Selfcalibrating

Self-calibrating refers to systems or processes that estimate and adjust their own calibration parameters during or after operation, without relying on external references. The approach uses data redundancy, known physical models, or prior information to separate the true signal from instrumental or environmental effects. It is commonly applied when calibration conditions change over time or are difficult to maintain manually.

In astronomy and radio interferometry, self-calibration iteratively solves for complex gains and phase errors of detectors using the observed data itself, aided by redundant measurements and closure relations. The procedure often alternates between updating a source model and refining calibration parameters, improving image fidelity in the presence of atmospheric or instrumental drift.

In imaging, photography, and computer vision, self-calibration can correct sensor nonuniformities, lens distortion, or color biases by exploiting scenes with redundancy or by integrating auxiliary data. In robotics and metrology, it enables online estimation of sensor biases and kinematic parameters to maintain accuracy under changing conditions.

Typical methods involve joint or alternating estimation of the true signal and calibration factors, using optimization techniques such as least squares, expectation-maximization, or Bayesian inference. Regularization and priors help avoid degeneracies where signal and calibration effects trade off against each other.

Advantages include reduced downtime, adaptability to changing conditions, and potential long-term accuracy gains. Limitations involve the need for sufficiently rich data to constrain parameters, possible ambiguities between signal and calibration, and higher computational demands.

See also calibration, blind calibration, adaptive sensing.