Joint Self-Calibration of Receiver Geometry, Timing, and Target Positions for Multistatic Radar Autofocus

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Computational Imaging

Background:

• Near-field multistatic radar imaging relies on precise knowledge of transmitter, receiver, and target positions, along with accurate receiver time references.
• In practical scenarios, these parameters are known only approximately, introducing errors due to small survey, synchronization, and target localization inaccuracies.
• These approximations degrade the quality and accuracy of the final radar image.

Purpose of the Study:

• To develop a joint self-calibration framework for near-field multistatic radar imaging.
• To estimate small corrections to receiver positions, receiver clock biases, and target positions using bistatic echo delays.
• To directly link these corrections to image sharpness, rather than solely to parameter accuracy.

Main Methods:

• Derivation of a linearized observation model relating delay residuals to corrections in receiver positions, clock biases, and target positions.
• Development of a regularized (maximum a posteriori) weighted least-squares estimator to separate measurement noise from prior parameter uncertainty.
• Progressive characterization of estimator identifiability with increasing numbers of anchors (transmitter, targets, receivers) and reformulation of calibration objective in terms of coherent multistatic image sharpness.

Main Results:

• Identifiability of the self-calibration framework transitions from a 3D rotational null space with one anchor to complete ambiguity removal with three anchors in general position.
• The dual problem of localizing an unknown transmitter from known anchors reveals uncorrectable ambiguities for collinear (continuous) and coplanar (discrete) anchor geometries.
• Numerical experiments validate identifiability transitions, demonstrate quadratic convergence of the estimator, and show improved image sharpness and target resolvability after self-calibration.

Conclusions:

• The proposed joint self-calibration framework effectively estimates and corrects for positional and timing errors in near-field multistatic radar systems.
• The method directly optimizes image sharpness, leading to improved imaging performance.
• The study provides a comprehensive analysis of identifiability and ambiguity in both self-calibration and transmitter localization problems within multistatic radar configurations.