Indoor positioning based on foot-mounted IMU

Guo, Hang; Uradziński, Marcin; Yin, Hao; Yu, Min

doi:10.1515/bpasts-2015-0074

Cited by 31 publications

(15 citation statements)

References 8 publications

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“…As can be seen, compared to the frequency and nonlinear models, the error is reduced to about 2% by using the BP-ANN model. It should be pointed out however that [29] used wavelet transform and a moving average filter to preprocess the raw data, with the error of the stride length estimation at about 0.43%; and [28] proposed the use of a sensor-fusion algorithm with the error reduced to 0.2%. The errors were smaller, but the walking characteristic parameters were for a specific individual, so the generality and practicability of the algorithms were limited.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…where is the stride length, Acc Vmax (or Acc Vmin ) is the maximum (or minimum) vertical acceleration in a stride, and is the personalized parameter. This model seems simple because it has only one coefficient, but in order to find the maximum and minimum vertical acceleration in each stride, initial alignment must be completed [28]. This can be done using accelerometers and magnetometers, and we can then obtain the vertical acceleration by utilizing acceleration measurements as in [27,28].…”

Section: Two Common Models For Datamentioning

confidence: 99%

“…This model seems simple because it has only one coefficient, but in order to find the maximum and minimum vertical acceleration in each stride, initial alignment must be completed [28]. This can be done using accelerometers and magnetometers, and we can then obtain the vertical acceleration by utilizing acceleration measurements as in [27,28]. Figure 6 shows the variation in vertical acceleration of test subject number 1 at normal walking speed.…”

Section: Two Common Models For Datamentioning

confidence: 99%

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Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

Xing

Hou

et al. 2017

Journal of Sensors

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Pedestrian dead reckoning (PDR) can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN), using a consumer-grade inertial measurement unit (IMU); it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Two Common Models For Datamentioning

confidence: 99%

Section: Two Common Models For Datamentioning

confidence: 99%

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Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

Xing

Hou

et al. 2017

Journal of Sensors

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“…This constraint enables IMU-only personal navigation, when the IMU is mounted on a shoe. In this case, at each step, the IMU is stationary for a short interval, when the shoe is on the ground, and thus, ZUPT can be applied to significantly bind the IMU drift errors [1][2][3]. Other constraints, such as a height constraint or constraints that enforce the filter to follow a predefined motion model, commonly called nonholonomic constraints, are typically applied for land vehicles [4].…”

Section: Related Workmentioning

confidence: 99%

Using Adaptive Motion Constraints to Support UWB/IMU Based Navigation

Koppanyi

Navrátil

et al. 2018

J Inst Navig

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This study addresses the problem of short UWB (ultra-wide band) outages in a UWB/IMU (inertial measurement unit) integrated navigation system. UWB outages may occur due to non-line-of-sight between the UWB radios. To mitigate the errors during short UWB outages, we consider the special dynamics of the platform by adaptively applying constraints in the navigation filter. The application presented here is the navigation problem of the unexploded ordnances (UXOs) mapping platform. A UXO platform moves straight or performs turns or stops; these are the three dynamic states with pre-defined constraints. Two neural networks are examined to determine the current dynamic state based on IMU data. Tests on open-sky and canopied areas were carried out for the performance assessment. The networks were trained on the open-sky dataset, and then tested on the canopied area. The detected improvement of the proposed solution is 10-30% (10-15 cm) for 5-10 s long outages.

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“…Lately, some other positioning techniques have been developed such as wearable dead reckoning (DR) sensors, pseudolites, and MEMS to obtain a seamless indoor/outdoor positioning solution [1].…”

Section: Introductionmentioning

confidence: 99%

Advanced Indoor Positioning Using Zigbee Wireless Technology

Uradziński

Guo

Liu

et al. 2017

Wireless Pers Commun

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The paper presents the results of the project which examines the level of accuracy that can be achieved in precision indoor positioning using a new improved Zigbee network fingerprint method. This method can provide more accurate positioning by filtering algorithm for improving the fingerprint Zigbee database accuracy. In our experiment the following two steps have been completed. Firstly, we filtered out the interference data generated in the fingerprint database source data acquisition process what improved the accuracy of establishing the fingerprint database. Next, the nearest algorithm, the weighted nearest algorithm and Bayesian algorithm were used to calculate pedestrian's location, and then the results are compared and analyzed. As a result, the average error with the improved fingerprint database is less than or equal to 0.81 m in a long distance range. The accuracy of the results got much better compared with the results without filtering. A newly developed ZigBee system by us can be applied to the location based services in a bigger space inside the buildings (distances up to 40 m) or in the underground mines.

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Indoor positioning based on foot-mounted IMU

Cited by 31 publications

References 8 publications

Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

Using Adaptive Motion Constraints to Support UWB/IMU Based Navigation

Advanced Indoor Positioning Using Zigbee Wireless Technology

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