An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning

Li, Changgeng; Qiu, Zhengyang; Liu, Changtong

doi:10.1007/s11277-017-4295-z

Cited by 61 publications

(28 citation statements)

References 19 publications

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“…The reduction in the localization distance error, RMSE, using the proposed method is verified by comparing it to other methods in the literature. It is evident from Table II that the the proposed method yields the lowest localization distance error, RMSE, compared to the other existing methods in the literature such as those by Bahl et al [10], Li et al [38], Alsindi et al [28], [18].…”

Section: B Machine Learning For Localization Position Estimationmentioning

confidence: 79%

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

AlHajri¹,

Ali²,

Shubair³

2019

Antennas Wirel. Propag. Lett.

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Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This paper presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on k-Nearest Neighbor (k-NN) machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments.Received Signal Strength, Channel Transfer Function, and Frequency Coherence Function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.

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Section: B Machine Learning For Localization Position Estimationmentioning

confidence: 79%

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

AlHajri¹,

Ali²,

Shubair³

2019

Antennas Wirel. Propag. Lett.

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“…The research used 2.4G and 5G WiFi signal to compare localization accuracy from Manhattan distance of RSS average error, resulting 1.4700 for 2.4G and 1.1500 for 5G. Other KNN method used by [11] is called weighted-KNN. Where a parameter called weight assigned to every coordinate according to the value of distance.…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning System using Regression-based Fingerprint Method

Ghozali¹,

Kusuma²

2019

IJACSA

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Indoor Positioning System has opportunity to be used in different business platform. Based on past research, optimized localization method for Bluetooth Low Energy (BLE) to predict position of person or object with high accuracy has not been found yet. Most recent research that have solve Received Signal Strength (RSS) inconsistent value is using fingerprint method. This paper proposed a deep regression machine learning using convolutional neural network (CNN) with regression-based fingerprint model to estimate real position. The model used 5 nearest fingerprints as reference RSS values with their location (x or y) label as inputs to produce output of single value position (x or y), then repeat the process to produce second value of position to create complete coordinate of estimated position. To evaluate the proposed model, a comparison between training data with validation data using Root Mean Squared Error (RMSE) is used. The comparisons are with Multilayer Perceptron model and with the weighted sum method as benchmark. The experiment Gave results of mean distance and 90th percentile distance between proposed model with the benchmark. CNN model achieved accuracies of lower than 330cm at 90th percentile with mean distance lower than 185cm. Weighted sum model achieved accuracies lower than 360cm at 90th percentile with mean distance higher than 185cm, and MLP is in between them. The result demonstrates that the proposed method outperformed the benchmark methods.

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“…Nevertheless, due to the fact that equal RSS differences at different RSS levels may not mean equal differences in geometrical distances in the calculation of signal distances between different RSS vectors, Li et al [22] proposed a feature-scalingbased k-nearest neighbor (FS-kNN) algorithm for achieving improved localization accuracy. Since the accuracy of KNN-based algorithm using Euclidean distance is not high enough due to the ignorance of statistical regularities from the training set, an improved method combining the Manhattan distance with the WKNN algorithm was proposed to distinguish the influence of different reference nodes [23]. To obtain the optimized node location estimate, Fang et al [24] proposed an optimal WKNN (OWKNN) algorithm for wireless sensor network (WSN) fingerprint localization in a noisy environment.…”

Section: Related Workmentioning

confidence: 99%

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

Zhang

Jiang

2019

J Wireless Com Network

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Accurate detection of the unknown radio transmitter (URT) is crucial to combat illegal occupation of radio signal resources and protect communication system from harmful signal interference. The fingerprint positioning technique based on received signal strength (RSS) is famous for requiring no extra equipment, antenna arrays, and time synchronization. However, conventional RSS-based fingerprint positioning techniques that utilize K-nearest neighbor (KNN) method are confronted with problems when the positioning target is radio transmitter with unknown emission strength and frequency. Moreover, they not only cannot realize the precise localization of the URT but also only rely on pre-set reference points in the fingerprint database. In this paper, a new KNN-based geo-location approach using received signal strength difference (RSSD) information and virtual reference point is proposed to estimate an URT location. To obtain more accurate RSSD measurements, a RSSD-based filtering method by calculating the Euclidean distance between each sampling RSSD and the average value is devised to establish the fingerprint database. To achieve higher positioning accuracy, we combine KNN technique with the virtual reference (VR) point to propose RSSD-VRKNN algorithm. The simulation results show that the proposed scheme can obtain the best positioning performance compared with the conventional KNN and weighted K-nearest neighbor (WKNN) techniques. The performance and feasibility of our proposed algorithm are verified through extensive experiments.

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An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning

Cited by 61 publications

References 19 publications

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

Indoor Positioning System using Regression-based Fingerprint Method

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

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