.
System Modeling
Proposed Method
Simulation Analysis
Conclu-sion

8. System Modeling
System Modeling
Introdu-ction
System Modeling
Proposed Method
Localization area is evenly divided into a discrete grid with N points.
K targets whose positions are unknown and M sensors whose positions are known .
The goal is to determine the index of grid where targets exist.
Simulation Analysis
Conclu-sion

9. System Modeling
System Modeling
Introdu-ction
, when there is a target in the grid, xn=1, else xn=0.
Y is got by sensors collecting and accumulating the signals intensity.
Φ is equal to
The goal is to recover X.
System Modeling
Proposed Method
Simulation Analysis
Conclu-sion

10. Introduction System Modeling Proposed Method Simulation Analysis
Contents
Introduction
System Modeling
Proposed Method
Simulation Analysis
Conclusion

11. Signal Reconstruction
Proposed Method
Introdu-ction
Rough Localization
Compressive Sampling
Signal Reconstruction
Fine Localization
Alternate Iteration
Diamond Search
System Modeling
Proposed Method
Simulation Analysis
Conclu-sion
Diamond Search Mode

12. Flow Chart of Alternate Iteration Method
Proposed Method
Introdu-ction
System Modeling
Flow Chart of Alternate Iteration Method
Proposed Method
Simulation Analysis
Conclu-sion

13. Introduction System Modeling Proposed Method Simulation Analysis
Contents
Introduction
System Modeling
Proposed Method
Simulation Analysis
Conclusion

14. Targets are in the Center of Grid Introdu-ction
Simulation Analysis
Targets are in the Center of Grid
Introdu-ction
Localization results in three different situations.
Localization effect decreased with noise increasing.
When targets gather together, localization result is worse.
System Modeling
Proposed Method
K=15
M=49
Simulation Analysis
Conclu-sion
Localization Result

15. Targets are in the Center of Grid Introdu-ction
Simulation Analysis
Targets are in the Center of Grid
Introdu-ction
200 Monte-Carlo experiments are conducted.
Localization error increases as the number of targets increasing.
CS-based localization algorithm can obtain high localization accuracy in high degree of sparsity.
System Modeling
Proposed Method
K=1~20
M=49
Simulation Analysis
Conclu-sion
Localization Error

16. Targets are not in the Center of Grid Introdu-ction
Simulation Analysis
Targets are not in the Center of Grid
Introdu-ction
Three localization algorithms are compared.
The proposed method has best performance in localization accuracy under high SNR.
System Modeling
Proposed Method
K=7
M=49
SNR=25dB
Simulation Analysis
Conclu-sion
Localization Result

17. Introduction System Modeling Proposed Method Simulation Analysis
Contents
Introduction
System Modeling
Proposed Method
Simulation Analysis
Conclusion

18. Conclusion
Introdu-ction
A method of multiple target localization based on CS theory in WSNs, which combines CS theory with the alternate iteration method.
The proposed method divides localization process into two stages rough localization and fine localization.
Simulation results have demonstrated that the proposed method have good performance.
In the future, we will work towards on the computational complexity.
System Modeling
Proposed Method
Simulation Analysis
Conclu-sion

19. contents
Thank you