Kalman filtering with real-time applications
Kalman filtering with real-time applications
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
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Based on Universal Mobile Telecommunications System (UMTS) Wideband Code Division Multiple Access (WCDMA) technology, the Mobile User Objective System (MUOS) provides a wide range of wireless telecommunications services to the war fighter over geosynchronous satellites. As MUOS is a multiple beam system, it is of paramount importance for the User Entry (UE) to select the best beam, which in turn requires accurate transmit power from the satellite. To minimize the satellite path gain uncertainty from the base station to the UE, the Enhanced Gain Variation Reduction (EGVR) algorithm is proposed. Implemented on the basis of the measurements of the received signal code power (RSCP) of the pilot channel from the UE and the measurements of the received signal code power at the terrestrial base station, the EGVR process generates the transmission power estimate and integrates it into the overall gain adjust system to keep the transmission power levels as desired. The features of the algorithm include the weighted gain adjusts generated from the estimation error, appropriate timers to control the measurement flow, and differential measurements to eliminate the common errors between satellite beams. The preliminary study shows that the EGVR can reduce the power uncertainty from 2.4 dB to 0.4 dB, in addition to reducing any differential errors encountered. With the blending of the telemetry feedback into the algorithm, it can make the transmit power uncertainty even smaller.