Hybrid technique for indoor positioning system based on Wi-Fi received signal strength indication

Torteeka, Peerapong; Xiu, Chundi; Yang, Dongkai

doi:10.1109/ipin.2014.7275467

Cited by 17 publications

(5 citation statements)

References 47 publications

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“…We modified this method to calculate the distance from the reference point to the object. To get the distance between RP and the object we can subtract the distance between the object and beacon with the distance between RP and beacon shown in equation (14)(15)(16) where p(d)dBm are the RSSI signal strength or c = |b-a| in Figure 5. Later we will calculate the average of this calculated distance for n beacon that has stronger signal in each test point coordinates.…”

Section: E Hybrid Methodsmentioning

confidence: 99%

Performance Evaluation of Multilateration and Fingerprinting Method in Indoor Positioning System

Setiabudi¹,

Kusuma²

2021

IJETAE

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Indoor Positioning System has been one of the most attractive research after Bluetooth Low Energy (BLE) was introduced. This technology mainly used because of the reduction of material and energy cost over time that has huge impact compared to other technologies, which are more costly. Most recent research resolve around improving the accuracy of calculated position of the user by implementing different method to enable an indoor positioning system, and to remove any noises in the dataset. This paper objective is to compare some of the available methods that are used to enable Indoor Positioning System such as Fingerprinting, Multilateration, Trilateration, and Heron Bilateration. Since the performance of Fingerprinting is better compared to other methods, we combine Fingerprinting’s offline phase with the other methods to create a hybrid method and compare the accuracy of predicted user’s position. The experimental results show that the Fingerprinting and WKNN method outperform all other methods by resulting on 271.76 cm mean of error.

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Section: E Hybrid Methodsmentioning

confidence: 99%

Performance Evaluation of Multilateration and Fingerprinting Method in Indoor Positioning System

Setiabudi¹,

Kusuma²

2021

IJETAE

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“…Trilateration is then used to position the user based on the computed distances to the various APs. These methods generally require detailed floor plans of the building and the locations of the APs in the building (Torteeka et al, 2014).…”

Section: Location Estimation Methodsmentioning

confidence: 99%

Indoor Positioning Using Wlan Fingerprint Matching and Path Assessment With Retroactive Adjustment on Mobile Devices

Gulo¹,

Sohn²,

Afnan³

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> With the increasing number and usage of mobile devices in people’s daily life, indoor positioning has attracted a lot attention from both academia and industry for the purpose of providing location-aware services. This work proposes an indoor positioning system, primarily based on WLAN fingerprint matching, that includes various minor improvements to improve the positioning accuracy of the algorithm, as well as improve the quality and reduce the collection time of the reference fingerprints. In addition, a novel Path Evaluation and Retroactive Adjustment module is employed; it intends to improve the positioning accuracy of the system in a similar fashion to a Pedestrian Dead Reckoning implemented along with WLAN Fingerprint Matching in a Sensor Fusion system. The benefit of this approach being that it avoids the requirement of inertial sensor data, as well as its intensive computation and power use, while providing a similar accuracy improvement to Pedestrian Dead Reckoning. Our experimental results demonstrate that this may be a viable approach for positioning using mobile devices in an indoor environment.</p>

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“…These classifiers can be implemented in many ways such as but not limited to: artificial neural networks (ANN) [32,33], decision trees (DT) [34], fuzzy logic [35], support vector machines (SVM) [36], SVM and DT fusion [37], multilayer perceptron (MLP) [38], and bidirectional long short-term memory recurrent neural networks (BLSTM-RNN) [39]. Alternative sensors from smartphones have been also utilized for step detection, such as camera sensors which is referred to as visual odometry [40][41][42][43], but they can only work under the constraint of using the device in compassing mode for the camera to capture the foot motion of the user.…”

Section: Step Detectionmentioning

confidence: 99%

S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Khedr

El‐Sheimy

2021

Geomatics

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Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

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Hybrid technique for indoor positioning system based on Wi-Fi received signal strength indication

Cited by 17 publications

References 47 publications

Performance Evaluation of Multilateration and Fingerprinting Method in Indoor Positioning System

Performance Evaluation of Multilateration and Fingerprinting Method in Indoor Positioning System

Indoor Positioning Using Wlan Fingerprint Matching and Path Assessment With Retroactive Adjustment on Mobile Devices

S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

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