984 research outputs found
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization
Indoor localization in multi-floor buildings is an important research
problem. Finding the correct floor, in a fast and efficient manner, in a
shopping mall or an unknown university building can save the users' search time
and can enable a myriad of Location Based Services in the future. One of the
most widely spread techniques for floor estimation in multi-floor buildings is
the fingerprinting-based localization using Received Signal Strength (RSS)
measurements coming from indoor networks, such as WLAN and BLE. The clear
advantage of RSS-based floor estimation is its ease of implementation on a
multitude of mobile devices at the Application Programming Interface (API)
level, because RSS values are directly accessible through API interface.
However, the downside of a fingerprinting approach, especially for large-scale
floor estimation and positioning solutions, is their need to store and transmit
a huge amount of fingerprinting data. The problem becomes more severe when the
localization is intended to be done on mobile devices which have limited
memory, power, and computational resources. An alternative floor estimation
method, which has lower complexity and is faster than the fingerprinting is the
Weighted Centroid Localization (WCL) method. The trade-off is however paid in
terms of a lower accuracy than the one obtained with traditional fingerprinting
with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based
method for floor estimation via fingerprint clustering of WiFi and various
other positioning sensor outputs is introduced. Our method achieves a floor
estimation accuracy close to the one with NN fingerprinting, while
significantly improves the complexity and the speed of the floor detection
algorithm. The decrease in the database size is achieved through storing and
transmitting only the cluster heads (CH's) and their corresponding floor
labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and
Tracking: Indoors, Outdoors and Emerging Network
Fingerprint positioning of users devices in long term evolution cellular network using K nearest neighbour algorithm
The rapid exponential growth in wireless technologies and the need for public safety has led to increasing demand for location-based services. Terrestrial cellular networks can offer acceptable position estimation for users that can meet the statutory requirements set by the Federal Communications Commission in case of network-based positioning, for safety regulations. In this study, the proposed radio frequency pattern matching (RFPM) method is implemented and tested to determine a user’s location effectively. The RFPM method has been tested and validated in two different environment. The evaluations show remarkable results especially in the Micro cell scenario, at 67% of positioning error 15m and at 90% 31.78m for Micro cell scenario, with results of 75.66m at 67% and 141.4m at 90% for Macro cell scenario
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: PD
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