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1

Brett, Daniel J. Orbital parameter estimation using an extended Kalman filter. Monterey, Calif: Naval Postgraduate School, 1992.

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2

Civera, Javier, Andrew J. Davison, and José María Martínez Montiel. Structure from Motion using the Extended Kalman Filter. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24834-4.

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3

Üner, Muhittin. Frequency, amplitude, and phase tracking of nonsinusoidal signal in noise with extended Kalman filter. Monterey, Calif: Naval Postgraduate School, 1991.

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4

Taylor, Michael Eric. System identification and control of an Arleigh Burke Class Destroyer using an extended Kalman Filter. Springfield, Va: Available from National Technical Information Service, 2000.

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5

Galinis, William J. Fixed interval smoothing algorithm for an extended Kalman filter for over-the-horizon ship tracking. Monterey, California: Naval Postgraduate School, 1989.

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6

Civera, Javier, Andrew J. Davison, and José María Martínez Montiel. Structure from Motion using the Extended Kalman Filter. Springer, 2011.

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7

Civera, Javier, Andrew J. Davison, and José María Martínez Montiel. Structure from Motion using the Extended Kalman Filter. Springer, 2014.

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8

Structure From Motion Using The Extended Kalman Filter. Springer, 2011.

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9

An Extended Kalman Filter for Quaternion-Based Attitude Estimation. Storming Media, 2000.

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10

D, Andrisani, and Dryden Flight Research Facility, eds. Estimating short-period dynamics using an extended Kalman filter. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1990.

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11

D, Andrisani, and Dryden Flight Research Facility, eds. Estimating short-period dynamics using an extended Kalman filter. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1990.

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12

D, Andrisani, and Dryden Flight Research Facility, eds. Estimating short-period dynamics using an extended Kalman filter. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1990.

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13

Moorman, Martin J. Analysis of bias in the extended kalman filter with application to passive target tracking. 1993.

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14

Vaez-Zadeh, Sadegh. Rotor Position and Speed Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0006.

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The ultimate importance of rotor position and speed information in permanent magnet synchronous (PMS) machines control, and the industry interest to the rotor and speed sensorless systems as a cost-saving and practical alternative to the motor control with mechanical sensors are emphasized. Major position and speed estimation schemes are then presented in detail. These are the: back electromotive force (EMF)-based method; flux linkage method; hypothesis rotor position method; saliency-based method, including high frequency signal injection and inverter switching harmonics schemes; and finally, the observer-based method, including state observer and extended Kalman filter-based schemes. Each scheme was discussed by presenting the corresponding fundamental principles, followed by the appropriate motor model, estimation procedure, and the implementation. Demanding criteria such as accuracy, robustness, swiftness, and capability of working over the entire range of motor operation are discussed with each method.
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15

Vaez-Zadeh, Sadegh. Parameter Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0007.

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Abstract:
In this chapter, the estimation of permanent magnetic synchronous (PMS) motor parameters, including stator winding resistance, motor inductances, and magnitude of permanent magnet flux linage, is presented in two main categories, i.e., offline and online. Several offline schemes, including DC and AC standstill tests, no-load test, load test, and vector control schemes, are presented for estimation of all the motor parameters. Major online schemes used in the estimation of PMS motor parameters are also presented in this chapter. They include closed-loop observer-based estimation, model reference adaptive system (MRAS)-based estimation, recursive least-squares (RLS) estimation, and extended Kalman filter scheme. The online schemes take into account the motor parameter variations during motor operation. The motor model, estimation procedure, and the connection of estimation systems to the motor control system are discussed for each parameter estimation scheme.
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