Error analysis in indoors localization using ZigBee wireless networks

Azenha, Abílio; Peneda, Luis; Carvalho, Adriano

doi:10.1109/iecon.2010.5675061

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Alternatively, an RSSI of -65 dBm corresponds to distances ranging between 5.5 and 77 m which actually covers almost the entire building. This large RSSI variability is also found in other experimental studies in industrial indoor environments [5]. It is obvious that, in such realistic environment, physical relationships cannot be applied as such.…”

Section: Introductionmentioning

confidence: 70%

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Vanheel

Verhaevert

Laermans

et al. 2011

J Wireless Com Network

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Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.

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Section: Introductionmentioning

confidence: 70%

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Vanheel

Verhaevert

Laermans

et al. 2011

J Wireless Com Network

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“…The AOA algorithm uses a set of antenna arrays to determine the angle of the arrival signals and thus estimates the target position and orientation. The RSSI algorithm, which is discussed in this study, transforms the received signal strength into an equivalent distance between a signal sender and receiver pair and then calculates the target position by trilateration using at least two pairs of transmission devices (Azenha et al, 2010; Kim and Lee, 2006; Lee and Kim, 2014; Lin and Song, 2014; Montaser and Moselhi, 2014; Peng et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Localization algorithms based on RSSI signals can be conveniently implemented on a mobile robot system without the need of additional hardware. However, RSSI-based localization algorithms usually have limited localization accuracy in real applications because the interference and reflection of transmission signals and the orientation effects of the applied antenna can significantly affect the RSSI signals (Azenha et al, 2010; Graefenstein et al, 2009; Min et al, 2012). Therefore, some studies have focused on reducing the adverse effects induced by the antenna orientation (Graefenstein et al, 2009) and others, on improving the localization accuracy by employing probabilistic sensor fusion methods such as the Kalman filter (Ben Kilani et al, 2014; Derenick et al, 2011; Menegatti et al, 2009; Oliveira et al, 2014; Pathirana et al, 2005; Zhang et al, 2014), Bayesian filter (Ahn and Yu, 2007; Zhuang et al, 2008), least-squares estimation (Challa et al, 2005; Szalay and Nagy, 2013) and others (Feng et al, 2014; Lin and Song, 2014; Li and Wu, 2014).…”

Section: Introductionmentioning

confidence: 99%

Particle filter design for mobile robot localization based on received signal strength indicator

Hsu

Yeh

Hsu

2016

Transactions of the Institute of Measurement and Control

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This paper presents a particle filter design to improve the accuracy of received signal strength indicator (RSSI)-based localization algorithms for localizing mobile robots that move in an environment with an 802.15.4 ZigBee wireless sensor network. In this study, the RSSI of transmission signals is adopted to estimate the location of a mobile robot by applying the trilateration algorithm. However, the interference and reflection effects of transmission signals and the orientation effects of the antenna can significantly affect the RSSI signals and result in serious localization errors. This study analyses the characteristics of RSSI signals and the orientation effects of the antenna and then develops a particle filter design with the fusion of the current states of the mobile robot to improve localization accuracy. A static localization experiment shows that the developed localization algorithm can significantly improve the localization accuracy, and a maximum rate of improvement of up to 85% is achieved in the test environment. A dynamic localization experiment demonstrates that the developed algorithm is suitable for localizing a mobile robot.

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