Improved Obstacle Mitigation and Localization Accuracy in Narrowband Ultrasonic Localization Systems Using RoBCUL Algorithm

2020 ◽  
Vol 69 (5) ◽  
pp. 2315-2324
Author(s):  
Sebastian Haigh ◽  
Janusz Kulon ◽  
Adam Partlow ◽  
Paul Rogers ◽  
Colin Gibson
Keyword(s):  
Localization Accuracy ◽  
Localization Systems

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

Reducing Tracking Error in RFID Real-Time Localization Systems Using Kalman Filters

2014 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Benjamin Sanda ◽  
Ikhlas Abdel-Qader ◽  
Abiola Akanmu
Keyword(s):  
Real Time ◽  
Radio Frequency Identification ◽  
Kalman Filters ◽  
Tracking Error ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Real World Data ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Localization Systems

The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology in this way experience localization errors due to noise and multipath effects inherent to these environments. This paper presents the use of both linear Kalman filters and non-linear Unscented Kalman filters to reduce the error rate inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a filtering model based on 3rd order motion is developed. The filtering model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw time of arrival measurements as well as final localization results.

scholarly journals Improving Accuracy and Reliability of Bluetooth Low-Energy-Based Localization Systems Using Proximity Sensors

2019 ◽  
Vol 9 (19) ◽  
pp. 4081 ◽  
Author(s):  
Marcin Kolakowski
Keyword(s):  
Assisted Living ◽  
Experimental Studies ◽  
Low Energy ◽  
Elderly Persons ◽  
Bluetooth Low Energy ◽  
Localization Accuracy ◽  
Hybrid Positioning ◽  
Improving Accuracy ◽  
The Cost ◽  
Localization Systems

One of the functionalities which are desired in Ambient and Assisted Living systems is accurate user localization at their living place. One of the best-suited solutions for this purpose from the cost and energy efficiency points of view are Bluetooth Low Energy (BLE)-based localization systems. Unfortunately, their localization accuracy is typically around several meters and might not be sufficient for detection of abnormal situations in elderly persons behavior. In this paper, a concept of a hybrid positioning system combining typical BLE-based infrastructure and proximity sensors is presented. The proximity sensors act a supporting role by additionally covering vital places, where higher localization accuracy is needed. The results from both parts are fused using two types of hybrid algorithms. The paper contains results of simulation and experimental studies. During the experiment, an exemplary proximity sensor VL53L1X has been tested and its basic properties modeled for use in the proposed algorithms. The results of the study have shown that employing proximity sensors can significantly improve localization accuracy in places of interest.

Improving Accuracy for Range-Based Localization in Multiple Target Wireless Sensor Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Oscar Rodríguez ◽  
Tomoaki Ohtsuki
Keyword(s):  
Location Estimation ◽  
Wireless Sensor ◽  
Target Range ◽  
Time Of Arrival ◽  
Multiple Target ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Improving Accuracy ◽  
Localization Systems

We introduce a system for multiple target range-based localization systems, such as those based on time-of-arrival (TOA) and received signal strength indicator (RSSI) for distance measurement estimations. In order to improve the accuracy of the location estimation for all target nodes, our system makes use of distance estimations between target nodes as well as between anchor and target nodes, and weights for all nodes. We propose variations on two popular localization algorithms and compare their accuracy against that of the conventional algorithms using simulations and experiments. Our results show that our proposal consistently offers a better localization accuracy than the conventional algorithms.

scholarly journals Noise reduction for radio map crowdsourcing building in WLAN indoor localization system

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Liye Zhang ◽  
Zhuang Wang ◽  
Xiaoliang Meng ◽  
Chao Fang ◽  
Cong Liu
Keyword(s):  
Indoor Localization ◽  
Sensor Data ◽  
Mode Switching ◽  
Map Building ◽  
Position Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Straight Line ◽  
Radio Map ◽  
Localization Systems

