Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

A novel prediction method for robust beating heart tracking is proposed. The dual time-varying Fourier series is used to model the heart motion. The frequency parameters and Fourier coefficients in the model are estimated respectively by using a dual Kalman filter scheme. The instantaneous frequencies of breathing and heartbeat motion are measured online from the 3D trajectory of the point of interest using an orthogonal decomposition algorithm. The proposed method is evaluated based on both the simulated signals and the real motion signals, which are measured from the videos recorded using the da Vinci surgical system.

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC.2015.7319485DOI Listing

Publication Analysis

Top Keywords

dual kalman
8
kalman filter
8
robust beating
8
beating heart
8
heart tracking
8
motion
4
motion prediction
4
prediction dual
4
filter robust
4
tracking novel
4

Similar Publications

Fiber Optic Gyro Random Error Suppression Based on Dual Adaptive Kalman Filter.

Micromachines (Basel)

July 2025

Institute of Optics and Electronics, School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China.

The random error of fiber optic gyros is a critical factor affecting their measurement accuracy. However, the statistical characteristics of these errors exhibit time-varying properties, which degrade model fidelity and consequently impair the performance of random error suppression algorithms. To address these issues, this study first proposes a recursive dynamic Allan variance calculation method that effectively mitigates the poor real-time performance and spectral leakage inherent in conventional dynamic Allan variance techniques.

View Article and Find Full Text PDF

In industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi-sensor transmissions introduce significant interference and latency, impairing the KF's ability to continuously obtain reliable observations. Meanwhile, existing remote state estimation systems typically rely on oversimplified wireless communication models, unable to adequately handle the dynamics and interference in realistic network scenarios.

View Article and Find Full Text PDF

To accurately determine the relationships among sub-aperture cameras in four-aperture infrared bionic compound eye systems and enhance the target-positioning accuracy, addressing the issues that traditional single-aperture infrared cameras suffer from a limited imaging field-of-view, and multi-aperture camera systems fail to utilize all camera combination and exhibit slow target-positioning convergence speed due to neglecting pose differences of sub-eye cameras, static and dynamic target images were captured using the system. Target-positioning methods were then designed and investigated. Spatial and pose weights were assigned based on the spatial positions and rotation angles of the cameras.

View Article and Find Full Text PDF

A robust two-stage Kalman filter (KF) scheme is proposed to address the challenges of Doppler shifts in LEO-LEO coherent laser inter-satellite links (LISLs). A dual-satellite LEO-LEO LISL model is developed through rotational normalization to analyze the necessity of compensating for abrupt shifts in link establishment and gradual shifts in link maintenance. The proposed scheme utilizes a two-stage KF with two sets of specific parameter states to realize cascaded rough and fine compensations.

View Article and Find Full Text PDF

To address the limitations of single-sensor systems in environmental perception, such as the difficulty in comprehensively capturing complex environmental information and insufficient detection accuracy and robustness in dynamic environments, this study proposes a distance measurement method based on the fusion of millimeter-wave (MMW) radar and monocular camera. Initially, a monocular ranging model was constructed based on object detection algorithms. Subsequently, the pixel-distance joint dual-constraint matching algorithm is employed to accomplish cross-modal matching between the MMW radar and the monocular camera.

View Article and Find Full Text PDF