Wireless Received Signal Strength Research Articles

A Method For Guidance of a Wheeled Mobile Robot Based on Received Radio Signal Strength Measurements

We propose a new guidance law for the problem of wheeled mobile robot (WMR) navigation towards an unknown target based on Received Signal Strength (RSS) Information. Given a mobile robot moving with a constant linear velocity, we use the miss-distance derivative as a measure for the angle at which the robot approaches the target. Miss-distance derivative is estimated from RSS, using a Robust Extended Kalman Filter (REKF). Having applied the proposed steering control law, termed Equiangular Navigation Guidance (ENG), the robot approaches the stationary or maneuvering target along a semi-equiangular spiral and eventually goes into a circular trajectory around it. In order to avoid circling, the algorithm is modified to decrease the robot's linear velocity to that of the target as it approaches the moving target and follow it in a smooth trajectory while preserving a safety distance from the target. Eventually, the performance of the guidance law and its effectiveness is confirmed with an extensive simulation study.

Path loss exponent estimation for wireless sensor network localization

The wireless received signal strength (RSS) based localization techniques have attracted significant research interest for their simplicity. The RSS based localization techniques can be divided into two categories: the distance estimation based and the RSS profiling based techniques. The path loss exponent (PLE) is a key parameter in the distance estimation based localization algorithms, where distance is estimated from the RSS. The PLE measures the rate at which the RSS decreases with distance, and its value depends on the specific propagation environment. Existing techniques on PLE estimation rely on both RSS measurements and distance measurements in the same environment to calibrate the PLE. However, distance measurements can be difficult and expensive to obtain in some environments. In this paper we propose several techniques for online calibration of the PLE in wireless sensor networks without relying on distance measurements. We demonstrate that it is possible to estimate the PLE using only power measurements and the geometric constraints associated with planarity in a wireless sensor network. This may have a significant impact on distance-based wireless sensor network localization.