Research On Effects Of Measurement Errors On Localization Precision And The Improving Algorithm

With the rapid development of communication technologies, sensor technology, embedded computing technology, distributed information processing techniques, Wireless sensor network(WSN) is designed and developed. WSN is made up of wireless communication nodes in self-organization for monitoring detected region. As a kind of bran-new information acquisition and processing technology, wireless sensor networks have been proposed for various applications, including searching and rescuing, disaster relief, target tracking, and smart environments. Location awareness becomes an essential capability of sensor nodes. For example, it is also used in communication protocols that route to geographical areas. Measurement errors and localization algorithms have great impact on positioning precision. While different technologies of range measurement will lead to different error models. So it is significant to select appropriate localization algorithm of location. Based on different error models, the effect of different location algorithms on positioning error is studied in this paper and an improved positioning algorithm is offered.First, this paper described WSNs and positioning concepts and basic principles. Subsequently, this paper introduces many classical localization algorithms. We give conventional measurement error model according to different characteristics of different ranging technologies. We evaluate performance of a lot of classic localization algorithms based on different error models by simulate. The results of the simulation shows range measurement error models have great effects on positioning accuracy with better stability.Under the circumstances that there are many small obstacles or low accuracy in measurement, this paper consider improving localization algorithm. If the unknown node receive multiple signals for measuring distance, we can select the minimum or minor. The probability is much smaller than the measurement distance between a pair of nodes with obstacle is minimum and minor distance. In order to avoid abnormal error models, we sort the measuring distance and select the nodes with the minimum and minor distance to compute the location of the unknown node. We modify the RSSI signal attenuation models by adding ultrasonic transmitting and receiving device in a small amount of nodes. In the localization phase, in order to improve accuracy, we interpret RSSI signal vector and decrease the region.Finally, we summarize the whole paper. Of course, concrete study is still required in work or study.