Abstract: The localization information is crucial for the
operation of WSN. There are principally two types of localization
algorithms. The Range-based localization algorithm has strict
requirements on hardware, thus is expensive to be implemented in
practice. The Range-free localization algorithm reduces the hardware
cost. However, it can only achieve high accuracy in ideal scenarios.
In this paper, we locate unknown nodes by incorporating the
advantages of these two types of methods. The proposed algorithm
makes the unknown nodes select the nearest anchor using the
Received Signal Strength Indicator (RSSI) and choose two other
anchors which are the most accurate to achieve the estimated
location. Our algorithm improves the localization accuracy compared
with previous algorithms, which has been demonstrated by the
simulating results.
Abstract: This report aims to utilize existing and future Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing Wireless Local Area Network (MIMO-OFDM WLAN) systems characteristics–such as multiple subcarriers, multiple antennas, and channel estimation characteristics–for indoor location estimation systems based on the Direction of Arrival (DOA) and Radio Signal Strength Indication (RSSI) methods. Hybrid of DOA-RSSI methods also evaluated. In the experimental data result, we show that location estimation accuracy performances can be increased by minimizing the multipath fading effect. This is done using multiple subcarrier frequencies over wideband frequencies to estimate one location. The proposed methods are analyzed in both a wide indoor environment and a typical room-sized office. In the experiments, WLAN terminal locations are estimated by measuring multiple subcarriers from arrays of three dipole antennas of access points (AP). This research demonstrates highly accurate, robust and hardware-free add-on software for indoor location estimations based on a MIMO-OFDM WLAN system.