Wuk Kim

The interior-point method for an optimal treatment of bias in trilateration location

This paper presents a new position-determination estimator for trilateration location. The proposed estimator takes the measurement bias into consideration and improves the location accuracy of a mobile location system. In case that a mobile station (MS) utilizes signals from a set of base stations for its location, the computed location is largely affected by nonline-of-sight (NLOS) error in signal propagation. A constrained optimization method in a three-stage estimation structure is proposed to estimate and eliminate the measurement bias contained in each pseudorange and mainly caused by the NLOS error. A linear observation model of the bias is formulated, and the interior-point optimization technique optimally estimates the bias by introducing a feasible range of the measurement bias. It is demonstrated that the new three-stage estimator successfully computes an accurate location of an MS in a realistic environment setting. The location accuracy of the proposed estimator is analyzed and compared with the existing methods through mathematical formulations and simulations. The proposed estimator efficiently mitigates the effect of a measurement bias and shows that the iterated least square (ILS) accuracy of 118 m [67% distance root-mean-square (DRMS)] can be improved to about 17 m in a typical urban environment

Efficient use of digital road map in various positioning for ITS

There are many R&D improvements on positioning systems for ITS (intelligent transportation systems) adopting GPS, cellular phones or other communication systems. But, a position from any signal is always corrupted to a few meters through several hundreds of meters because of multipath, atmospheric delay, NLOS (non-line-of-sight), low DOP and so on. When the positioning systems are employed for ITS, a digital road map can be used together to display their navigation solutions in most ITS applications. Due to the fact that land-vehicles almost always run on roads, most of CNS (car navigation systems) translate the measured position onto a road. This methodology called map-matching, if it depends on a contaminated position due to white noise and biased error, has not only low accuracy but also the road ambiguity problems in some crossroads. Therefore, this paper presents an efficient use of an advanced map-matching in order to get a more improved accuracy, which estimates a large bias being the main source of errors and corrects a vehicles position. It is composed of a modeling of biased error and filtering by a Kalman filter. We have applied the proposed map-matching to not only GPS navigation but also CDMA location. The proposed approach represents that in addition to its original visual display, an accurate digital road-map can improve the positioning accuracy effectively by correcting the vehicles position.

Increase of Positioning Robustness By Integration of CDMA and GPS

Abstract This paper shows how to integrate CDMAs pilot signals and GPS pseudoranges in order to improve the location accuracy, coverage and robustness to various errors in the position-determination. Since two radio positioning systems are complementary in urban canyons and open areas, the hybrid integration of them gives better performance, especially of wider coverage. Due to signal propagation, ionospheric error, multipath and NLOS(none-line-of-sight) errors cause the measured pseudoranges to contain too severe bias error to be ignored. Hence, this paper presents a 3-stage constrained optimization filter that takes the measurement bias into account. Comparison with simple ILS(iterated least square), extended Kalman filter shows that the presented filter reduces both bias error and linearization error effectively.