On the fusion of inertial data for signal strength localization

Schmid, Johannes; Gädeke, Tobias; Stork, Wilhelm; Müller-Glaser, Klaus D.

doi:10.1109/wpnc.2011.5961006

Cited by 21 publications

(6 citation statements)

References 12 publications

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“…Recent advances in inertial sensors allow for fusion of inertial data with RSSI-based localization data to provide more reliable estimates as reported in [79] and in [81]. The reported accuracy in [81] is 1.6 m over an area of 40 m × 80 m.…”

Section: ) Rf Methods Distance Via Rssi: Received Signalmentioning

confidence: 99%

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Iliev

Paprotny

2015

IEEE Sensors J.

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Spatial localization (co-location) of nodes in wireless sensor networks (WSNs) is an active area of research, with many applications in sensing from distributed systems such as micro-aerial vehicles, smart dust sensors, and mobile robotics. This paper provides a comprehensive review and comparison of recent implementations (commercial and academic) of physical measurement techniques used in sensor localization, and of the localization algorithms that apply these measurement techniques. Physical methods for measuring distances and angles between WSN nodes are reviewed, followed by a comprehensive comparison of localization accuracy, applicable ranges, node dimensions, and power consumption of the different implementations. A summary of advantages and disadvantages of each measurement technique is provided along with a comparison of co-localization methods in WSNs across multiple algorithms and distance ranges. A discussion of possible improvements to accuracy, range, and power consumption of selected self-localization methods is included in the concluding discussion. Although the preferred implementation depends on the application, required accuracy, and range, passive optical triangulation is reported as the most energy efficient localization method for low-cost/low-power miniature sensor nodes. It is capable of providing micron-level resolution, however the applicable range (inter-node distance) is limited to single centimeters.1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2015.2450742, IEEE Sensors Journal SUBMITTED TO THE IEEE SENSORS JOURNAL 2 on 2.4 GHz radios are: Nordic Semiconductor nRF24L01 transceiver [16] with consumed power from 345 µW to 2.4 mW depending on the refresh rate of the location task, ZigBee Texas Instruments SoC CC2430 radio transceiver [12] with 10 mW to 40 mW consumption depending on implementation, RF Telosb transceivers [17] with consumption from 35 mW to 41 mW depending on the activity profile, and finally on custom radios for IEEE 802.15.4 build from digital ASIC standard cells technology [18] with power consumption of 15.6 mW at 0 dB output power. The communication system is used by a sensor node to perform its message-based self-localization task as well as for broadcasting the basic data collected by the sensor. Communication systems based on ultrasound waves have not been attempted due to the directional dependence of acoustic signals and their dependence on air temperature and humidity. Optical communication between sensors in WSN has been implemented recently using low-power laser diode emitters and detectors [19], modulating Corner-Cube Reflectors (CCR) [20], MEMS CCR arrays [21], MEMS steering mirors (gold coated, refl...

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Section: ) Rf Methods Distance Via Rssi: Received Signalmentioning

confidence: 99%

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Iliev

Paprotny

2015

IEEE Sensors J.

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“…An example of this kind of hybrid systems is found in [ 47 ] where the authors developed a system that combines the position estimation of a WiFi probabilistic fingerprinting with the information of a foot mounted SHS using an Extended Kalman Filter (EKF) for the fusion of the systems. Similarly, in [ 48 , 49 , 64 ] the step information of a hip mounted IMU is combined with the position estimations of a range based RSS system. Jimenez et al [ 50 ] combine a strapdown foot mounted inertial system with the RSS of RFID tags using an EKF.…”

Section: Hybrid Positioning Systemsmentioning

confidence: 99%

A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

Correa

Barceló

Morell

et al. 2017

Sensors

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In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications.

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“…The sensor data fusion is obtained by an EKF. Schmid et al [21] presented an experimental study on the pedestrian localization problem, which analyzes the improvements that can be obtained by fusing inertial data and RSSI data. This work compared the accuracy of a RSSI-only localization approach with respect to the RSSI and IMU data fusion approach.…”

Section: Related Workmentioning

confidence: 99%

Data fusion for relative localization of wireless mobile nodes

Franco

Franchino

Marinoni

2014

Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014)

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Monitoring teams of mobile nodes is becoming crucial in a growing number of activities. When it is not possible to use fix references or external measurements, a practicable solution is to derive relative positions from local communication.In this work, we propose an anchor-free Received Signal Strength Indicator (RSSI) method aimed at small multi-robot teams. Information from Inertial Measurement Unit (IMU) mounted on the nodes and processed with a Kalman Filter are used to estimate the robot dynamics, thus increasing the quality of RSSI measurements. A Multidimensional Scaling algorithm is then used to compute the network topology from improved RSSI data provided by all nodes. A set of experiments performed on data acquired from a real scenario show the improvements over RSSI-only localization methods. With respect to previous work only an extra IMU is required, and no constraints are imposed on its placement, like with camera-based approaches. Moreover, no a-priori knowledge of the environment is required and no fixed anchor nodes are needed.

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On the fusion of inertial data for signal strength localization

Cited by 21 publications

References 12 publications

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

Data fusion for relative localization of wireless mobile nodes

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