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In this paper we introduce a new multilateration-based method for source localization. Sensors with known positions collect noisy signals whose strength depends on the relative position between the sensors and the source. A nonlinear system of equations is obtained which is then recast into a linearized least squares (LS) problem, which resembles a multilateration scenario. From the LS problem a low-complexity...
Future distributed applications will combine mobile and stationary sensor actuator entities as part of smart environments. Hence, a various number of intelligent sensor systems and sensor types will be available for mobile robots. We designed a programming abstraction - the Smart Abstract Entity - to deal with the diversity of data formats, measurement units, individual uncertainties, etc., in such...
Most unmanned aerial robots use a Global Navigation Satellite System (GNSS), such as GPS, GLONASS, and Galileo, for their navigation. However, from time to time the GNSS fails to function due to geographical restrictions and deliberated jamming. This paper proposes an Unscented Kalman Filter-based GPS/IMU integration method in order to accurately estimate the position and velocity of an unmanned miniature...
The α-β filter based on the Kalman-like estimation scheme has been recognized as a outstanding tool for estimating the position and velocity signals of moving objects. Nevertheless, the performance of estimation heavily depends on the parameters α and β. In general, the choice of parameters is a trade-off optimization problem between the tracking accuracy and noise reduction capability. In order to...
This paper tackles the problem of the position measurement and estimation techniques in the robot navigation field based on Kalman filters. It presents the problem of the position estimation based on odometric, infrared and ultrasonic measurements. Further on deals with the theoretical and practical aspects of the state estimation based on Kalman filtering techniques. From the wide range of derivatives...
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