A New Observation Model to Solve Vehicle Localization Problem

2006 ◽  
Author(s):  
Nga-viet Nguyen ◽  
Kiseon Kim ◽  
Vladimir Shin
Keyword(s):  
Observation Model ◽  
Localization Problem ◽  
Vehicle Localization

scholarly journals Robust Interval-Based Localization Algorithms for Mobile Sensor Networks

2011 ◽  
Vol 8 (1) ◽  
pp. 303895 ◽  
Author(s):  
Farah Mourad ◽  
Hichem Snoussi ◽  
Michel Kieffer ◽  
Cédric Richard
Keyword(s):  
Sensor Networks ◽  
Mobile Sensor Networks ◽  
Observation Model ◽  
Localization Problem ◽  
Mobile Sensor ◽  
Localization Algorithms ◽  
Set Membership ◽  
Correct Location

This paper considers the localization problem in mobile sensor networks. Such a problem is a challenging task, especially when measurements exchanged between sensors may contain outliers, that is, data not matching the observation model. This paper proposes two algorithms robust to outliers. These algorithms perform a set-membership estimation, where only the maximal number of outliers is required to be known. Using these algorithms, estimates consist of sets of boxes whose union surely contains the correct location of the sensor, provided that the considered hypotheses are satisfied. This paper proposes as well a technique for evaluating the number of outliers to be robust to. In order to corroborate the efficiency of both algorithms, a comparison of their performances is performed in simulations using Matlab.

scholarly journals An Unscented Rauch-Tung-Striebel Smoother for a Vehicle Localization Problem

2011 ◽  
Vol 15 (7) ◽  
pp. 860-868 ◽  
Author(s):  
Saifudin Razali ◽  
◽  
Keigo Watanabe ◽  
Shoichi Maeyama ◽  
Kiyotaka Izumi ◽  
...  
Keyword(s):  
Unscented Kalman Filter ◽  
Localization Problem ◽  
Unscented Transformation ◽  
Vehicle Localization ◽  
Deterministic Sampling ◽  
Derivative Free ◽  
Nonlinear Smoothing ◽  
Estimation Problems ◽  
A New Technique ◽  
Filtering Problem

The Unscented Kalman Filter (UKF) has become relatively a new technique used in a number of nonlinear estimation problems to overcome the limitation of Taylor series linearization. It uses a deterministic sampling approach known as sigma points to propagate nonlinear systems and has been discussed in many literature. However, a nonlinear smoothing problem has received less attention than the filtering problem. Therefore, in this article an unscented smoother based on Rauch-Tung-Striebel formis examined for discretetime dynamic systems. It has advantages available in unscented transformation over approximation by Taylor expansion as well as its benefit in derivative free. To show the effectiveness of the proposed method, the unscented smoother is implemented and evaluated through a vehicle localization problem.

scholarly journals Localization of a Vehicle: A Dynamic Interval Constraint Satisfaction Problem-Based Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Kangni Kueviakoe ◽  
Zhan Wang ◽  
Alain Lambert ◽  
Emmanuelle Frenoux ◽  
Philippe Tarroux
Keyword(s):  
Kalman Filter ◽  
Bayesian Methods ◽  
Constraint Satisfaction ◽  
Measurement Errors ◽  
Constraint Satisfaction Problem ◽  
Constraint Propagation ◽  
Gps Receiver ◽  
Localization Problem ◽  
Vehicle Localization ◽  
Interval Constraint

This paper introduces a new interval constraint propagation (ICP) approach dealing with the real-time vehicle localization problem. Bayesian methods like extended Kalman filter (EKF) are classically used to achieve vehicle localization. ICP is an alternative which provides guaranteed localization results rather than probabilities. Our approach assumes that all models and measurement errors are bounded within known limits without any other hypotheses on the probability distribution. The proposed algorithm uses a low-level consistency algorithm and has been validated with an outdoor vehicle equipped with a GPS receiver, a gyro, and odometers. Results have been compared to EKF and other ICP methods such as hull consistency (HC4) and 3-bound (3B) algorithms. Both consistencies of EKF and our algorithm have been experimentally studied.

Sign in / Sign up

Export Citation Format

Share Document