scholarly journals Research on improved indoor positioning algorithm based on WiFi–pedestrian dead reckoning

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771985193
Author(s):  
Guanghua Zhang ◽  
Xue Sun ◽  
Jingqiu Ren ◽  
Weidang Lu
Keyword(s):  
Nearest Neighbor ◽  
Dead Reckoning ◽  
Signal Strength ◽  
K Nearest Neighbor ◽  
Received Signal Strength Indicator ◽  
Positioning Accuracy ◽  
Positioning Algorithm ◽  
And Control ◽  
The Impact ◽  
Pedestrian Dead Reckoning

In order to improve the positioning accuracy and reduce the impact of indoor complex environment on WiFi positioning results, an improved fusion positioning algorithm based on WiFi–pedestrian dead reckoning is proposed. The algorithm uses extended Kalman filter as the fusion positioning filter of WiFi–pedestrian dead reckoning. Aiming at the problem of WiFi signal strength fluctuation, Bayesian estimation matching algorithm based on K-nearest neighbor is proposed to reduce the impact of the dramatic change of received signal strength indicator value on the positioning result effectively. For the cumulative error problem in pedestrian dead reckoning positioning algorithm, a post-correction module is used to reduce the error. The experimental results show that the algorithm can improve the shortcomings of these two algorithms and control the positioning accuracy within 1.68 m.

scholarly journals DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

2022 ◽  
Vol 14 (2) ◽  
pp. 297
Author(s):  
Jingxue Bi ◽  
Hongji Cao ◽  
Yunjia Wang ◽  
Guoqiang Zheng ◽  
Keqiang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Nearest Neighbor ◽  
Reference Points ◽  
Manhattan Distance ◽  
Support Vector ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Positioning Algorithm ◽  
Accuracy And Stability

A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.

scholarly journals An RSSD-Based Fingerprint Positioning Method for Detection of an Unknown Radio Transmitter Using WLS and Factor Graph

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Liyang Zhang ◽  
Taihang Du ◽  
Chundong Jiang
Keyword(s):  
Nearest Neighbor ◽  
Access Point ◽  
Reference Points ◽  
Least Square ◽  
Factor Graph ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Radio Transmitter ◽  
Error Weight ◽  
The Impact

Realizing accurate detection of an unknown radio transmitter (URT) has become a challenge problem due to its unknown parameter information. A method based on received signal strength difference (RSSD) fingerprint positioning technique and using factor graph (FG) has been successfully developed to achieve the localization of an URT. However, the RSSD-based FG model is not accurate enough to express the relationship between the RSSD and the corresponding location coordinates since the RSSD variances of reference points are different in practice. This paper proposes an enhanced RSSD-based FG algorithm using weighted least square (WLS) to effectively reduce the impact of RSSD measurement variance difference on positioning accuracy. By the use of stochastic RSSD errors between the measured value and the estimated value of the selected reference points, we utilize the error weight matrix to establish a new WLSFG model. Then, the positioning process of proposed RSSD-WLSFG algorithm is derived with the sum-product principle. In addition, the paper also explores the effects of different access point (AP) numbers and grid distances on positioning accuracy. The simulation experiment results show that the proposed algorithm can obtain the best positioning performance compared with the conventional RSSD-based K nearest neighbor (RSSD-KNN) and RSSD-FG algorithms in the case of different AP numbers and grid distances.

scholarly journals An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2769
Author(s):  
Jingjing Wang ◽  
Joongoo Park
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Gaussian Function ◽  
Low Complexity ◽  
Correction Method ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Indoor Location ◽  
Positioning Algorithm

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

scholarly journals Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5546
Author(s):  
Firdaus Firdaus ◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin
Keyword(s):  
Ray Tracing ◽  
Nearest Neighbor ◽  
Signal Strength ◽  
Signal Propagation ◽  
Local Area ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Received Signal Strength Indicator ◽  
Multipath Effects ◽  
Level 3

Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people’s effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people’s presence and multipath effects were considered.

scholarly journals A quadratic weighted centroid algorithm for tunnel personnel positioning

