Reduced complexity dynamic programming solution for Kalman filtering of linear discrete time descriptor systems

Al-Matouq, Ali; Vincent, Tyrone L.

doi:10.1109/acc.2013.6579860

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…However, intelligent management of the inequality constraints can be achieved using similar techniques to the ones presented in this paper. This and other problems involving 1 and Huber penalties will be subjects for future studies.…”

Section: Resultsmentioning

confidence: 99%

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Multiple window moving horizon estimation

Al-Matouq

Vincent

2015

Automatica

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Long horizon lengths in Moving Horizon Estimation are desirable to reach the performance limits of the full information estimator. However, the conventional MHE technique suffers from a number of deficiencies in this respect. First, the problem complexity scales at least linearly with the horizon length selected, which restrains from selecting long horizons if computational limitations are present. Second, there is no monitoring of constraint activity/inactivity which results in conducting redundant constrained minimizations even when no constraints are active. In this study we develop a Multiple-Window Moving Horizon Estimation strategy (MW-MHE) that exploits constraint inactivity to reduce the problem size in long horizon estimation problems. The arrival cost is approximated using the unconstrained full information estimator arrival cost to guarantee stability of the technique. A new horizon length selection criteria is developed based on maximum sensitivity between remote states in time. The development will be in terms of general causal descriptor systems, which includes the standard state space representation as a special case. The potential of the new estimation algorithm will be demonstrated with an example showing a significant reduction in both computation time and numerical errors compared to conventional MHE.

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Section: Resultsmentioning

confidence: 99%

“…Descriptor Kalman recursions were previously derived in [12] and [20] with the most general case derived in [25]. Descriptor Kalman recursions for the case when mixed stochastic and deterministic components are considered can be found in [1].…”

Section: Theoremmentioning

confidence: 99%

Multiple window moving horizon estimation

Al-Matouq

Vincent

2015

Automatica

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“…Unless coupled, the larger system can be decomposed into the sum of its uncoupled subsystems, enabling major optimizations for computational complexity [10]. …”

Section: Technical Backgroundmentioning

confidence: 99%

A Study about Kalman Filters Applied to Embedded Sensors

Valade

Acco

Grabolosa³

et al. 2017

Sensors

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Over the last decade, smart sensors have grown in complexity and can now handle multiple measurement sources. This work establishes a methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion. A driving constraint being production cost and power consumption, this methodology focuses on algorithmic complexity while meeting real-time constraints and improving both precision and reliability despite low power processors limitations. Consequently, processing time available for other tasks is maximized. The known problem of estimating a 2D orientation using an inertial measurement unit with automatic gyroscope bias compensation will be used to illustrate the proposed methodology applied to a low power STM32L053 microcontroller. This application shows promising results with a processing time of 1.18 ms at 32 MHz with a 3.8% CPU usage due to the computation at a 26 Hz measurement and estimation rate.

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Reduced complexity dynamic programming solution for Kalman filtering of linear discrete time descriptor systems

Cited by 2 publications

References 18 publications

Multiple window moving horizon estimation

Multiple window moving horizon estimation

A Study about Kalman Filters Applied to Embedded Sensors

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