State Estimation for a Class of Non-Uniform Sampling Systems with Missing Measurements

Area of Science:

• Control Systems Engineering
• Signal Processing

Background:

• State estimation is crucial for systems with non-uniform sampling and missing data.
• Existing methods often face computational challenges with increased measurement frequency.

Purpose of the Study:

• To develop an efficient state estimation algorithm for systems with randomly missing measurements.
• To provide state estimates at both uniform update and random sampling points.
• To unify optimal estimation for single and multi-sensor configurations.

Main Methods:

• A new state model was developed to capture dynamics at measurement sampling points.
• An innovation analysis approach was used to create a non-augmented state estimator.
• A distributed suboptimal fusion estimator using covariance intersection was proposed for multi-sensor systems.

Main Results:

• The proposed non-augmented estimator reduces computational burden compared to augmented methods.
• The estimator provides accurate state estimates at both state update and measurement sampling points.
• The distributed fusion estimator enhances reliability for multi-sensor systems.

Conclusions:

• The developed algorithms effectively address state estimation challenges in non-uniform sampling systems with missing measurements.
• The proposed methods offer computational efficiency and improved reliability for various system configurations.