Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

Download Full-text

mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

Sensors ◽

10.3390/s18072283 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2283 ◽

Cited By ~ 17

Author(s):

Imran Ashraf ◽

Soojung Hur ◽

Yongwan Park

Keyword(s):

Magnetic Flux ◽

Indoor Localization ◽

Search Space ◽

Initial Position ◽

Received Signal Strength Indicator ◽

Strength Based ◽

Flux Intensity ◽

Smartphone Sensors ◽

Path Lengths ◽

Fingerprint Database

An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The system exploits the fingerprint database approach for localization. Traditional fingerprinting technology stores data intensity values in database such as RSSI (Received Signal Strength Indicator) values in the case of WiFi fingerprinting and magnetic flux intensity values in the case of geomagnetic fingerprinting. The down side is the need to update the database periodically and device heterogeneity. We solve this problem by using the fingerprint database of patterns formed by magnetic flux intensity values. The pattern matching approach solves the problem of device heterogeneity and the algorithm’s performance with Samsung Galaxy S8 and LG G6 is comparable. A deep learning based artificial neural network is adopted to identify the user state of walking and stationary and its accuracy is 95%. The localization is totally infrastructure independent and does not require any other technology to constraint the search space. The experiments are performed to determine the accuracy in three buildings of Yeungnam University, Republic of Korea with different path lengths and path geometry. The results demonstrate that the error is 2–3 m for 50 percentile with various buildings. Even though many locations in the same building exhibit very similar magnetic attitude, the algorithm achieves an accuracy of 4 m for 75 percentile irrespective of the device used for localization.

Download Full-text

An Efficient Fingerprint Database Construction Approach Based on Matrix Completion for Indoor Localization

IEEE Access ◽

10.1109/access.2020.3009441 ◽

2020 ◽

Vol 8 ◽

pp. 130708-130718

Author(s):

Teng Tan ◽

Lingwen Zhang ◽

Qiumei Li

Keyword(s):

Indoor Localization ◽

Matrix Completion ◽

Database Construction ◽

Fingerprint Database

Download Full-text

Scanning method for indoor localization using the RSSI approach

Journal of Sensors and Sensor Systems ◽

10.5194/jsss-6-247-2017 ◽

2017 ◽

Vol 6 (1) ◽

pp. 247-251 ◽

Cited By ~ 2

Author(s):

Ahmad Warda ◽

Bojana Petković ◽

Hannes Toepfer

Keyword(s):

Indoor Localization ◽

Signal Strength ◽

Reconstruction Error ◽

Received Signal Strength Indicator ◽

Contrast Resolution ◽

Scanning Method ◽

Database Construction ◽

Indoor Mobile Robot ◽

Mobile Robot Localization ◽

Localization And Tracking

Abstract. This paper presents a scanning method for indoor mobile robot localization using the received signal strength indicator (RSSI) approach. The method eliminates the main drawback of the conventional fingerprint, whose database construction is time-consuming and which needs to be rebuilt every time a change in indoor environment occurs. It directly compares the column vectors of a kernel matrix and signal strength vector using the Euclidean distance as a metric. The highest resolution available in localization using a fingerprint is restricted by a resolution of a set of measurements performed prior to localization. In contrast, resolution using the scanning method can be easily changed using a denser grid of potential sources. Although slightly slower than the trilateration method, the scanning method outperforms it in terms of accuracy, and yields a reconstruction error of only 0. 08 m averaged over 1600 considered source points in a room with dimensions 9.7 m × 4.7 m × 3 m. Its localization time of 0. 39 s makes this method suitable for real-time localization and tracking.

Download Full-text

Indoor Floor Localization Based on Multi-Intelligent Sensors

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010006 ◽

2020 ◽

Vol 10 (1) ◽

pp. 6

Author(s):

Min Zhao ◽

Danyang Qin ◽

Ruolin Guo ◽

Xinxin Wang

Keyword(s):

Machine Learning ◽

Indoor Localization ◽

Collaborative Work ◽

Magnetic Data ◽

Data Mapping ◽

Localization Accuracy ◽

Database Construction ◽

Intelligent Sensors ◽

Majority Principle ◽

Fingerprint Database

With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large errors. A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor localization phase. In the fingerprint database construction phase, data acquisition is performed using magnetometer sensor, accelerator sensor and gyro sensor in the smartphone. In the floor localization phase, an active pattern recognition is performed through the collaborative work of multiple intelligent sensors and machine learning classifiers. Then floor localization is performed using magnetic data mapping, Euclidean closest approximation and majority principle. Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL. The experimental results show that the performance of the proposed method is superior to the existing IFL.

Download Full-text

Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

Sensors ◽

10.3390/s18092990 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2990 ◽

Cited By ~ 2

Author(s):

Junhong Lin ◽

Bang Wang ◽

Guang Yang ◽

Mu Zhou

Keyword(s):

Indoor Localization ◽

Field Measurements ◽

Localization Accuracy ◽

Database Construction ◽

Cross Domain ◽

Site Survey ◽

Intersection Algorithm ◽

Promising Solution ◽

Fingerprint Database ◽

Sample Annotation

Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.

Download Full-text