Sang-Moo Lee

Low complexity ranging algorithm based on TOA for IEEE 802.15.4a system

A computationally efficient ranging scheme that estimates time of arrival (TOA) by a low complexity minimum mean square error (MMSE) and a matrix pencil techniques is proposed for IEEE 802.15.4a chirp spread spectrum system. In TOA estimation, it is known that accurate channel delay can be estimated by the MMSE technique. However, the major drawback of the MMSE is its high computational complexity, which grows with number of signal samples. In this paper, thus, a computational efficient MMSE technique based on the chirp spectrum characteristic is proposed for a low complexity ranging algorithm. The proposed TOA estimation method consists of two-step signal processing: proposed low complexity MMSE-based channel impulse response (CIR) estimation and the channel delay tracking by matrix pencil algorithm. The proposed TOA estimation method not only has low computational complexity, but also achieves small ranging error. The effectiveness of the proposed algorithm is demonstrated by simulation results.

High Accurate Two-Dimensional Geo-Location System for Social Safety Robot

In this paper, we propose an efficient two-dimensional geo-location system for social safety robot, where its location is obtained by direction of arrival (DOA) and time of arrival (TOA) of the radio signal. The proposed system requires two reference signals while the conventional systems generally deploy more than three reference signals. For estimating TOA and DOA information together, we employ a multiple signal classification (MUSIC) algorithm. The simulation results show that the proposed geo-location system achieves accuracy within 3 meters error in 2 kilo-meters coverage which is constraint for mobile social safety robot.

A Data Driven cSLAM of Multiple Exploration Robots

This paper proposes a novel cSLAM method for multiple exploration robots with insufficient sensor ranges. For this purpose, we make the robots depend on a navigation map and sensor model driven from data obtained during the exploration instead of predefined sensor or navigation map model. And The proposed algorithm is implemented with OPRoS (Open SW Platform for Robotic Services) SW Components.

Efficient Mobile Robot Localization through Position Tracking Bias Mitigation for the High Accurate Geo-location System

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.