A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Xu, Shenglei; Wang, Yunjia; Sun, Min; Si, Minghao; Cao, Hongji

doi:10.3390/app11178170

Cited by 9 publications

(7 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those influences can be reduced successfully by implementing various filters, most commonly used are variations of the Kalman Filter [27] and Particle Filters [25]. Fingerprinting usually requires a calibration pre-operation, which can take tremendous time and effort; however, if done properly, it can achieve a positioning error of less than 1 m, especially if fused with other sensor data [28]. Frequently, this process requires to be repeated after some time, due to the changes in environment.…”

Section: Ble Indoor Localization Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

2022

View full text Add to dashboard Cite

Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes; however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s.

show abstract

Section: Ble Indoor Localization Systemsmentioning

confidence: 99%

“…Xu et al [28] presented an IPS which combines the fingerprinting-based RSSI techniques with pedestrian dead reckoning (PDR). The fingerprinting method was improved by using robust filter, and the PDR was improved by using a Mahony complementary filter, which reduced the drift error.…”

Section: Ble Indoor Localization Systemsmentioning

confidence: 99%

Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

2022

View full text Add to dashboard Cite

show abstract

“…The speed estimation research mainly includes step frequency detection and step length estimation [3]. Many step detection algorithms have been proposed by researchers, including peak detection, threshold setting, zero velocity update, autocorrelation and finite-state machine (FSM) [31]. This paper uses the peak detection method to detect a step.…”

Section: Speed Estimationmentioning

confidence: 99%

“…This paper uses the peak detection method to detect a step. Studies have shown that the step length is related to the acceleration, height, and strides of different people, and the step length estimated by different methods differs little [7,31]. In this paper, a nonlinear step length estimation algorithm is adopted, which takes the maximum and minimum acceleration of pedestrians within one step as the characteristic quantity.…”

Section: Speed Estimationmentioning

confidence: 99%

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Geng

Xia

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…This leads to poor positioning accuracy. To realize indoor localization, extensive research has been carried out on various localization technology such as Wireless Fidelity (Wi-Fi) [2], Bluetooth [3], geomagnetism [4], Ultra-wideband (UWB) [5][6][7][8], pseudo-satellite [9]. Among these approaches, UWB has attracted extensive attention due to its large transmission bandwidth that leads to high localization accuracy resolution, which can meet the requirements of indoor highprecision positioning.…”

mentioning

confidence: 99%

UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response's (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibilitybased support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms.

show abstract

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Cited by 9 publications

References 46 publications

Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

Contact Info

Product

Resources

About