Possibilities of Using Kalman Filters in Indoor Localization

Fronckova, Katerina; Pražák, Pavel

doi:10.3390/math8091564

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ RSS measurement is in two phases, namely the offline and online phases [1], [3], [5]. The offline phase involves the generation of an RSS fingerprint database using RSS measurements collected from spatially deployed wireless access points (APs).…”

Section: Introductionmentioning

confidence: 99%

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Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm With Context Similarity Coefficient

Yaro

Maly

2023

IEEE Access

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The performance of fingerprint-based indoor wireless localization systems (IWL-Ss) can be enhanced using fingerprint clustering. The localization performance of clustered fingerprint-based IWL-Ss is affected by several factors, including choosing the most optimal initial parameters and the appropriate fingerprint similarity measurement metric. The problem of choosing the best initial parameter is solved by using the affinity propagation clustering (APC) algorithm in this paper, which automatically calculates the number of clusters and cluster centroid vectors. However, the choice of fingerprint similarity measure and the selection of the best cluster centroid when there are multiple potential cluster centroids limit the performance of the APC algorithm. To address this issue, this paper proposes modifying the conventional APC (c-APC) algorithm, which will be referred to as the ''m-APC algorithm.'' The context similarity coefficient (CSC) fingerprint similarity measure replaces the distance-based fingerprint similarity measure used by the c-APC algorithm. Furthermore, the cluster centroids that are generated automatically are replaced by the centroid that is obtained by averaging all fingerprints within a cluster. Using the k-NN localization algorithm and four online fingerprint databases, the performance of the m-APC+CSC algorithm is determined and compared to the c-APC algorithm using cosine, Euclidean, and Shepard distances as fingerprint similarity measures. Based on simulation results, the m-APC algorithm reduced the position root mean square error (RMSE) and mean absolute error (MAE) by about 12% and 8%, respectively, when compared to the c-APC algorithm when both used the CSC as a fingerprint similarity measure. Furthermore, the m-APC+CSC algorithm achieved an 8% and 9%, respectively, position RMSE and MAE reduction over the c-APC algorithm using cosine, Euclidean, and Shepard distances as similarity measurements. The m-APC+CSC algorithm should, however, be used on a reasonably sized fingerprint database with at least four wireless access points (APs) for better localization performance.

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Section: Introductionmentioning

confidence: 99%

“…This is done by comparing the IU's instantly acquired fingerprint with the fingerprint stored in the database. Algorithms such as Kalman filters, k-nearest neighbours (k-NN), support vector machines (SVM), and the Gaussian mixture model (GMM) have been used to determine the location of an IU in the online phase [1], [3], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm With Context Similarity Coefficient

Yaro

Maly

2023

IEEE Access

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“…A fingerprint consists of RSS measurements obtained from multiple Wi-Fi or BLE APs at the same RL. There are several methods [1,5] used in the generation of the fingerprint database, and the site survey method [1] is the most used. The generation of the fingerprint database is not the focus of this paper; hence, it is assumed that it has been generated using any of the available methods present in the literature.…”

Section: -Introductionmentioning

confidence: 99%

A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization

Yaro,

Malý,

Malý

2023

Emerg Sci J

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Fingerprint database clustering is one of the methods used to reduce localization time and improve localization accuracy in a fingerprint-based localization system. However, optimal selection of initial hyperparameters, higher computation complexity, and interpretation difficulty are among the performance-limiting factors of these clustering algorithms. This paper aims to improve localization time and accuracy by proposing a clustering algorithm that is extremely efficient and accurate at clustering fingerprint databases without requiring the selection of optimal initial hyperparameters, is computationally light, and is easily interpreted. The two closest wireless access points (APs) to the reference location where the fingerprint is generated, as well as the labels of the two APs in vector form, are used by the proposed algorithm to cluster fingerprints. The simulation result shows that the proposed clustering algorithm has a localization time that is at least 45% faster and a localization accuracy that is at least 25% higher than the k-means, fuzzy c-means, and lightweight maximum received signal strength clustering algorithms. The findings of this paper further demonstrate the real-time applicability of the proposed clustering algorithm in the context of indoor wireless localization, as low localization time and higher localization accuracy are the main objectives of any localization system. Doi: 10.28991/ESJ-2023-07-05-019 Full Text: PDF

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“…Furthermore, the fingerprint database has temporal correlation, considering the similarity that exists between RSSI values obtained by an AP at different times in a fixed position. Therefore, deterministic methods such as k-nearest neighbor (KNN) [1] and probabilistic Bayesian methods such as Horus [2], Kalman and particle filters [3,4], and hidden Markov models (HMMs) [5,6] are generally used as location estimation algorithms to obtain an enhanced system accuracy.…”

Section: Introductionmentioning

confidence: 99%

WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model

Babalola¹,

Balyan²

2021

Sensors

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Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

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Possibilities of Using Kalman Filters in Indoor Localization

Cited by 7 publications

References 22 publications

Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm With Context Similarity Coefficient

Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm With Context Similarity Coefficient

A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization

WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model

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