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Design and Implementation of Indoor Positioning System Using Particle Filter Based on Wireless Signal Intensity
http://doi.org/10.5626/JOK.2020.47.4.433
This paper proposes an Indoor Positioning System to track a user’s position indoors by using beacons’ wireless signal intensity. To overcome the non-linearity of an existing indoor positioning scheme using wireless signal intensity, a particle filter is used for a positioning algorithm, so the noise of the wireless signal intensity is not directly reflected on the positioning result. In the observation phase of the particle filter, the distance from a user’s smartphone is estimated based on the wireless signal intensity, and the similarity of each particle with an estimated ground truth is calculated through the predicted distance value. Also, our proposed positioning scheme uses the random walk technique (the Monte Carlo method) to calculate a position estimation value. Additionally, to solve the well-known local minimum problem of the particle filter, the particles estimated closest to the beacons according to the distance prediction values are given proximity weights, so the particles can quickly locate the user. The positioning error on the walking path is also corrected by considering the indoor map.
Parallel Gaussian Processes for Gait and Phase Analysis
This paper proposes a sequential state estimation model consisting of continuous and discrete variables, as a way of generalizing all discrete-state factorial HMM, and gives a design of gait motion model based on the idea. The discrete state variable implements a Markov chain that models the gait dynamics, and for each state of the Markov chain, we created a Gaussian process over the space of the continuous variable. The Markov chain controls the switching among Gaussian processes, each of which models the rotation or various views of a gait state. Then a particle filter-based algorithm is presented to give an approximate filtering solution. Given an input vector sequence presented over time, this finds a trajectory that follows a Gaussian process and occasionally switches to another dynamically. Experimental results show that the proposed model can provide a very intuitive interpretation of video-based gait into a sequence of poses and a sequence of posture states.
Robust Particle Filter Based Route Inference for Intelligent Personal Assistants on Smartphones
Much research has been conducted on location-based intelligent personal assistants that can understand a user"s intention by learning the user"s route model and then inferring the user"s destinations and routes using data of GPS and other sensors in a smartphone. The intelligence of the location-based personal assistant is contingent on the accuracy and efficiency of the real-time predictions of the user"s intended destinations and routes by processing movement information based on uncertain sensor data. We propose a robust particle filter based on Dynamic Bayesian Network model to infer the user"s routes. The proposed robust particle filter includes a particle generator to supplement the incorrect and incomplete sensor information, an efficient switching function and an weight function to reduce the computation complexity as well as a resampler to enhance the accuracy of the particles. The proposed method improves the accuracy and efficiency of determining a user"s routes and destinations.
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