Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System

Mo, Yun; Zhang, Zhongzhao; Yang, Lu; Meng, Weixiao; Agha, Gul

doi:10.1155/2014/850926

Cited by 14 publications

(15 citation statements)

References 18 publications

(21 reference statements)

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“…When applied to LF techniques, random forests outperform other machine learning methods such as artificial neural nets and support vector machines while requiring less training time complexity (Mo et al . ). Random forest models can also provide the user with the distribution of conditional predictions produced by the trees, rather than only a single conditional mean for the forest.…”

Section: Methodsmentioning

confidence: 97%

“…Random forests are an ensemble machine learning method that combine multiple models based on 'weak' subsets of the learning data to create a single model with high accuracy (Breiman 2001). When applied to LF techniques, random forests outperform other machine learning methods such as artificial neural nets and support vector machines while requiring less training time complexity (Mo et al 2014). Random forest models can also provide the user with the distribution of conditional predictions produced by the trees, rather than only a single conditional mean for the forest.…”

Section: E S T I M a T I N G T A G L O C A T I O Nmentioning

confidence: 99%

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Novel, continuous monitoring of fine‐scale movement using fixed‐position radiotelemetry arrays and random forest location fingerprinting

et al. 2017

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Summary1. Radio-tag signals from fixed-position antennas are most often used to indicate presence or absence of individuals, or to estimate individual activity levels from signal strength variation within an antenna's detection zone. The potential of such systems to provide more precise information on tag location and movement has not been explored in great detail in an ecological setting. 2. By reversing the roles that transmitters and receivers play in localization methods common to the telecommunications industry, we present a new telemetric tool for accurately estimating the location of tagged individuals from received signal strength values. The methods used to characterize the study area in terms of received signal strength are described, as is the random forest model used for localization. The resulting method is then validated using test data before being applied to true data collected from tagged individuals in the study site. 3. Application of the localization method to test data withheld from the learning dataset indicated a low average error over the entire study area ( <1 m), whereas application of the localization method to real data produced highly probable results consistent with field observations. 4. This telemetric approach provided detailed movement data for tagged fish along a single axis (a migratory path) and is particularly useful for monitoring passage along migratory routes. The new methods applied in this study can also be expanded to include multiple axes (x, y, z) and multiple environments (aquatic and terrestrial) for remotely monitoring wildlife movement.

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Section: Methodsmentioning

confidence: 97%

Section: E S T I M a T I N G T A G L O C A T I O Nmentioning

confidence: 99%

Novel, continuous monitoring of fine‐scale movement using fixed‐position radiotelemetry arrays and random forest location fingerprinting

et al. 2017

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“…Jedari et al [12] compared the RF with KNN and a rules-based classifier (JRip), and the results indicated that the RF classifier presents the best performance as compared to KNN and JRip classifiers with positioning accuracy higher than 91%. Mo et al [13] proposed a coarse positioning method based on RF, which is able to customize several subregions, and test point to the region with an outstanding accuracy compared with some typical clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

Combination of DNN and Improved KNN for Indoor Location Fingerprinting

Dai

Yang

Wang

et al. 2019

Wireless Communications and Mobile Computing

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Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.

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“…Mo et al [21] proposed the usage of kernel PCA (KPCA) algorithm for the coarse-level prediction of manually labelled cluster using Random Forest. ey derived trained matrices from extracted KPCA features and prepared subradio maps.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

Akram

Akbar

Kim

2018

Mobile Information Systems

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Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks, k-nearest neighbors, K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.

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Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System

Cited by 14 publications

References 18 publications

Novel, continuous monitoring of fine‐scale movement using fixed‐position radiotelemetry arrays and random forest location fingerprinting

Novel, continuous monitoring of fine‐scale movement using fixed‐position radiotelemetry arrays and random forest location fingerprinting

Combination of DNN and Improved KNN for Indoor Location Fingerprinting

CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

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