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091702
Author(s):  
Haiying Wang ◽  
Linhao Liang ◽  
Jian Xu ◽  
Hui She ◽  
Wuxiang Li
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Weighting Factor ◽  
Gaussian Filter ◽  
Received Signal Strength Indicator ◽  
Positioning Accuracy ◽  
Filter Model ◽  
Positioning Systems ◽  
Centroid Algorithm ◽  
Positioning Algorithm

To improve the accuracy and generalization of tunnel personnel positioning systems, this article proposes a quadratic weighted centroid algorithm. By adopting a Gaussian filter model to improve the range accuracy of the received signal strength indicator algorithm and combining the centroid algorithm and weighting factor with a trilateration positioning model, a quadratic weighted centroid algorithm is proposed to improve the positioning accuracy of unknown positioning nodes. The key ideas behind the quadratic weighted centroid algorithm include an optimization of the received signal strength indicator range value scheme, a centroid algorithm based on trilateral measurement positioning, and a weighting factor to improve the positioning accuracy of the trilateral centroid positioning algorithm. Compared with the centroid algorithm, the Min-Max multilateration algorithm, and the weighted centroid based on distance algorithm, the simulation results showed that (1) the positioning performance of the quadratic weighted centroid algorithm was superior to the other three algorithms; (2) when the reference nodes were symmetrically arranged, the positioning accuracy was higher than a fold line layout; and (3) when the lateral reference node spacing was extended from 20 to 30 m, the average positioning error met positioning accuracy requirements, which could reduce overall system costs.

An Improved RSSI Positioning Algorithm Based on Reference Distances

2014 ◽  
Vol 971-973 ◽  
pp. 1547-1552 ◽  
Author(s):  
Yun Feng Leng ◽  
Hai Ping Zhu ◽  
Talal Alsharari ◽  
Fei He
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Positioning Accuracy ◽  
Anchor Nodes ◽  
Reference Distance ◽  
Fluctuation Amplitude ◽  
Positioning Algorithm ◽  
Mean Filtering

Due to the fluctuation of RSSI (Received Signal Strength Indicator) value, the positioning accuracy of indoor localization algorithm that based on RSSI value is not high and unstable, RSSI filtering and reference distance used in this paper. Fluctuation amplitude of RSSI is reduced by mean filtering, and a known distance between anchor nodes introduced when calculating the unknown distance between blind node and anchor node. According to the result of experiment, the positioning accuracy of new localization algorithm can reach 1.45 meter, and it can meet the demand of indoor positioning.

scholarly journals The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Renzhou Gui ◽  
Tongjie Chen ◽  
Han Nie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Correlation Matrix ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Fmri Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Impact ◽  
Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

scholarly journals Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019 ◽  
Vol 9 (11) ◽  
pp. 2337 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Scene Recognition ◽  
Localization Error ◽  
K Nearest Neighbor ◽  
Current Location ◽  
Smartphone Sensors ◽  
The Impact ◽  
Localization Systems

Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian’s current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.

scholarly journals Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Qing Yang ◽  
Shijue Zheng ◽  
Ming Liu ◽  
Yawen Zhang
Keyword(s):  
Nearest Neighbor ◽  
Science And Technology ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Experimental Simulation ◽  
K Nearest Neighbor ◽  
Improvement Strategy ◽  
Fingerprint Algorithm ◽  
Received Signal Strength Indication

AbstractTo improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

Research on WPS/PDR/MM Integrated Algorithm for Pedestrian Navigation and Positioning

2013 ◽  
Vol 437 ◽  
pp. 870-875 ◽  
Author(s):  
Zhong Liang Deng ◽  
Fei Peng Xie ◽  
Yan Pei Yu ◽  
Xiao Hong Zhao ◽  
Zhuang Yuan
Keyword(s):  
User Experience ◽  
Dead Reckoning ◽  
Positioning System ◽  
Map Matching ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Integrated Method ◽  
Pedestrian Navigation ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

In order to solve the discontinuity of navigation and positioning in indoor signal coverage blind areas, and false region judgment caused by positioning error, an integrated method combining Wireless Positioning System (WPS), Pedestrian Dead Reckoning (PDR) and Map Matching (MM) is presented in this paper. By using the combination of Kalman filtered WPS and PDR information, inertial information and geographic information, pedestrian position could be evaluated. Through experiment, this method effectively increased positioning accuracy of the system as well as greatly improved the user experience.

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