Analysis And Countermeasures About Multipath Reflections On The Impact Of WSN Nodes Ranging Accuracy

Localization algorithm based on Received Signal Strength Indication（RSSI）,with low power consumption, low cost and easy-to-realization, has become a worldwide research on localization algorithms in wireless sensor networks. However, it performs obvious location errors in real environment. So the accuracy is required to be improved.Distance measurement and localization are two aspects in localization algorithm based on RSSI. Each aspect could influent the accuracy of the whole location system. So far, researchers at home and abroad in this field pay far more attention on localization algorithm rather than distance measurement. With the influence of multipath reflection in wireless sensor networks, it is truly hard to get a high-ranging accuracy distance between two sensors. According to problem mentioned above, this paper focus on the study of distance measurement. The main contents are as follows:1. According to the distance measurement based on RSSI, the multipath reflection affects the ranging accuracy. This article conducts a lot of experiments to obtain the relationship between the RSSI value and the distance d. On base of the data above, the article analyzes the influence of ranging accuracy caused by environment.2. In order to decrease the evident ranging errors caused by the least square fitting method, this article introduces Empirical Mode Decomposition（EMD）algorithm to match the RSSI-d curve. Firstly, environment element is separated from the RSSI-d curve by EMD algorithm.Secondly, the distance is calculated from the fitted RSSI-d curve. At last, EMD algorithm fitting method is compared with the traditional curve fitting processing method. It turns out that the ranging accuracy in EMD algorithm fitting method is superior to the traditional least square fitting method.3. To solve the issues of no real-time in EMD algorithm, the article puts forward Kalman filter algorithm in the analysis of distance measurement based on RSSI. At any time, the system can achieve the optimal estimation of RSSI value on present moment by Kalman filter as long as the optimal estimation of RSSI value on last moment and measurement value on present moment are known. This algorithm just needs the memory to store the optimal estimation of RSSI value on last moment rather than all of the past RSSI value when it is used to estimate the distance. Experimental results show that the Kalman filter algorithm not only improves the ranging precision but also solves the issues of no real-time.