AbstractRecent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost-effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor localization system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduce the noise in the location space of the radio map, the short-term Fourier transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

scholarly journals Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

2017 ◽  
Author(s):  
Liye Zhang ◽  
Shahrokh Valaee ◽  
Yu Bin Xu ◽  
Lin Ma ◽  
Farhang Vedadi
Keyword(s):  
Supervised Learning ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Propagation Model ◽  
Localization Accuracy ◽  
Crowdsourced Data ◽  
Negative Effect ◽  
Rapid Deployment ◽  
Radio Map ◽  
Localization Systems

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

scholarly journals A Deep Learning Approach for Wi-Fi Based People Localization

2018 ◽  
Author(s):  
A. M. Khalili ◽  
Abdel-Hamid Soliman ◽  
Md Asaduzzaman
Keyword(s):  
Deep Learning ◽  
Prior Knowledge ◽  
Building Block ◽  
Learning Approach ◽  
Localization Accuracy ◽  
End To End ◽  
Task Architecture ◽  
Localization Systems

People localization is a key building block in many applications. In this paper, we propose a deep learning based approach that significantly improves the localization accuracy and reduces the runtime of Wi-Fi based localization systems. Three variants of the deep learning approach are proposed, a sub-task architecture, an end-to-end architecture, and an architecture that incorporates prior knowledge. The performance of the three architectures under different conditions is evaluated and the significant improvement of the three architectures over existing approaches is demonstrated.

scholarly journals A Deep Learning Approach for Wi-Fi Based People Localization

2018 ◽  
Author(s):  
A. M. Khalili ◽  
Abdel-Hamid Soliman ◽  
Md Asaduzzaman
Keyword(s):  
Deep Learning ◽  
Prior Knowledge ◽  
Building Block ◽  
Learning Approach ◽  
Localization Accuracy ◽  
End To End ◽  
Task Architecture ◽  
Localization Systems

People localization is a key building block in many applications. In this paper, we propose a deep learning based approach that significantly improves the localization accuracy and reduces the runtime of Wi-Fi based localization systems. Three variants of the deep learning approach are proposed, a sub-task architecture, an end-to-end architecture, and an architecture that incorporates prior knowledge. The performance of the three architectures under different conditions is evaluated and the significant improvement of the three architectures over existing approaches is demonstrated.

scholarly journals Robust Power Allocation for Cooperative Localization in Jammed Wireless Sensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Mingxing Ke ◽  
Shiwei Tian ◽  
Lu Lu ◽  
Chuang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Allocation ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Sensor Localization ◽  
Localization Accuracy ◽  
Robust Power Allocation ◽  
Allocation Strategies ◽  
Localization Systems

In this paper, we propose robust power allocation strategies to improve the localization performance in cooperative wireless sensor localization systems when suffering interference of jammer nodes. In wireless sensor localization systems, transmitting power strategies will affect the localization accuracy and determine the lifetime of wireless sensor networks. At the same time, the power allocation problem will be evolution to a new challenge when there are jammed nodes. So in this paper, we first present the optimization framework in jammed cooperative localization systems. Moreover, the imperfect parameter estimations of agent and jammer nodes are considered to develop robust power allocation strategies. In particular, this problem can be transformed into second-order cone programs (SOCPs) to obtain the end solution. Numerical results show the proposed power allocation strategies can achieve better performance than uniform power allocation and the robust schemes can ensure lower localization error than nonrobust power control when systems are subject to uncertainty.

scholarly journals Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

2019 ◽  
Author(s):  
Timothy Otim ◽  
Luis Enrique Díez ◽  
Alfonso Bahillo ◽  
Peio Lopez Iturri ◽  
Francisco Falcone
Keyword(s):  
Head Position ◽  
Position Estimation ◽  
The Body ◽  
Wearable Sensor ◽  
Sensor Location ◽  
Localization Accuracy ◽  
Sensor Position ◽  
Accurate Position ◽  
Sensor Positions ◽  
Localization Systems

In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence the accuracy obtained is subject to a given wearable sensor position. In this paper, we study the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target wearable sensor. And so, the large errors arise due to the signal arriving at the target wearable sensor from reflections of a nearby object or a wall in the environment.

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