scholarly journals Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 618
Author(s):  
Jan Grottke ◽  
Jörg Blankenbach
Keyword(s):  
Particle Filter ◽  
Sensor Fusion ◽  
Local Area Network ◽  
Information Source ◽  
Position Estimation ◽  
Measurement Unit ◽  
Area Network ◽  
Fusion Algorithm ◽  
Individual Measurement ◽  
Grid Based

Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit (IMU) based movements of the pedestrian dead reckoning with the absolute fingerprinting-based position estimations of Wireless Local Area Network (WLAN), Bluetooth (Bluetooth Low Energy—BLE), and magnetic field anomalies as well as a building model in real time. Thus, a step-based position estimation without knowledge of any start position was achieved. For this, a grid-based particle filter and a Bayesian filter approach were combined. Furthermore, various optimization methods were compared to weigh the different information sources within the sensor fusion algorithm, thus achieving high position accuracy. Although a particle filter was used, no particles move due to a novel grid-based particle interpretation. Here, the particles’ probability values change with every new information source and every stepwise iteration via a probability-map-based approach. By adjusting the weights of the individual measurement methods compared to a knowledge-based reference, the mean and the maximum position error were reduced by 31%, the RMSE by 34%, and the 95-percentile positioning errors by 52%.

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

A Comparison of SLAM Prediction Densities Using the Kolmogorov Smirnov Statistic

2016 ◽  
Vol 04 (04) ◽  
pp. 245-254
Author(s):  
Akshay Rao ◽  
Wang Han ◽  
P. G. C. N. Senarathne
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Fusion Algorithm ◽  
Central Difference ◽  
Localization Algorithms ◽  
Kolmogorov Smirnov ◽  
Prediction Density ◽  
Smirnov Statistic

Accurate pose and trajectory estimates, are necessary components of autonomous robot navigation system. A wide variety of Simultaneous Localization and Mapping (SLAM) and localization algorithms have been developed by the robotics community to cater to this requirement. Some of the sensor fusion algorithms employed by SLAM and localization algorithms include the particle filter, Gaussian Particle Filter, the Extended Kalman Filter, the Unscented Kalman Filter, and the Central Difference Kalman Filter. To guarantee a rapid convergence of the state estimate to the ground truth, the prediction density of the sensor fusion algorithm must be as close to the true vehicle prediction density as possible. This paper presents a Kolmogorov–Smirnov statistic-based method to compare the prediction densities of the algorithms listed above. The algorithms are compared using simulations of noisy inputs provided to an autonomous robotic vehicle, and the obtained results are analyzed. The results are then validated using data obtained from a robot moving in controlled trajectories similar to the simulations.

scholarly journals Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6307
Author(s):  
Marco Caruso ◽  
Angelo Maria Sabatini ◽  
Marco Knaflitz ◽  
Ugo Della Croce ◽  
Andrea Cereatti
Keyword(s):  
Sensor Fusion ◽  
Parameter Tuning ◽  
Optical Data ◽  
Measurement Unit ◽  
Average Error ◽  
Fusion Algorithm ◽  
Multiple Parameters ◽  
Rigid Constraint ◽  
Parameter Values ◽  
Constraint Method

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online.

scholarly journals Data Processing Method in Medium Range Coastal Radar Complex

2018 ◽  
pp. 35-41
Author(s):  
Dang Quang Hieu ◽  
Nguyen Xuan Truong
Keyword(s):  
Situation Awareness ◽  
Local Area Network ◽  
Local Area ◽  
Radar Data ◽  
System Structure ◽  
Local Network ◽  
Area Network ◽  
Monitoring And Control ◽  
Fusion Algorithm ◽  
And Control

The article presents the basic principles of design and development of integrated middle range Coastal Surveillance System (CSS) used for water surface lookout. It provides solutions for such missions as command and control of maritime forces, border monitoring and control, prevention of illegal activities such as piracy, smuggling, illegal immigration, illegal fishing, supporting search and rescue (SAR) operations, and creates a common situation awareness picture of the Naval Theatre. The system structure diagram is designed to solve computational overload problem when processing large volume of data received from radar stations. The measurement-level fusion algorithm is developed based on the JPDA framework, in which radar data received from a single or group of radars and AIS data is aggregated in a processing center. The servers and workstations make use of local area network (LAN), using standard Gigabit Ethernet technologies for local network communications. Acquisition, analysis, storage and distribution of target data is executed in servers, then the data is sent to automated operator stations (console), where functional operations for managing, identifying and displaying of target on digital situational map are performed.

scholarly journals A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

2019 ◽  
Vol 9 (20) ◽  
pp. 4379 ◽  
Author(s):  
Alwin Poulose ◽  
Jihun Kim ◽  
Dong Seog Han
Keyword(s):  
Sensor Fusion ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Localization Error ◽  
Positioning Errors ◽  
Indoor Scenarios ◽  
Fusion Framework

Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

Enhanced Kalman filter algorithm using fuzzy inference for improving position estimation in indoor navigation

2021 ◽  
Vol 40 (5) ◽  
pp. 8991-9005
Author(s):  
Faisal Jamil ◽  
DoHyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Fuzzy Inference ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Measurement Unit ◽  
Fusion Algorithm ◽  
Positioning Systems ◽  
Sensory Data ◽  
Kalman Filter Algorithm

In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.

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