A Resetting Approach for INS and UWB Sensor Fusion Using Particle Filter for Pedestrian Tracking

Tian, Qinglin; Wang, Kevin I‐Kai; Salčić, Zoran

doi:10.1109/tim.2019.2958471

Cited by 51 publications

(33 citation statements)

References 18 publications

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“…Approaches to mitigate particle filter divergence that are tailored to a specific application include [ 50 ] for indoor localization using an inertial measurement unit or [ 51 ] for pedestrian tracking using inertial navigation system and ultra-wideband technology.…”

Section: Solutions To the Particle Filter Challengesmentioning

confidence: 99%

Particle Filters: A Hands-On Tutorial

Elfring

Torta

Molengraft

2021

Sensors

113

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The particle filter was popularized in the early 1990s and has been used for solving estimation problems ever since. The standard algorithm can be understood and implemented with limited effort due to the widespread availability of tutorial material and code examples. Extensive research has advanced the standard particle filter algorithm to improve its performance and applicability in various ways in the years after. As a result, selecting and implementing an advanced version of the particle filter that goes beyond the standard algorithm and fits a specific estimation problem requires either a thorough understanding or reviewing large amounts of the literature. The latter can be heavily time consuming especially for those with limited hands-on experience. Lack of implementation details in theory-oriented papers complicates this task even further. The goal of this tutorial is facilitating the reader to familiarize themselves with the key concepts of advanced particle filter algorithms and to select and implement the right particle filter for the estimation problem at hand. It acts as a single entry point that provides a theoretical overview of the filter, its assumptions and solutions for various challenges encountered when applying particle filters. Besides that, it includes a running example that demonstrates and implements many of the challenges and solutions.

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Section: Solutions To the Particle Filter Challengesmentioning

confidence: 99%

Particle Filters: A Hands-On Tutorial

Elfring

Torta

Molengraft

2021

Sensors

113

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“…The parameters of the model were determined by ranging results, and the inputs of the model were the RSSIs of the FTs. According to the maximum likelihood method, µ is the mean of the RSSI and σ is the variance of the RSSI in Equations (6) and 7, where RSSI i d is the RSSI collected at the distance d; N is the number of RSSI collected. The determination of µ and σ is described below in detail.…”

Section: The Identification Model Of Los/nlosmentioning

confidence: 99%

“…The training data acquisition time was only one day away from that of the testing data, and the training data did not participate in the test. for all construct the approximate distance matrix , ; construct the constant matrix L, according to the equation 4; construct the cosine coefficient matrix A , according to the equation (6) ; end for calculate the unknown parameter , according to the equation 7; update the approximate coordinare ; until meet the condition of stop x, the final approximate coordinate of MT…”

Section: Evaluation Of the Identification Modelmentioning

confidence: 99%

“…The importance of indoor positioning is reflected by the increasing requirements of location-based services (LBSs). Under such a background, global researchers have proposed a lot of indoor positioning methods based on different technologies, such as wireless fidelity (Wi-Fi) [1,2], Bluetooth [3], radio frequency identification (RFID) [4], computer vision [5], ultra-wideband (UWB) [6], infrared [7], and inertial navigation systems (INSs) [8], micro-electro-mechanical systems (MEMSs) [9], visible light [10,11], geomagnetic fields [12,13], and pseudolites [14,15], among others.…”

Section: Introductionmentioning

confidence: 99%

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A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Wang

et al. 2020

Applied Sciences

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In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

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“…Combining data from different sensors has been done successfully for numerous applications [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. In this work, we employ a Bayesian framework and machine learning methods to build a model that combines radar batted ball data and optical running speed data.…”

Section: Introductionmentioning

confidence: 99%

Combining Radar and Optical Sensor Data to Measure Player Value in Baseball

Healey

2020

Sensors

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Evaluating a player’s talent level based on batted balls is one of the most important and difficult tasks facing baseball analysts. An array of sensors has been installed in Major League Baseball stadiums that capture seven terabytes of data during each game. These data increase interest among spectators, but also can be used to quantify the performances of players on the field. The weighted on base average cube model has been used to generate reliable estimates of batter performance using measured batted-ball parameters, but research has shown that running speed is also a determinant of batted-ball performance. In this work, we used machine learning methods to combine a three-dimensional batted-ball vector measured by Doppler radar with running speed measurements generated by stereoscopic optical sensors. We show that this process leads to an improved model for the batted-ball performances of players.

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A Resetting Approach for INS and UWB Sensor Fusion Using Particle Filter for Pedestrian Tracking

Cited by 51 publications

References 18 publications

Particle Filters: A Hands-On Tutorial

Particle Filters: A Hands-On Tutorial

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Combining Radar and Optical Sensor Data to Measure Player Value in Baseball

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