摘要：Vehicle intelligent position systems based on Received Signal Strength Indicator (RSSI) in WirelessSensor Networks (WSNs) areefficientlyutilized. The vehicle's position accuracy is of great importance for transportation behaviors, such as dynamic vehicle routing problems and multiple pedestrian routing choice behaviors and so on. Therefore, a precise position and available optimization is necessary for total parameters of conventional RSSI model. In this papar, we investigate the experimental performance of translating the power measurements to corresponding distance between eachpair of nodes. The priori knowledge about the environment interference could impact the accuracy of vehicles's position and the reliability of paremeters greatly. Based on the real-world outdoor experiments, we compares different regression analysis of the RSSI model, in order to establish a calibration scheme on RSSI model. Empirical experimentation shows that the average errors of RSSI model is able to decrease throughout therules of environmental factor n and shadowing factor ηrespectively. Moreover, the calculation complexity is reduced, as aninnovative approach. Since variation tendency of environmental factor n, shadowing factor η with distance and signal strength could be simulated respectively, RSSI model fulfills the precision of the vehicle intelligent position system.
