WPI precision personnel locator system: Inertial navigation supplementation

Amendolare, V.; Cyganski, David; Duckworth, R. James; Makarov, Sergey B.; Coyne, James; Daempfling, H.; Woodacre, B.

doi:10.1109/plans.2008.4570055

Cited by 16 publications

(10 citation statements)

References 3 publications

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“…24 PPL can use the RF signals alone to estimate location or to correct drift in dead-reckoned positions. The dead reckoning is particularly useful in larger buildings, where RF position estimates are less accurate.…”

Section: Precision Personnel Locationmentioning

confidence: 99%

Location and Navigation Support for Emergency Responders: A Survey

Fischer

Gellersen

2010

IEEE Pervasive Comput.

181

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Section: Precision Personnel Locationmentioning

confidence: 99%

Location and Navigation Support for Emergency Responders: A Survey

Fischer

Gellersen

2010

IEEE Pervasive Comput.

181

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“…Unfortunately, MEMS acelerometers and gyroscopes are subject to significant random noise and bias [16] that have to be estimated on-line in to partially attenuate their drift effects. A more effective way to eliminate the accumulated error, is to fuse PDR with some indoor absolute positioning references, such as a received signal strength (RSS)-based local positioning system or RSS-LPS [17] or time-of-arrival (TOA) positioning systems (TOA-LPS) [18]. This PDR+LPS integrated concept, as already implemented in many outdoor applications fusing INS and GPS [19], has the potential to provide an accurate driftfree positioning solution.…”

Section: Introductionmentioning

confidence: 99%

Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements

Jiménez

Seco

Honorato

et al. 2012

IEEE Trans. Instrum. Meas.

391

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Abstract-We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial-and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted readerto-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1 % of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.

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“…Such a solution can be used in almost any location: airports, mines or shopping malls. It can also be very helpful to guide blind pedestrians, emergency rescue workers or firefighters (Chen et al, 2015;Deng et al, 2015;He et al, 2013;Placer and Kovacic, 2013). However, collected signals are very responsive to the alignment of sensor devices, the built-in instrumental errors and distortions from the surrounding environment (Morales et al, 2016;Zhang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The basic INA uses Kalman filtering to estimate the walker's trajectory using gyroscope and accelerometer outputs. For the reason that positioning accuracy decreases over time, supplementary sensors are usually used with IMU such as a time of arrival based on the local positioning system (LPS) -a radio frequency based system with inertial sensor supplementation (Amendolare et al, 2008). Such a solution is used in situations where accuracy can be severely degraded at particular locations within a building.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

Uradziński

Guo

2020

J. Sens. Sens. Syst.

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Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

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WPI precision personnel locator system: Inertial navigation supplementation

Cited by 16 publications

References 3 publications

Location and Navigation Support for Emergency Responders: A Survey

Location and Navigation Support for Emergency Responders: A Survey

Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

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