scholarly journals An Improved Particle Filter Algorithm for Geomagnetic Indoor Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
He Huang ◽  
Wei Li ◽  
De An Luo ◽  
Dong Wei Qiu ◽  
Yang Gao
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Iteration Process ◽  
Indoor Positioning ◽  
Matching Theory ◽  
Positioning Error ◽  
Matching Algorithm ◽  
Point Set ◽  
Particle Filter Algorithm ◽  
Error Constraint

Geomagnetic indoor positioning is an attractive indoor positioning technology due to its infrastructure-free feature. In the matching algorithm for geomagnetic indoor localization, the particle filter has been the most widely used. The algorithm however often suffers filtering divergence when there is continuous variation of the indoor magnetic distribution. The resampling step in the process of implementation would make the situation even worse, which directly lead to the loss of indoor positioning solution. Aiming at this problem, we have proposed an improved particle filter algorithm based on initial positioning error constraint, inspired by the Hausdorff distance measurement point set matching theory. Since the operating range of the particle filter cannot exceed the magnitude of the initial positioning error, it avoids the adverse effect of sampling particles with the same magnetic intensity but away from the target during the iteration process on the positioning system. The effectiveness and reliability of the improved algorithm are verified by experiments.

Indoor Localization and Navigation for a Mobile Robot Equipped with Rotating Ultrasonic Sensors Using a Smartphone as the Robot's Brain

2020 ◽  
pp. 1018-1029
Author(s):  
Jongil Lim ◽  
Seokju Lee ◽  
Girma Tewolde ◽  
Jaerock Kwon
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
State Space ◽  
Indoor Localization ◽  
Robot Navigation ◽  
Control Architecture ◽  
Distance Measurements ◽  
Current Location ◽  
Computationally Expensive ◽  
Particle Filter Algorithm

Identifying the current location of a robot is a prerequisite for robot navigation. To localize a robot, one popular way is to use particle filters that estimate the posterior probabilistic density of a robot's state space. But this Bayesian recursion approach is computationally expensive. Most microcontrollers in a small mobile robot cannot afford it. The authors propose to use a smartphone as a robot's brain in which heavy-duty computations take place whereas an embedded microcontroller on the robot processes rudimentary sensors such as ultrasonic and touch sensors. In their design, a smartphone is wirelessly connected to a robot via Bluetooth by which distance measurements from the robot are sent to the smartphone. Then the smartphone takes responsible for computationally expensive operations like executing the particle filter algorithm. In this paper, the authors designed a mobile robot and its control architecture to demonstrate that the robot can navigate indoor environment while avoiding obstacles and localize its current position.

scholarly journals PDR Combined with Magnetic Fingerprint Algorithm for Indoor Positioning

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
He Huang ◽  
Kaiyue Qiu ◽  
Wei Li ◽  
Dean Luo
Keyword(s):  
Relative Position ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Fingerprint Matching ◽  
Localization Algorithm ◽  
Position Information ◽  
Matching Algorithm ◽  
Advantages And Disadvantages ◽  
Positioning Method ◽  
Particle Filter Algorithm

Geomagnetism has become a popular technology for indoor positioning, and its accuracy mainly depends on the accuracy of the geomagnetic matching algorithm. Pedestrian dead reckoning technology can calculate the relative position of pedestrians based on sensor information, but only obtain relative position information. According to the advantages and disadvantages of these two techniques, a high-precision GPDR indoor positioning method is proposed, and the improved particle filter algorithm is used to solve the problem of geomagnetic fingerprint fuzzy solution. Finally, a simulation experiment was conducted. The experimental results show that the accuracy of the proposed fusion localization algorithm is 42% higher than that of the PDR algorithm. Compared with a single geomagnetic fingerprint matching algorithm, the positioning accuracy is improved by 57%.

scholarly journals GEOMAGNETIC FIELD-BASED INDOOR POSITIONING USING BACK-PROPAGATION NEURAL NETWORKS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 557-563
Author(s):  
K. Y. Qiu ◽  
H. Huang ◽  
A. El-Rabbany
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Indoor Localization ◽  
Back Propagation ◽  
Weighted Average ◽  
Back Propagation Neural Network ◽  
Indoor Positioning ◽  
Experimental Results ◽  
Positioning Error ◽  
Main Research

Abstract. High-precision indoor positioning in complex environments has always been a hot research topic within the positioning and robotic communities. As one of the indoor positioning technologies, geomagnetic positioning is receiving widespread attention due to its global coverage. Additionally, geomagnetic positioning does not require special infrastructure configuration, its hardware cost is low, and its positioning errors do not accumulate over time. However, geomagnetic positioning is prone to mismatching, which causes serious problems at the positioning points. To tackle this challenge, this paper proposes an indoor localization method based on spectral clustering and weighted back-propagation neural network. The main research contribution is that in the offline phase, the spatial specificity of geomagnetism is used to define the similarity between fingerprints. In addition, a clustering-based reference point algorithm is proposed to divide the sub-fingerprint database, and a positioning prediction model based on back-propagation neural network is trained. Subsequently, in the online stage, the weights of different positioning prediction models are calculated according to the defined fingerprint similarity, weighted average prediction coordinates are obtained, and thereby the positioning accuracy is improved. Experimental results show that, in comparison with other neural network-based positioning methods, the positioning error of our proposed algorithm is reduced by approximately 26.6% and the positioning time is reduced by 24.7%. Experimental results show that the average positioning error of the algorithm is 1.81m.

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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