Non-parametric sensor bias estimation using gaussian process

Date

2020

Author

Deniz, Ufuk Mehmet

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

201
views

0
downloads

In this work, we propose to use Gaussian Process (GP) regression techniques to estimate possible non-parametric sensor biases in a multi-sensor environment. Using multi-sensor for tracking applications makes a system more reliable. Combining information acquired from multiple sensors has several difficulties. In a multi-sensor environment, it is difficult to assure that all sensors are registered and calibrated perfectly during operation. When the error between different sensors exceeds tolerable limits, multi-sensor tracking might become a problem. In this study, we assume that the biases between local agents stem from unidentified and complicated sources, which would make parametric modeling very difficult, if not impossible. GPs are used to model the unknown bias that may occur between the measurements/estimates of two different sensors. The model does not require a parametric model for the bias error structure. Since standard GP becomes impractical in reality because of the computational burden of large data sets, a sparse approximation of GP is implemented to scale down the computational complexity. Estimated biases can later be used to perform association or fuse tracks among sensors more accurately. We present a review of three different data fusion architectures so as to combine the data from multiple sensors. We compare the tracking performance of the data fusion architectures to explore the one which provides the best accuracy in our study.

Subject Keywords

Engineering., Keywords: Target tracking, Sensor Bias Estimation, Sensor Registration, Non-parametric, Gaussian Process

URI

http://etd.lib.metu.edu.tr/upload/12625095/index.pdf
https://hdl.handle.net/11511/45542

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

New models and inference techniques for Gaussian process-based extended object tracking Kumru, Murat; Özkan, Emre; Department of Electrical and Electronics Engineering (2022-9-09) In this thesis, we consider the problem of tracking dynamic objects with unknown shapes using point cloud measurements generated by, e.g., lidars, radars, and depth cameras. The point measurements do not only convey information about the object pose, i.e., position and orientation, but they also naturally reveal the characteristics of its latent extent. Aiming to harness the full potential of the available information, we investigate the Gaussian process-based extended object tracking (GPEOT) framework. W...
Radar target detection in non-gaussian clutter Doyuran, Ülkü; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2007) In this study, novel methods for high-resolution radar target detection in non-Gaussian clutter environment are proposed. In solution of the problem, two approaches are used: Non-coherent detection that operates on the envelope-detected signal for thresholding and coherent detection that performs clutter suppression, Doppler processing and thresholding at the same time. The proposed non-coherent detectors, which are designed to operate in non-Gaussian and range-heterogeneous clutter, yield higher performanc...
Applications of estimation techniques on genetic and other types of data Aslan, Murat; Akkaya, Ayşen; Department of Statistics (2003) The parameters of genetic and other types of data, particularly with small samples, are estimated by using method of moments, least squares, minimum chi- square, maximum likelihood and modified maximum likelihood estimation methods. These methods are also compared in terms of their efficiencies and robustness property.
Consensus clustering of time series data Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014) In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Performance of a non-linear adaptive beamformer algorithm for signal-of-interest extraction / Oğuz, Özkan; Tuncer, Temel Engin; Department of Electrical and Electronics Engineering (2015) In this thesis a non-linear adaptive beamforming technique, Adaptive Projections Subgradient Method [1] (APSM) is considered. This method uses projections over convex sets in Reproducing Kernel Hilbert Space. Main advantage of this method is observed if the signal-of-interest is due to digital modulation and when there are more jammers than the number of antennas. The performance of this non-linear beamforming technique is compared with well-known methods including Minimum Variance Distortionless Response [...

Citation Formats

U. M. Deniz, “Non-parametric sensor bias estimation using gaussian process,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2020.