Radar Parameter Calibration Without Reference Targets
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Solution Overview
Problem
Existing radar calibration methods require reference objects or specific radar scenes, making them unreliable in the presence of moving targets and limiting their applicability to certain types of radar scenes.
Innovation Solution
A method and device for calibrating radar parameters without a reference object, using a parameter-dependent representation of radar scene measurements and minimizing the nuclear norm to estimate parameters, allowing calibration on any radar scene, including those with moving targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional calibration methods using reference objects (known targets or antenna channels) are employed, then calibration can be performed with available measurement data, but the calibration becomes vulnerable to errors from moving targets and requires specific scene conditions
Solution Approach 1:
The patent extracts and removes the dependency on reference objects (known targets or antenna channels) from the calibration process. By formulating calibration to work with any radar scene without requiring specific reference objects, the method eliminates the vulnerability to moving targets and scene condition constraints that plague conventional reference-object-based approaches
Solution Approach 2:
The calibration method achieves universality by being applicable to any radar scene regardless of its content. The optimization-based approach can process measurement data from diverse scenes (with or without moving targets, various terrains, etc.) without requiring the scene to meet specific criteria, making the calibration procedure universally applicable across all operating conditions
2Adaptability or versatility
If iterative methods or approximations are used to estimate parameters from unknown radar scenes, then calibration can proceed without known targets, but the presence of moving targets impairs the quality of estimated parameters
Solution Approach 1:
The patent implements feedback through an optimization loop that iteratively adjusts calibration parameters to maximize a cost function based on measurement data consistency. This feedback mechanism allows the system to automatically converge to optimal parameter values while being robust to moving targets, as the optimization process inherently accounts for all targets in the scene rather than being disrupted by them
Solution Approach 2:
The method changes the approach from direct parameter estimation (which is sensitive to moving targets) to optimization-based parameter determination. By formulating calibration as an optimization problem where parameters are adjusted to maximize measurement consistency, the system achieves accurate parameter estimation even in the presence of moving targets that would otherwise corrupt direct estimation methods
3Measurement precision
If direct methods using known targets are employed for absolute calibration, then precise parameter determination is achieved, but the method cannot be applied when no known targets are available
Solution Approach 1:
The patent inverts the conventional approach by not starting with known targets to determine parameters, but rather using the parameters themselves as optimization variables to be determined through cost function maximization. This inversion allows calibration to proceed without known targets, using the radar scene itself and the measurement data consistency as the basis for determining precise parameters
Data Source
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AI summary
A method for calibrating at least one parameter of a radar system comprises receiving (110) a plurality of measurements of a radar scene and transforming (120) the plurality of measurements into a representation dependent on the parameter. The method further comprises varying (130) the parameter until a similarity measure of at least one measured value among the plurality of measurements is maximized.