Particle Filtering with Propagation Delayed Measurements

Date

2010-03-13

Author

Orguner, Umut

Metadata

Show full item record

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

Item Usage Stats

200
views

0
downloads

This paper investigates the problem of propagation delayed measurements in a particle filtering scenario. Based on implicit constraints specified by target dynamics and physics rules of signal propagation, authors apply the ideas that were first proposed in their previous work to the case of particle filters. Unlike the deterministic sampling based approach called propagation delayed measurement filter (PDMF) in their previous work, the new algorithm proposed here (called as PDM particle filter (PDM-PF)) has the potential to be used with general nonlinear models. This advantage and the estimation results of PDM-PF are illustrated in a challenging target tracking scenario by making comparisons to PDMF along with some other alternative particle filters.

Subject Keywords

Filtering, Propagation delay, Particle measurements, Particle filters, Delay estimation, Delay effects, Kinematics, Electric variables measurement, Physics, Sampling methods

URI

https://hdl.handle.net/11511/46388

DOI

https://doi.org/10.1109/aero.2010.5446679

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Entropy Calculation in Particle Filters Orguner, Umut (2009-04-11) This paper presents a differential entropy calculation method to be used for particle mixtures in particle filters. First it is shown that the exact differential entropy of particle mixtures is minus infinity and therefore useless in practice. The disadvantage of using discrete entropy formulation instead of differential entropy is also explained. Unlike the kernel-based methods in the literature, a Bayes rule based approximation is then proposed. The performance of the algorithm is illustrated on a basic G...
Pseudo-Linear Kalman Filter for Attitude Estimation of a Spinning Nanosatellite Söken, Halil Ersin; Kallio, Esa; Visala, Arto; Selkainaho, Jorma (null; 2017-06-09) This paper presents the pseudo-linear estimation approach to the high-rate spinning small spacecraft attitude estimation problem. The sensor suit utilised in the presented approach uses gyro, magnetometer and sun-sensor measurements. The presented estimation technique has been designed particularly for the problem of attitude determination during the Aalto-1 nanosatellite's Plasma Brake Experiment (PBE). The design of the PBE demands the satellite to be spun up to 200 deg/s for deploying the tether by the u...
Delay and Peak-Age Violation Probability in Short-Packet Transmissions Devassy, Rahul; Durisi, Giuseppe; Ferrante, Guido Carlo; Simeone, Osvaldo; Uysal, Elif (2018-06-22) This paper investigates the distribution of delay and peak age of information in a communication system where packets, generated according to an independent and identically distributed Bernoulli process, are placed in a single-server queue with first-come first-served discipline and transmitted over an additive white Gaussian noise (AWGN) channel. When a packet is correctly decoded, the sender receives an instantaneous error-free positive acknowledgment, upon which it removes the packet from the buffer. In ...
Component extraction analysis of multivariate time series Akman, I; DeGooijer, JG (1996-05-01) A method for modelling several observed parallel time series is proposed. The method involves seeking possible common underlying pure AR and MA components in the series. The common components are forced to be mutually uncorrelated so that univariate time series modelling and forecasting techniques can be applied. The proposed method is shown to be a useful addition to the time series analyst's toolkit, if common sources of variation in multivariate data need to be quickly identified.
Online EM algorithm for joint state and mixture measurement noise estimation Zhao, Yuxin; Yin, Feng; Gunnarsson, Fredrik; Amirijoo, Mehdi; Özkan, Emre; Gustafsson, Fredrik (null; 2012-07-09) In this study, we aim to estimate the unknown multimodal measurement noise distribution of nonlinear state space models. The unknown noise distribution is modeled as a mixture of exponential family of distributions. We use the ExpectationMaximization (EM) method in order to jointly estimate the unknown parameters as well as the states. The online version of the EM algorithm is implemented by using particle filtering techniques. The resulting algorithm is a noise adaptive particle filter which is applicable ...

Citation Formats

U. Orguner, “Particle Filtering with Propagation Delayed Measurements,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46388.