A comparison of human body compensation models for RSSI based localization and tracking

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Indoor Positioning System Berdasarkan Fingerprinting Received Signal Strength (Rss) Wifi dengan Algoritma K-nearest Neighbor (K-nn)

Wireless networks other than communication media can be used to find out the existence of an object. Positioning technology that is commonly used is Global Positioning System (GPS). GPS can receive location information accurately in outdoor, this situation is contradictory in indoor environment, GPS signal is interrupted by signal attenuation caused by building materials and types of physical barriers. This study aims as an alternative solution for indoor positioning using RSS (Received Signal Strength) WiFi. Fingerprinting technique is used to collect RSS data on 5 access points in 3 test locations, RSS data collected is 243 data. This study uses Euclidean Distance and K-Nearest Neighbor (K-NN) method. The accuracy of the system is tested using the 10-Fold Cross Validation method based on the results of the test shows that the system is able to determine the location with an accuracy rate of 96.71%

An assessment of different optimization strategies for location tracking with an Android application on a smartphone

This paper presents a study of the efficacy of different optimization strategies for location tracking on an Android App that is run on a smartphone. The basic algorithm determines the most probable path of the user within a WiFi network by comparing raw RSSI measurements at each location with values in a fingerprint database. The investigated optimization strategies include: accounting for previous locations, increasing the number of WiFi scans per location, applying an advanced averaging technique, exploiting accelerometer data, shifting the frequency band from 2.4 to 5 GHz, and changing the position of the smartphone with respect to the body. It is shown that especially the accelerometer data allow enhancing the location estimation significantly. By combining different techniques, an average accuracy better than 2 m can be achieved

Enhanced indoor location tracking through body shadowing compensation

This paper presents a radio frequency (RF)-based location tracking system that improves its performance by eliminating the shadowing caused by the human body of the user being tracked. The presence of such a user will influence the RF signal paths between a body-worn node and the receiving nodes. This influence will vary with the user's location and orientation and, as a result, will deteriorate the performance regarding location tracking. By using multiple mobile nodes, placed on different parts of a human body, we exploit the fact that the combination of multiple measured signal strengths will show less variation caused by the user's body. Another method is to compensate explicitly for the influence of the body by using the user's orientation toward the fixed infrastructure nodes. Both approaches can be independently combined and reduce the influence caused by body shadowing, hereby improving the tracking accuracy. The overall system performance is extensively verified on a building-wide testbed for sensor experiments. The results show a significant improvement in tracking accuracy. The total improvement in mean accuracy is 38.1% when using three mobile nodes instead of one and simultaneously compensating for the user's orientation

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, K-nearest neighbor (KNN), fingerprint-based localizatio

Semi-Sequential Probabilistic Model For Indoor Localization Enhancement

This paper proposes a semi-sequential probabilistic model (SSP) that applies an additional short term memory to enhance the performance of the probabilistic indoor localization. The conventional probabilistic methods normally treat the locations in the database indiscriminately. In contrast, SSP leverages the information of the previous position to determine the probable location since the user's speed in an indoor environment is bounded and locations near the previous one have higher probability than the other locations. Although the SSP utilizes the previous location information, it does not require the exact moving speed and direction of the user. On-site experiments using the received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for localization demonstrate that SSP reduces the maximum error and boosts the performance of existing probabilistic approaches by 25% - 30%