Low-Cost Three-Dimensional Navigation Solution for RISS/GPS Integration Using Mixture Particle Filter

Georgy, Jacques; Noureldin, Aboelmagd; Korenberg, Michael J.; Bayoumi, M.M.

doi:10.1109/tvt.2009.2034267

Cited by 125 publications

(98 citation statements)

References 23 publications

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“…The field test data was collected by installing various equipments in a test vehicle [24]. These include a Crossbow IMU 300CC-100, reference high grade Honeywell IMU-HG1700, Novatel OEM GPS receivers and computer.…”

Section: Results Ablementioning

confidence: 99%

“…To combine the advantages of LPF and SIR filters, mixture particle filters (MPF) were developed. These MPF can be based on mixture posterior; where posterior is a weighted Gaussian mixture of parallel EKF or mixture proposal distributions [24], where distribution is based on mixture transition and likelihood functions. However, there are number of practical implementation limitations of these filters, for eg.…”

Section: Types Of Particle Filtersmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Voronoi Based Particle Filter for Multi-Sensor Data Fusion

Cheruvu¹,

Aggarwal²,

Devabhaktuni³

2012

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Seamless and reliable navigation for civilian/military application is possible by fusing prominent Global Positioning System (GPS) with Inertial Navigation System (INS). This integrated GPS/INS unit exhibits a continuous navigation solution with increased accuracy and reduced uncertainty or ambiguity. In this paper, we propose a novel approach of dynamically creating a Voronoi based Particle Filter (VPF) for integrating INS and GPS data. This filter is based on redistribution of the proposal distribution such that the redistributed particles lie in high likelihood region; thereby increasing the filter accuracy. The usual limitations like degeneracy, sample impoverishment that are seen in conventional particle filter are overcome using our VPF with minimum feasible particles. The small particle size in our methodology reduces the computational load of the filter and makes real-time implementation feasible. Our field test results clearly indicate that the proposed VPF algorithm effectively compensated and reduced positional inaccuracies when GPS data is available. We also present the preliminary results for cases with short GPS outages that occur for low-cost inertial sensors.

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Section: Results Ablementioning

confidence: 99%

Section: Types Of Particle Filtersmentioning

confidence: 99%

A Novel Voronoi Based Particle Filter for Multi-Sensor Data Fusion

Cheruvu¹,

Aggarwal²,

Devabhaktuni³

2012

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“…PF has been used for GPS/INS integrations using different approaches (Yi and Brzezinska 2006, Giremus et al, 2005, Caron et al, 2007and Georgy et al, 2010. PF handles the multi-dimensional integrals numerically rather than the exact analytical solution.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Filtering for Precise Point Positioning GPS/INS integration

Rabbou

El-Rabbany

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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This research investigates the performance of non-linear estimation filtering for GPS-PPP/MEMS-based inertial system. Although integrated GPS/INS system involves nonlinear motion state and measurement models, the most common estimation filter employed is extended Kalman filter. In this paper, both unscented Kalman filter and particle filter are developed and compared with extended Kalman filter. Tightly coupled mechanization is adopted, which is developed in the raw measurements domain. Un-differenced ionosphere-free linear combination of pseudorange and carrier-phase measurements is employed. The performance of the proposed non-linear filters is analyzed using real test scenario. The test results indicate that comparable accuracy-level are obtained from the proposed filters compared with extended Kalman filter in positioning, velocity and attitude when the measurement updates from GPS measurements are available.

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“…Having reliable and accurate location information significantly helps in emergency services, navigation, commercial services, recreation, tracking, and networking (Zandbergen, 2009 (Georgy, Noureldin, Korenberg, & Bayoumi, 2010a, 2010b. To obtain a device's current location, a positioning system needs to obtain contextual information from a user; a GPS might need to know whether a user is walking or driving, and the speed of the two transportation modes.…”

Section: Introductionmentioning

confidence: 99%

Impact of Indoor Location Information Reliability on Users’ Trust of an Indoor Positioning System

Wei

Bell

2012

Geographic Information Science

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Indoor positioning systems have been used as a supplement to provide positioning in settings where GPS does not function. However, the accuracy of calculated results varies among techniques and algorithms used; system performance also differs across testing environments. As a result, users' responses to and opinions of these positioning results could be different. Furthermore, user trust, most closely associated with their confidence in the system, will also vary. A relatively little studied topic is the effect of positioning variance on a user's opinion or trust of such systems (GPS as well, for that matter). Therefore, understanding how user interaction with such systems (through trust) changes is important for achieving more usable positioning system design. An experiment was designed to examine if the sequence of location accuracy will affect users' trust in an individual episode positioning result as well as the system overall. The simulated positioning system running on an iPad used for this experiment provides 10 priming positioning results at a specific category of accuracy.The accuracy is controlled and is presented as either 1. ACCURATE (within 5 meters of actual location), 2. INACCURATE (greater 15 meters), 0r 3. WRONG BUILDING (outside current building's footprint). After one set of these priming locations a series of 55 post-priming locations across the same categories in addition to 10 CONTINUOUS locations (with between 6 and 15 meters of error) were presented. At each experimental site participants located themselves using the simulated system and rated their trust for that location. Variables obtained from the experiment include: 1. Two types of trust at each location (positioning trust and system trust); 2. Spatial abilities, sense of direction, and ancillary survey data (user characteristics). Results show that users' trust varies iii among different accuracy categories and changes over time according to the system performance in association with their own characteristics. Specifically, the accuracy of the priming locations has an impact on users' trust of later results. Besides, users' trust in individual positioning results is quite variable and the variability is closely related to accuracy, while user trust of the overall system is less variable.iv ACKNOWLEDGMENTS

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Low-Cost Three-Dimensional Navigation Solution for RISS/GPS Integration Using Mixture Particle Filter

Cited by 125 publications

References 23 publications

A Novel Voronoi Based Particle Filter for Multi-Sensor Data Fusion

A Novel Voronoi Based Particle Filter for Multi-Sensor Data Fusion

Non-Linear Filtering for Precise Point Positioning GPS/INS integration

Impact of Indoor Location Information Reliability on Users’ Trust of an Indoor Positioning System

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