Conference Paper

State estimation with delayed measurements considering uncertainty of time delay

Dept. of Mech. Eng., Pohang Univ. of Sci. & Technol., Pohang, South Korea
DOI: 10.1109/ROBOT.2009.5152887 Conference: Robotics and Automation, 2009. ICRA '09. IEEE International Conference on
Source: IEEE Xplore


State estimation problem with time delayed measurements is addressed. In dynamic system with noise, after taking measurements, it often requires some time until that is available in a filter. A filter not considering this time delay cannot be used since a current measurement is related with a past state. These delayed measurements problem is solved with augmented state Kalman filter, and uncertainty of the delayed time is also resolved based on the probability distribution of the delay. The proposed method is analyzed by a simple example, and its consistency is verified.

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    • "One may refer to [11] for more details. Also, a multi-sensor system is prone to measurement delays caused by context switches and scheduling decisions [3] [4]. These delays cannot be neglected since they might have considerable adverse effects on the results. "
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    ABSTRACT: Many real-time embedded systems are sensitive to both the accuracy and timeliness of job results. In this letter, two sources of inaccuracy are considered for such systems: 1) input data noise (IDN) due to the environmental transient noises, and 2) age of data (AD) related to the time of capturing data, which may depend on the length of time between capturing and using the input data. Thus, in the presence of one or more jobs in the system, some tradeoffs are needed among capturing data with an appropriate IDN when the environment is less noisy, reducing AD, and respecting the timeliness of jobs. Our emphasis in the current study is to model firm real-time jobs having some thresholds for the inaccuracy and handle the aforementioned tradeoff by the system scheduler. An online accuracy-aware real-time scheduler is also proposed and evaluated.
    IEEE embedded systems letters 09/2012; 4(3):61-64. DOI:10.1109/LES.2012.2195294
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    • "where and are some constants related to the control process. Kalman filters are widely used in avionics [3], industrial systems [7] [9] and robotics [23]. These are used to estimate the state of a process by taking discrete measurement which is influenced by Gaussian noises [23] [24]. "
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    ABSTRACT: Timeliness and accuracy are two major concerns in many real-time embedded systems working in dynamic environments. It has been emphasized in the literature that in various real-time applications such as control systems and Kalman filters, delay is one main source of inaccuracy in the system. In this paper, we present a solution based on scheduling algorithms for the problem of inaccuracy in such systems. To this aim, first an accuracy model is introduced for systems which their accuracy is influenced by sampling and I/O delays. Then an algorithm called adjacency trade is presented to improve system accuracy while maintaining its timeliness. This algorithm follows an iterative approach and can be applied to each priority-based scheduling algorithm with no intervention in respecting the deadlines. Finally, through various simulation experiments, the effectiveness of this algorithm is examined against some algorithms in the literature.
    Embedded and Real-Time Computing Systems and Applications (RTCSA), 2012 IEEE 18th International Conference on; 01/2012
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    • "Treatments of unknown delays found in the literature can be divided into two cases. First, the delays are unknown and random with a short (zero) correlation time (time jitter), see for example [1] [2]. Second, the delays are unknown but static or varying slowly. "
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    ABSTRACT: Sensor fusion algorithms often assume perfect time synchronization of the sensor clocks. In a practical sensor-actuator setup this is often difficult to achieve which in turn can give rise to systematic errors in the sensor fusion. In this article we suggest how the effect of the synchronization error from an unknown static or slowly varying measurement time-delays in a nonlinear state space system can be handled by linearizing the measurement equation in time. Based on the linearization an augmented system is constructed from which the system states and the delays can be jointly estimated. Expressions for the system, measurement, and covariance matrices of the augmented system are derived. Finally, the feasibility of the suggested approach is demonstrated by an example and a Monte-Carlo simulation.
    Information Sciences Signal Processing and their Applications (ISSPA), 2010 10th International Conference on; 06/2010
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