Design And Optimization Of Wsn-based Targeting System

Wireless sensor networks have developed rapidly in recent years, and theyare comprehensive research fields which integrated of computer technology,wireless communications technology, modern sensor technology, and embeddedcomputing technology. It has a broad space for development in the field ofnational defense construction, modern healthcare, smart home, environmentmonitoring, etc. The hot technology, such as network targeting and tracking,environmental information collection and monitoring relates to wireless sensornetwork positioning, has become even more attention. Therefore, there has agreat theoretical value and wide practical application space on the research ofwireless sensor network positioning technology.Based on the analysis of wireless sensor network and its positioningtechnology, aiming at the characteristics and needs of the physical environment,the article carried out the distance estimation method based on RSSI rangingbetween nodes, also made a research on wireless sensor network positioning andalgorithm optimization, developed a positioning system that can actually be used.The thesis used JN5139communication module as the hardware platform,and designed program with CODEBLOCKS integrated development environment.It also realized efficient communication mechanism based on ZigBee protocol,enhanced the reliability of the wireless sensor network communication. Aftercomparative analysis of various types of positioning algorithm’s applicableoccasions and features, the paper selected location algorithm based on RSSIranging. Combined with the actual environment of the wireless signal strengthattenuation model, the dissertation used the classic lognormal distribution modelmodeling the relationship between RSSI and node distance. Finally, the paperanalyzes the characteristics of the maximum likelihood positioning algorithmused by system through the actual environmental system localization experiments,as well as sources of error, also presents the optimization algorithm to solve theproblem of error superimposed generated by Maximum Likelihood positioningalgorithm, strengthening system positioning’s accuracy. Experimental resultsshow that the optimization algorithm’s error accuracy used by positioning systemcan be reduced to0.3m in the actual environment within the region.