Earticle

Due to the uncertainty of the RFID measurements and limit of the placement of the readers, it’s necessary to use the estimation method to obtain more accurate trajectory in RFID indoor tracking system. While the traditional recursive estimation from K to K +1 sampling point may fail, because the measurement of RFID system is irregular sampling due to the data-driven measurement mechanism. This paper develops the tracking method for indoor RFID system, including estimation dynamic model based on the estimated states and nonlinear fusion estimation algorithm for variable-irregular sampling measurements. Two estimation methods were given based on the Extended Kalman filter (EKF) and Unscented Kalman filter (UKF), respectively. The tracking performances are compared and the simulation results show that the performance of UKF can get better performance for indoor RFID tracking, especially in the low detection rate area.

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

This paper addresses the issue fo target classification and localization with a SONAR for mobiler robot indoor navigation. In particular, multiple refetions of SONAR sound are used actively and interntionally. As for the SONAR sensor, the multiple reflection has been generally considered as one of the noisy phenomena, which is inevitable in the indoor environments. However, these multiple reflections can be a clue for classifying and localizing targets in the indoor environment if those can be controlled and used well. This paper develops a new SONAR sensor module with a reflection plane which can actively create the multiple refection. This paper also intends to suggest a new target classification emthod which uses the multiple refectiions. We approximate the world as being two dimensional and assume that the targets consisting of the indoor environment are pland, corner, and edge. Multiple reflection paths of an acoustic bean by a SONAR are analyzed, by simulations and the patterns of the TOPs (Time Of Flight) and angles of multiple reflections from each target are also analyzed. In addition, a new algorithm for target classification and localization is proposed.

서비스 로봇 분야에서 물체를 인식하고 장면을 이해하는 것은 매우 중요하다. 전통적인 방법들은 기하학적 모델을 기반으로 물체를 인식하였으나 불확실하고 동적인 환경에서 이러한 방법은 한계를 갖는다. 이에 최근 지식 기반의 접근 방법을 통해 이러한 부분을 보완하는 연구가 이루어지고 있다. 본 논문에서는 효과적인 물체 탐색을 위해 베이지안 네트워크를 사용하여 대상 물체의 존재 여부를 추론하는 방법을 제안한다. 이를 위해 트리구조의 계층적 베이지안 네트워크를 사용하였고 물체들의 관계를 활동을 기준으로 모델링 하였다. 6가지 장소를 기반으로 한 실험 결과, $86.5\%$의 정확도를 보여주었다.

서비스 로봇 분야에서 물체를 인식하고 장면을 이해하는 것은 매우 중요하다. 전통적인 방법들은 기하학적 모델을 기반으로 물체를 인식하였으나 불확실하고 동적인 환경에서 이러한 방법은 한계를 갖는다. 이에 최근 지식 기반의 접근 방법을 통해 이러한 부분을 보완하는 연구가 이루어지고 있다. 본 논문에서는 효과적인 물체 탐색을 위해 베이지안 네트워크를 사용하여 대상 물체의 존재 여부를 추론하는 방법을 제안한다. 이를 위해 트리구조의 계층적 베이지안 네트워크를 사용하였고 물체들의 관계를 활동을 기준으로 모델링 하였다. 6가지 장소를 기반으로 한 실험 결과, $86.5\%$의 정확도를 보여주었다.