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Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching

Koroglu, Muhammed Taha
http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298

Abstract Details

2020, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Inertial sensors became wearable with the advances in sensing and computing technologies in the last two decades. Captured motion data can be used to build a pedestrian inertial navigation system (INS); however, time-variant bias and noise characteristics of low-cost sensors cause severe errors in positioning. To overcome the quickly growing errors of so-called dead-reckoning (DR) solution, this research adopts a pedestrian INS based on a Kalman Filter (KF) with zero-velocity update (ZUPT) aid. Despite accurate traveled distance estimates, obtained trajectories diverge from actual paths because of the heading estimation errors. In the absence of external corrections (e.g., GPS, UWB), map information is commonly employed to eliminate position drift; therefore, INS solution is fed into a higher level map-matching filter for further corrections. Unlike common Particle Filter (PF) map-matching, map constraints are implicitly modeled by generating rasterized maps that function as a constant spatial prior in the designed filter, which makes the Bayesian estimation cycle non-recursive. Eventually, proposed map-matching algorithm does not require computationally expensive Monte Carlo simulation and wall crossing check steps of PF. Second major usage of the rasterized maps is to provide probabilities for a self-initialization method referred to as the Multiple Hypothesis Testing (MHT). Extracted scores update hypothesis probabilities in a dynamic manner and the hypothesis with the maximum probability gives the correct initial position and heading. Realistic pedestrian walks include room visits where map-matching is de-activated (as rasterized maps do not model the rooms) and consequently excessive positioning drifts occur. Another MHT approach exploiting the introduced maps further is designed to re-activate the map filter at strides that the pedestrian returns the hallways after room traversals. Subsequently, trajectories left behind inside the rooms are heuristically adjusted for the sake of consistency in the overall solution. Various experiments with different unknown initial conditions, longer distances and short/long room visits are conducted and representative results are shown to validate the performance of the developed methods. The experimental results show feasible trajectories with negligible return-to-start and stride errors.
Alper Yilmaz, Prof (Advisor)
Keith Redmill, Prof (Committee Member)
Charles Toth, Prof (Committee Member)
Janet Best, Prof (Other)
135 p.
Electrical Engineering; Engineering
pedestrian tracking; MEMS; inertial navigation system; INS; inertial measurement unit; IMU; building information model; BIM; map-matching; multiple hypothesis testing; Kalman Filter; Particle Filter; Bayesian Filtering; pedestrian INS, zero velocity; ZUPT

Recommended Citations

Citations

  • Koroglu, M. T. (2020). Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298

    APA Style (7th edition)

  • Koroglu, Muhammed. Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching. 2020. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298.

    MLA Style (8th edition)

  • Koroglu, Muhammed. "Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching." Doctoral dissertation, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298

    Chicago Manual of Style (17th edition)

osu1577135195323298
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