State estimation under nonlinear state inequality constraints. A tracking application

Sircoulomb, Vincent; Hoblos, Ghaleb; Chafouk, Houcine; Ragot, José

doi:10.1109/med.2008.4602024

Cited by 29 publications

(14 citation statements)

References 16 publications

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“…Simon 2009 for a recent survey). Many of these approaches (like, e.g., the modified gain approach of Sircoulomb et al 2008) require an analytical expression for the constraints which is not available, as mentioned, in our case. A numerical procedure, which could be adapted to our case, is the one described in Cai et al (1995), where a constrained estimation problem having many similarities with the one addressed in this paper is considered.…”

Section: Related Workmentioning

confidence: 99%

Constrained and quantized Kalman filtering for an RFID robot localization problem

2010

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In this paper a global localization problem of a robot moving in a known environment is considered. The environment is equipped with a relatively sparse set of passive RFID (Radio Frequency IDentification) tags. The robot can detect the presence of the tags when traveling in their proximity and combines this information with the one given by other sensors (e.g. odometry). The RFID measurements are characterized by a highly non Gaussian noise: for this reason in the literature Particle Filter (PF) methods have often been used to fuse these data with the measurements coming from other sensors. In this paper a different approach is pursued, based on the observation that RFID readings can be considered as noisy quantized measurements of the pose of the robot or as noisy dynamic constraints on the pose itself. This allows to exploit the rich literature on Kalman quantized filtering or Kalman constrained estimation, to realize reliable methods with a satisfactory performance which require a computational time significantly lower with respect to the one needed by a PF. Simulative and experimental results will be reported to illustrate the proposed methods.

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Section: Related Workmentioning

confidence: 99%

Constrained and quantized Kalman filtering for an RFID robot localization problem

2010

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“…A nonlinear state inequality constraint can be transformed into a nonlinear state equality constraint [18], since two cases can occur:…”

Section: A Constrained Kalman Filteringmentioning

confidence: 99%

Constrained Kalman filtering for indoor localization of transport vehicles using floor-installed HF RFID transponders

Röhrig

Hes

Künemund

2015

2015 IEEE International Conference on RFID (RFID)

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Localization of transport vehicles is an important issue for many intralogistics applications. The paper presents an inexpensive solution for indoor localization of vehicles. Global localization is realized by detection of RFID transponders, which are integrated in the floor. The paper presents a novel algorithm for fusing RFID readings with odometry using Constraint Kalman filtering. The paper presents experimental results with a Mecanum based omnidirectional vehicle on a NaviFloor ® installation, which includes passive HF RFID transponders. The experiments show that the proposed Constraint Kalman filter provides a similar localization accuracy compared to a Particle filter but with much lower computational expense.

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“…To estimate the state and this estimate satisfies (37), the estimation method with projection approach is used [10]. Let us denote byˆthe state estimated at time by an unconstrained estimator (i.e.…”

Section: Input and State Estimationmentioning

confidence: 99%

Actuators and sensors fault diagnosis with dynamic, state-space neural networks

Luzar

Czajkowski

Witczak

et al. 2012

2012 17th International Conference on Methods &Amp; Models in Automation &Amp; Robotics (MMAR)

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In this paper, the actuators and sensors fault detection and localization using a system model is considered. To obtain the system model, the neural network modeling is used. The artificial feedforward neural network with dynamic neurons in the state-space representation is proposed. To estimate the neural network parameters, the Adaptive Random Search algorithm with projection is used. To identify, which of actuators or sensors is faulty, the system input estimator is proposed. The input and output residuals being the difference between the system input and output and its estimates are used to detect and isolate the faults. The final part of the paper presents an application study, which clearly confirms the effectiveness of the proposed approach.

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State estimation under nonlinear state inequality constraints. A tracking application

Cited by 29 publications

References 16 publications

Constrained and quantized Kalman filtering for an RFID robot localization problem

Constrained and quantized Kalman filtering for an RFID robot localization problem

Constrained Kalman filtering for indoor localization of transport vehicles using floor-installed HF RFID transponders

Actuators and sensors fault diagnosis with dynamic, state-space neural networks

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