Frequency tracking of nonsinusoidal periodic signals in noise

Parker, Philip J.; Anderson, Brian D. O.

doi:10.1016/0165-1684(90)90124-h

Cited by 76 publications

(57 citation statements)

References 9 publications

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“…A demodulator follows by constructing an EKF for an augmented state space system that includes the multiple signal components [2]. The signals may be modeled by (1) This form suggests where An examination of the EKF asymptotic error covariance for the above problem under low measurement noise conditions suggests a structure for the fake ARE solution, namely, diag , where in which .…”

Section: B Application To Signal Demodulationmentioning

confidence: 99%

“…The nonlinear state estimation problem is formulated along similar lines to the application of the EKF described in [1] and [2]. The EKF uses the nonlinear plant update and measurement function to compute a prediction error, which is then multiplied by the Kalman gain matrix derived from the linearised system and added to the state estimate.…”

Section: Introductionmentioning

confidence: 99%

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The use of fake algebraic riccati equations for co-channel demodulation

Einicke¹,

White

Bitmead

2003

IEEE Trans. Signal Process.

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Abstract-This paper describes a method for nonlinear filtering based on an adaptive observer, which guarantees the local stability of the linearized error system. A fake algebraic Riccati equation is employed in the calculation of the filter gain. The design procedure attempts to produce a stable filter at the expense of optimality. This contrasts with the extended Kalman filter (EKF), which attempts to preserve optimality via its linearization procedure, at the expense of stability. A passivity approach is applied to deduce stability conditions for the filter error system. The performance is compared with an EKF for a co-channel frequency demodulation application.

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Section: B Application To Signal Demodulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The use of fake algebraic riccati equations for co-channel demodulation

Einicke¹,

White

Bitmead

2003

IEEE Trans. Signal Process.

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“…This parameterization was introduced in [10] and shown to be a robust model for mitral annulus tracking with m = 8 harmonics [7].…”

Section: Quasiperiodic Predictive Filter: Extended Kalman Filtermentioning

confidence: 99%

3D Ultrasound-Guided Motion Compensation System for Beating Heart Mitral Valve Repair

Yuen

Kesner

Vasilyev

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. Beating heart intracardiac procedures promise significant benefits for patients, however, the fast motion of the heart poses serious challenges to surgeons. We present a new 3D ultrasound-guided motion (3DUS) compensation system that synchronizes instrument motion with the heart. The system utilizes the fact that the motion of some intracardiac structures, including the mitral valve annulus, is largely constrained to translation along one axis. This allows the development of a real-time 3DUS tissue tracker which we integrate with a 1 degree-of-freedom actuated surgical instrument, real-time 3DUS instrument tracker, and predictive filter to devise a system with synchronization accuracy of 1.8 mm RMSE. User studies involving the deployment of surgical anchors in a simulated mitral annuloplasty procedure demonstrate that the system increases success rates by over 100%. Furthermore, it enables more careful anchor deployment by reducing forces to the tissue by 50% while allowing instruments to remain in contact with the tissue for longer periods.

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“…Since the resulting estimation problem is nonlinear and highdimensional, approximate filtering methods have to be used. When the measurement noise is sufficiently white, and the SNR is sufficiently high, linearization-based filters such as the extended Kalman filter, [7], can be used, as in [8][9][10]. When the SNR is worse, the problem may become multimodal and unsuited to the Gaussian approximations inherent in Kalman filtering.…”

Section: Introductionmentioning

confidence: 99%

On frequency tracking in harmonic acoustic signals

Lindfors

Hendeby

Gustafsson

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-Acoustic frequency tracking of a harmonic signal with continuously varying frequency is considered. The RaoBlackwellized point mass filter (RBPMF), previously proposed by the authors for mechanical vibration tracking, is applied to the problem. The RBPMF is compared with two periodogram-based methods, and the similarities and differences between them are explained. Both experimental and simulation results in a Doppler frequency tracking scenario are presented, and the results show that the RBPMF can have significantly less estimation error than the competing methods.

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Frequency tracking of nonsinusoidal periodic signals in noise

Cited by 76 publications

References 9 publications

The use of fake algebraic riccati equations for co-channel demodulation

The use of fake algebraic riccati equations for co-channel demodulation

3D Ultrasound-Guided Motion Compensation System for Beating Heart Mitral Valve Repair

On frequency tracking in harmonic acoustic signals

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