An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

Bi, Jingxue; Wang, Yunjia; Li, Xin; Cao, Hongji; Xu, Shenglei

doi:10.3390/s18082502

Cited by 30 publications

(30 citation statements)

References 25 publications

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“…The importance of indoor positioning is reflected by the increasing requirements of location-based services (LBSs). Under such a background, global researchers have proposed a lot of indoor positioning methods based on different technologies, such as wireless fidelity (Wi-Fi) [1,2], Bluetooth [3], radio frequency identification (RFID) [4], computer vision [5], ultra-wideband (UWB) [6], infrared [7], and inertial navigation systems (INSs) [8], micro-electro-mechanical systems (MEMSs) [9], visible light [10,11], geomagnetic fields [12,13], and pseudolites [14,15], among others.…”

Section: Introductionmentioning

confidence: 99%

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Wang

et al. 2020

Applied Sciences

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In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

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Section: Introductionmentioning

confidence: 99%

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Wang

et al. 2020

Applied Sciences

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“…However, the indoor Wi-Fi signal propagation is very complicated, this algorithm relies too much on the signal attenuation model and an accurate path loss exponent is difficult to obtain [15], which makes it have poor adaptability to various signal environments in practice. Bi et al [16] proposes a cluster-filtered WKNN algorithm. It uses affinity propagation clustering algorithm to cluster the nearest RPs according to their position distances from each other, and the outliers are filtered out to reserve the subset with a larger number of RPs.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we compare the positioning accuracy of the proposed APD-WKNN algorithm with four nearest neighbor-based algorithms (Euclidean-WKNN, Manhattan-WKNN, Xue et al [14] and Bi et al [16]) on three Databases. The Euclidean-WKNN and Manhattan-WKNN are the WKNN algorithms that use the Euclidean distance and Manhattan distance as the distance measure, respectively.…”

Section: Positioning Performance Comparison With Nearest Neighbor-mentioning

confidence: 99%

A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning

et al. 2020

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In Wi-Fi fingerprint positioning, what we should most care about is the distance relationship between the user and the reference points (RP). However, most of the existing weighted k-nearest neighbor (WKNN) algorithms use the Euclidean distance of received signal strengths (RSS) as distance measure for fingerprint matching, and the RSS Euclidean distance is not consistent with the position distance. To address this issue, this paper analyzes the relationship between RSS similarity and position distance, propose a novel WKNN based on signal similarity and spatial position. Firstly, we obtain the weighted Euclidean distance (WED) by balancing the size between the RSS difference and the signal propagation distance difference according to the attenuation law of the spatial signal. Then, we obtain the approximate position distance (APD) by making full use of the position distances and WEDs between RPs. Finally, the nearest RPs can be selected more accurately based on the APDs between the user and different RPs, and the position of user can be estimated by the proposed WKNN based on the APD (APD-WKNN) algorithm. In order to fully evaluate the proposed algorithm, we use three fingerprint databases for comparison experiments with eight fingerprint positioning algorithms. The results show that the proposed algorithm can significantly improve the positioning accuracy of WKNN algorithm. INDEX TERMS Fingerprint positioning, weighted k-nearest neighbor, RSS similarity, position distance. I. INTRODUCTION With the rapid development of Location-based Services (LBS), many positioning technologies and signal processing methods [1]-[5] have been proposed. Due to the obstruction of the building, the usability of indoor navigation satellite signals is poor. This makes the Global Navigation Satellite System (GNSS) unable to guarantee satisfactory positioning performance in the indoor environment [6]. Therefore, various indoor positioning technologies have been proposed, among which the Wi-Fi fingerprint positioning is widely used because it can achieve positioning using only existing network facilities. The basic idea of Wi-Fi fingerprint positioning is to use the received signal strength (RSS) of Wi-Fi signal to The associate editor coordinating the review of this manuscript and approving it for publication was Liangtian Wan .

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“…This approach has already been successfully applied for various tasks in bioinformatics, e.g. for microarray and gene expression data [62][63][64][65] but not to our knowledge on whole-genome analysis. An extended panel of newly obtained full genomes sequences of F. tularensis subsp.…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Using affinity propagation clustering for identifying bacterial clades and subclades with whole-genome sequences of Francisella tularensis

et al. 2020

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By combining a reference-independent SNP analysis and average nucleotide identity (ANI) with affinity propagation clustering (APC), we developed a significantly improved methodology allowing resolving phylogenetic relationships, based on objective criteria. These bioinformatics tools can be used as a general ruler to determine phylogenetic relationships and clustering of bacteria, exemplary done with Francisella (F.) tularensis. Molecular epidemiology of F. tularensis is currently assessed mostly based on laboratory methods and molecular analysis. The high evolutionary stability and the clonal nature makes Francisella ideal for subtyping with single nucleotide polymorphisms (SNPs). Sequencing and real-time PCR can be used to validate the SNP analysis. We investigate whole-genome sequences of 155 F. tularensis subsp. holarctica isolates. Phylogenetic testing was based on SNPs and average nucleotide identity (ANI) as reference independent, alignment-free methods taking small-scale and large-scale differences within the genomes into account. Especially the whole genome SNP analysis with kSNP3.0 allowed deciphering quite subtle signals of systematic differences in molecular variation. Affinity propagation clustering (APC) resulted in three clusters showing the known clades B.4, B.6, and B.12. These data correlated with the results of real-time PCR assays targeting canSNPs loci. Additionally, we detected two subtle sub-clusters. SplitsTree was used with standard-setting using the aligned SNPs from Parsnps. Together APC, HierBAPS, and SplitsTree enabled us to generate hypotheses about epidemiologic relationships between bacterial clusters and describing the distribution of isolates. Our data indicate that the choice of the typing technique can increase our understanding of the pathogenesis and transmission of diseases with the eventual for prevention. This is opening perspectives to be applied to other bacterial species. The data provide evidence that Germany might be the collision zone where the clade B.12, also known as the East European clade, overlaps with the clade B.6, also known as the Iberian clade. Described methods allow generating a new, more detailed perspective for F. tularensis

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An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

Cited by 30 publications

References 25 publications

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning

Using affinity propagation clustering for identifying bacterial clades and subclades with whole-genome sequences of Francisella tularensis

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