An UWB/Vision Fusion Scheme for Determining Pedestrians’ Indoor Location

Liu, Fei; Zhang, Jixian; Wang, Jian; Han, Hyangsun; Yang, Dongkai

doi:10.3390/s20041139

Cited by 26 publications

(20 citation statements)

References 27 publications

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“…There are several formatting methods. The Huber's method is chosen in this paper [30]. Then, the diagonal r k ii and non-diagonal r k ii elements of R k are calculated as [7]:…”

Section: Height Estimationmentioning

confidence: 99%

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Geng

Xia

et al. 2021

Sensors

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Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

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“…There are several formatting methods. The Huber's method is chosen in this paper [30]. Then, the diagonal r k ii and non-diagonal r k ii elements of R k are calculated as [7]:…”

Section: Height Estimationmentioning

confidence: 99%

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Geng

Xia

et al. 2021

Sensors

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“…Thus, extra sensors are necessary to strengthen indoor positioning. In the past decades, WiFi [2][3][4], Bluetooth [5][6][7], ultra-wideband (UWB) [8][9][10], and microelectro-mechanical system (MEMS) [11][12][13] have been studied for indoor positioning. Among these techniques, MEMS sensors are more competitive as their independence of the existing infrastructures in indoor environments [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Geng

Xia

2021

Micromachines

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The demands for indoor positioning in location-based services (LBS) and applications grow rapidly. It is beneficial for indoor positioning to combine attitude and heading information. Accurate attitude and heading estimation based on magnetic, angular rate, and gravity (MARG) sensors of micro-electro-mechanical systems (MEMS) has received increasing attention due to its high availability and independence. This paper proposes a quaternion-based adaptive cubature Kalman filter (ACKF) algorithm to estimate the attitude and heading based on smart phone-embedded MARG sensors. In this algorithm, the fading memory weighted method and the limited memory weighted method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. The latest step data is used as the memory window data of the limited memory weighted method. Moreover, for restraining the divergence, the filter innovation sequence is used to rectify the noise covariance measurements and system. Besides, an adaptive factor based on prediction residual construction is used to overcome the filter model error and the influence of abnormal disturbance. In the static test, compared with the Sage-Husa cubature Kalman filter (SHCKF), cubature Kalman filter (CKF), and extended Kalman filter (EKF), the mean absolute errors (MAE) of the heading pitch and roll calculated by the proposed algorithm decreased by 4–18%, 14–29%, and 61–77% respectively. In the dynamic test, compared with the above three filters, the MAE of the heading reduced by 1–8%, 2–18%, and 2–21%, and the mean of location errors decreased by 9–22%, 19–31%, and 32–54% respectively by using the proposed algorithm for three participants. Generally, the proposed algorithm can effectively improve the accuracy of heading. Moreover, it can also improve the accuracy of attitude under quasistatic conditions.

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“…On the other hand, multi-antenna approaches can achieve centimetric accuracy [9]. Ultra-Wide Band (UWB) technologies have emerged as a suitable solution for indoor positioning, even included in current smartphones, providing accuracies below decimeters [10,11]. Additionally, 5G networking makes use of its rich bandwidth and the processing and communications capabilities.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Villadangos

Ureña

Domínguez

et al. 2021

Sensors

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Ultrasonic local positioning systems (ULPS) have been brought to the attention of researchers as one of the possibilities that can be used for indoor localization. Acoustic systems combine a suitable trade-off between precision, ease of development, and cost. This work proposes a method for measuring the time of arrival of encoded emissions from a set of ultrasonic beacons, which are used to implement an accurate ULPS. This method uses the generalized cross-correlation technique with PHAT filter and weighting factor β (GCC-PHAT-β). To improve the performance of the GCC-PHAT-β in encoded emission detection, the employment is proposed of mixed-medium multiple-access techniques, based on code division and time division multiplexing of beacon emissions (CDMA and TDMA respectively), and to dynamically adjust the PHAT filter weighting factor. The receiver position is obtained by hyperbolic multilateration from the time differences of arrival (TDoA) between a reference beacon and the rest, thus avoiding the need for receiver synchronization. The results show how the dynamic adaptation of the weighting factor significantly reduces positioning errors from 20 cm to 2 cm in 80% of measurements. The simulated and real experiments prove that the proposed algorithms improve the performance of the ULPS in situations with lower signal-to-noise ratios (SNR) than 0 dB and in environments where the multipath effect makes it difficult to correctly detect the encoded ultrasonic emissions.

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An UWB/Vision Fusion Scheme for Determining Pedestrians’ Indoor Location

Cited by 26 publications

References 27 publications

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

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