A closed loop ML algorithm for phase aberration correction in phased array imaging systems. I. Algorithm synthesis...

Area of Science:

• Signal Processing
• Coherent Imaging Systems
• Array Signal Processing

Background:

• Phased array coherent imaging systems are susceptible to phase aberrations.
• Accurate phase aberration correction is crucial for image quality.
• Existing methods often rely on correlation functions or adjacent element phase differences, requiring integration.

Purpose of the Study:

• To introduce a new closed loop algorithm for correcting phase aberrations in phased array coherent imaging.
• To utilize phase estimates directly from measured data, avoiding correlation functions.
• To enable real-time implementation and tracking of time-varying aberrations.

Main Methods:

• A closed loop algorithm based on maximum likelihood theory is presented.
• Phase estimates are directly obtained from measured data.
• Phase differences are estimated with respect to a common reference, eliminating integration steps.

Main Results:

• The algorithm effectively corrects phase aberrations in phased array coherent imaging systems.
• Direct use of measured data simplifies the process and avoids correlation function estimation.
• Real-time implementation is achieved, with data processed at input rate.
• The closed loop nature allows tracking of time-varying phase aberrations.

Conclusions:

• The proposed algorithm offers an efficient and real-time solution for phase aberration correction.
• It simplifies the correction process by directly using measured data and a common reference.
• The algorithm's ability to track temporal variations enhances its applicability in dynamic imaging scenarios.