Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Yuan, Zhengwu; Zha, Xupeng; Zhang, Xiaojian

doi:10.3390/s20185416

Cited by 10 publications

(8 citation statements)

References 41 publications

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“…A simple algorithm K Nearest Neighbor (KNN) is a popular supervised machine learning framework for both classification and regression tasks ( Wang et al, 2020 ; Yuan et al, 2020 ). KNN determines the class label of a query sample through the majority voting strategy of the KNNs of the query sample.…”

Section: Methodsmentioning

confidence: 99%

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

Liu¹,

Fu²,

Zhang³

et al. 2021

Front. Genet.

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Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.

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Section: Methodsmentioning

confidence: 99%

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

Liu¹,

Fu²,

Zhang³

et al. 2021

Front. Genet.

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“…This has brought great changes to our lives. The WIFI fingerprint-based indoor localization method [ 1 , 2 , 3 , 4 ] has become an important research topic due to its convenience and low cost. To accurately obtain the indoor location information of a moving object, the WIFI fingerprint-based indoor localization method uses a large number of WIFI signal measurements to establish a fingerprint database in the training stage and obtains the location of the moving object by matching fingerprints in the test stage.…”

Section: Introductionmentioning

confidence: 99%

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

Zhang

Wang

et al. 2021

Sensors

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With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.

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“…In such an industry environment, positioning technology not only meets basic positioning and navigation requirements but also brings huge changes to more scenarios. According to the application of positioning technology, the projects under construction and completed projects of the domestic integrated pipeline gallery can be divided into passive personnel positioning based on online patrol [1], personnel positioning based on WiFi [2], personnel positioning based on UWB (ultrawide band) technology [3], personnel positioning based on RFID (radio frequency identification) technology [4], and personnel positioning based on Bluetooth technology [5]. Among them, WiFi-based fingerprint positioning has been the most widely used due to its low cost, multiuser accessibility, wide coverage, high transmission rate, and strong anti-interference ability.…”

Section: Introductionmentioning

confidence: 99%

Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

Pan

Xiang

Xiong

et al. 2021

Wireless Communications and Mobile Computing

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Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k -nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

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Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Cited by 10 publications

References 41 publications

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

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