The Study Of Mobile Location Algorithm With NLOS Error Mitigation

Recently, the subject of mobile positioning in wireless communication systems has drawn considerable attention. With accurate location estimation, a variety of new applications and services such as Enhanced-911, location sensitive billing, improved fraud detection, intelligent transport system (ITS) and improved traffic management will become feasible. Mobile positioning using radio location techniques usually involves Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), Signal Strength (SS) measurements or some combination of these methods. By contrast with others, TDOA has been selected as the processing technique of the paper. Fang and Taylor series method, the solution methods of the TDOA hyperbolic model, have been researched and simulated. And a method to control the non-convergence of Taylor series has been proposed. The simulated results have shown that the method can effectively control the non-convergence situations and decrease the running time and iterative times.Multi-path, non-line-of-sight (NLOS) propagation and multiple access interference are often the main sources of errors in location, and make mobile positioning challenging. Among these error sources, NLOS is probably the most crucial one. NLOS errors, derived from the blocking of direct paths, have been considered as a killer issue in the location estimation. Several location techniques have been proposed in the literature which can reduce the effect of NLOS error to some extent. There are, broadly, three ways to cope with the NLOS condition. The first way is to eliminate or mitigate the NLOS errors from the range measurement, and then using the processed range measurements to calculate the location estimation. The second way localizes with all NLOS and LOS measurements, but provides weighting or scaling to minimize the effects of the NLOS contributions. The weighting comes from either the localization geometry and BS layout, or from the residuals (fitting errors) of individual BS. The advantage here is that there is...