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This paper proposes a received signal strength indicator (RSSI)-based maximum a posteriori (MAP) localization method with channel parameters estimation in wireless sensor networks. The proposed method makes use of not only likelihood value of the location of a target but also a priori knowledge of the target location. Furthermore, the proposed method also estimates channel model parameters with an...
RSSI (received signal strength indicator)-based localization is advantageous in terms of transceiver hardware complexity, because almost all current wireless standards have the functionality in their protocols. In a localization area, a ??design?? problem to determine the placement of anchor nodes satisfying a required localization accuracy has never been addressed, although the accuracy of RSSI method...
For a wirelessly networked robot swarm, to accomplish a unified task as a group, it is necessary to generate a set of common coordinates among all member robots and to notify each member robot of its heading direction in the generated common coordinates. However, when the member robots are not equipped with sensors to identify their locations or bearings, they can use only a function in the wireless...
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...
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