scholarly journals Scanning method for indoor localization using the RSSI approach

2017 ◽  
Vol 6 (1) ◽  
pp. 247-251 ◽  
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.

RADIO PROPAGATION CHARACTERISTICS OF INDOOR LOCATION SYSTEM BASED ON RSSI AT 490 MHz

2014 ◽  
Vol 23 (07) ◽  
pp. 1450094 ◽  
Author(s):  
WEIHONG FAN ◽  
MAJID AHMADI ◽  
FENG XUE
Keyword(s):  
Indoor Environment ◽  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Propagation Characteristic ◽  
Radio Propagation ◽  
Test Results ◽  
Received Signal Strength Indicator ◽  
Indoor Location ◽  
Localization And Tracking

Localization and tracking technology based on received signal strength indicator (RSSI) is one of the most popular topics because of its low demand on hardware and cost. But the complexity of the indoor environment, leads to the uncertainty of the radio propagation which can seriously affect the positioning accuracy based on the received signal strength. Focused on the wall reflection in the indoor environment, the radio propagation characteristic based on ray-tracing model is analyzed and one strategy for the near wall localization is presented. The actual hardware platform and experimental test results show the applicability of the empirical logarithmic path loss model for localization and the effect of the wall reflection.

scholarly journals Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI

2021 ◽  
Vol 7 (1) ◽  
pp. 40-53
Author(s):  
Dwi Joko Suroso ◽  
Farid Yuli Martin Adiyatma ◽  
Ahmad Eko Kurniawan ◽  
Panarat Cherntanomwong
Keyword(s):  
Approximation Method ◽  
Indoor Localization ◽  
Classical Method ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Performance Comparison ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Simple Power ◽  
Distance Parameter

The classical rang-based technique for position estimation is still reliably used for indoor localization. Trilateration and multilateration, which include three or more references to locate the indoor object, are two common examples. These techniques use at least three intersection-locations of the references' distance and conclude that the intersection is the object's position. However, some challenges have appeared when using a simple power-to-distance parameter, i.e., received signal strength indicator (RSSI). RSSI is known for its fluctuated values when used as the localization parameter. The improvement of classical range-based has been proposed, namely min-max and iRingLA algorithms. These algorithms or methods use the approximation in a bounding-box and rings for min-max and iRingLA, respectively. This paper discusses the comparison performance of min-max and iRingLA with multilateration as the classical method. We found that min-max gives the best performance, and in some positions, iRingLA gives the best accuracy error. Hence, the approximation method can be promising for indoor localization, especially when using a simple and straightforward RSSI parameter.

scholarly journals Localisation of Inspection Probes in A Storage Tank

2021 ◽  
Vol 1 (2) ◽  
pp. 101-112
Author(s):  
Nurmi Elisya Rosli ◽  
Ali Sophian ◽  
Arselan Ashraf
Keyword(s):  
Storage Tank ◽  
Ieee 802.15.4 ◽  
Indoor Localization ◽  
Signal Strength ◽  
Positioning System ◽  
Wireless Technology ◽  
Received Signal Strength Indicator ◽  
Application System ◽  
Wireless Communication Protocols ◽  
Indoor Positioning System

Indoor Positioning System (IPS) has been widely used in today’s industry for the various purposes of locating people or objects such as inspection, navigation, and security. Many research works have been done to develop the system by using wireless technology such as Bluetooth and Wi-Fi. The techniques that can give some better performances in terms of accuracy have been investigated and developed. In this paper, ZigBee IEEE 802.15.4 wireless communication protocols are used to implement an indoor localization application system. The research is focusing more on analyzing the behaviour of Received Signal Strength Indicator (RSSI) reading under several conditions and locations by applying the Trilateration algorithm for localizing. The conditions are increasing the number of transmitters, experimented in the non-wireless connection room and wireless connection room by comparing the variation of RSSI values. Analysis of the result shows that the accuracy of the system was improved as the number of transmitters was increased.

The Location Algorithm Based on Square-Root Cubature Kalman Filter

2013 ◽  
Vol 325-326 ◽  
pp. 1525-1529
Author(s):  
Ying Liu ◽  
Jun Feng Su ◽  
Ming Qiang Zhu
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Signal Strength ◽  
Estimation Algorithm ◽  
Received Signal Strength ◽  
Square Root ◽  
Received Signal Strength Indicator ◽  
Cubature Kalman Filter ◽  
Wireless Signal ◽  
Parameter Estimation Algorithm

When wireless signal is used for indoor localization, there is no consistent relationship between signal strength received by the receiving nodes and distance from the receiving nodes to the receiving nodes, so there is a larger localization error for the Received Signal Strength Indication (RSSI) in the indoor environment. A new received signal strength indicator parameter estimation algorithm based on square-root cubature kalman filter is proposed in this paper, this algorithm utilizes Square-root Cubature Kalman filter (SCKF) to estimate the target’s position and the RSSI channel attenuation parameter simultaneously. The experimental results demonstrate that there is a better accuracy for the algorithm based on SCKF than the traditional method.

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

2022 ◽  
Vol 4 ◽  
pp. 167-189
Author(s):  
Dwi Joko Suroso ◽  
Farid Yuli Martin Adiyatma ◽  
Panarat Cherntanomwong ◽  
Pitikhate Sooraksa
Keyword(s):  
Indoor Localization ◽  
Polynomial Regression ◽  
Polynomial Interpolation ◽  
Received Signal Strength Indicator ◽  
Database Construction ◽  
3D Environments ◽  
Regression Techniques ◽  
Fingerprint Database ◽  
2D And 3D ◽  
Classical Interpolation

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

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