Research On Indoor Device-free Passive Localization Algorithm Integrating Fingerprinting Method And Geometric Method

In recent years,the wireless sensor network has ushered in a blowout development,and that Device-free Passive localization technology locates targets through a wireless sensor network has become an emerging issue.Different from Device-base Active localization,the principle of Device-free Passive localization is as follows: When the target enters the wireless sensor network,it will cause the RSSI(Received Signal Strength Indication)value of the link signal in the sensor network to change.The RSSI values before and after the target enters the sensor network will be compared and analyzed to estimate the target's position.The technology can locate a target without carrying any electronic device,and it has great research value and significance in the fields of target monitoring,security,disaster relief,etc.As a result,an upsurge of research on Device-free Passive localization algorithm has been set off.In Device-free Passive localization,Fingerprinting method and Geometric method are widely used by researchers because of their easy implementation and low cost.However,these two methods also have some disadvantages.Fingerprint matching requires a large amount of calculations in Fingerprinting method.Euclidean distance can be used to measure the distance between two spatial position vectors,but it is used by most Fingerprinting methods to match the fingerprint vectors with non-spatial attributes.Geometric method localization is greatly affected by the environment layout and its stability is poor.Therefore,this thesis investigates these issues.This thesis firstly summarizes the wireless sensor network and analyzes the principle of Device-free Passive localization based on RSSI.In addition,comparing the advantages and disadvantages of Device-free Passive localization based on Fingerprinting method and Devicefree Passive localization based on Geometric method.For Fingerprinting method,it is unreasonable that Euclidean distance method is used to match the fingerprint vectors with nonspatial attributes.Therefore,this thesis introduces Spearman correlation coefficient method in the matching method of Fingerprinting method.Using the characteristic that Spearman correlation coefficient method is applicable to fingerprint vectors with non-spatial attributes and it matches the fingerprint vector by estimating the correlation between the two vectors improves the positioning accuracy of Fingerprinting method.At the same time,we use the CC2530 development board as the hardware development platform,BasicRF protocol as wireless network protocol and develop programs in the IAR Embedded Workbench IDE to build a wireless sensor network experiment system based on ZigBee and design a Device-free Passive localization experimental verification system based on RSSI.The simulation and calculation of algorithm are carrying out on MATLAB platform.The experimental results show that Spearman correlation coefficient method has a smaller average positioning error and a higher positioning accuracy than Euclidean distance method.Although Fingerprinting method has higher positioning accuracy and better stability,there are many calculations for fingerprint matching.Because Geometric method is simple and convenient,has a small amount of calculation,and it can quickly locate the approximate position of the target,we combine improved Fingerprinting method and Geometric method to research and propose the indoor Device-free Passive localization algorithm integrating Fingerprinting method and Geometric method(F&G method)based on RSSI.F&G method firstly determines the approximate location of the target,and reduces the area where the target is located by Geometric method.Then it uses Fingerprinting method to locate in the reduced area.Finally,the position coordinates determined by the two methods are averaged to obtain the final position of the target.The experimental results show that F&G method has achieved satisfactory positioning accuracy with less calculations compared with Fingerprinting method and Geometric method.