Mobile location estimation has attracted much attention in recent years. Location algorithms for mobile stations (MSs) can generally be categorized into network-and satellite-based systems. Both types of systems have their advantages and limitations under different environments (i.e., urban or rural area). In this paper, hybrid location estimation schemes, which combine both the satellite-and the network-based signals, are proposed to provide adaptation to various scenarios for location estimation. By exploiting the fusion algorithm, the proposed fusion-based hybrid (FH) architecture integrates the estimation results that are acquired from both the satellite-and the network-based systems. Two different types of signal selection schemes are adopted within the FH architecture: 1) the fixed set of signal inputs approach and 2) the selective set of signal inputs approach. On the other hand, the unified hybrid architecture employs the proposed hybrid signal-selection scheme and the hybrid least square estimator, which can conduct location estimation within a selected set of signal sources from the heterogeneous networks. The Kalman filtering technique is exploited in the proposed algorithms to both eliminate the measurement noises and to track the trajectories of the MSs. Numerical results demonstrate that the proposed hybrid location schemes can provide accurate location estimation by adapting themselves to different environments.
- Angle of arrival (AOA)
- Global Positioning System (GPS)
- Kalman filter
- Mobile location estimation
- Time difference of arrival (TDOA)
- Time of arrival (TOA)