A Simple Outlier Data Rejection Algorithm for An RSSI-Based ML Location Estimation in Wireless Sensor Networks

This paper proposes a simple outlier data rejection algorithm for a received signal strength indicator (RSSI)-based maximum likelihood (ML) location estimation in wireless sensor networks. The RSSI-based ML location method usually requires a pre-determined statistical model on the variation of RSSI in a sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As a result, the effect of the outlier RSSI data worsens the estimation accuracy. In order to improve the estimation accuracy, the proposed algorithm intentionally rejects such outlier RSSIs data. From our experiments, we show the proposed algorithm performs better with much less computational complexity than a previously proposed outlier RSSI data rejection algorithm.