Sensor Rotation Bias Removal via Cooperative Target Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Fixed cameras, pan-tilt-zoom cameras, and surface detection radars mounted on towers face measurement errors due to slowly varying deformations caused by temperature gradients and wind loading, leading to rotation biases that complicate accurate target location and tracking.
Innovation Solution
A method and system using a cooperative target with GPS to determine rotation bias in sensors by calculating a maximum likelihood estimate based on sensor data and target position data, allowing for automated calibration and correction of sensor measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compass and inclinometer boards are mounted on each sensor to correct rotation errors, then measurement precision is improved, but device complexity and maintenance burden increase significantly
Solution Approach 1:
A cooperative target with known position is introduced as an intermediary between the sensor and the rotation bias. The target serves as a reference object that enables the sensor to self-calibrate by comparing measured positions with actual GPS positions, eliminating the need for separate compass and inclinometer boards on each sensor.
Solution Approach 2:
The sensor system performs self-calibration by using the cooperative target as a reference. The rotation bias is automatically determined through maximum likelihood estimation comparing sensor measurements with known target positions, allowing the system to correct its own errors without external calibration equipment.
2Reliability
If multiple compass and inclinometer boards are deployed across large installations, then rotation correction capability is improved, but calibration and maintenance time increase
Solution Approach 1:
Each sensor automatically determines its own rotation bias by processing measurements of the cooperative target and performing maximum likelihood estimation. This self-calibration approach eliminates the need for manual calibration of multiple compass and inclinometer boards, significantly reducing maintenance time and effort across large installations.
Solution Approach 2:
The system transforms the calibration problem from a mechanical adjustment of multiple boards into a computational parameter estimation problem. By changing from physical calibration to mathematical optimization (maximum likelihood estimation), the system achieves automatic, rapid calibration without manual intervention.
3Measurement precision
If traditional calibration methods are used for each sensor, then individual sensor accuracy is improved, but overall system cost increases due to multiple calibration boards
Solution Approach 1:
A single cooperative target serves all sensors in the installation, providing a universal reference for rotation bias determination. This multi-functional approach allows one target to enable calibration for multiple sensors simultaneously, eliminating the need for separate calibration boards on each sensor and reducing overall system cost.
Solution Approach 2:
The cooperative target acts as a shared intermediary that facilitates calibration across the entire sensor network. By using this common reference object, the system achieves individual sensor accuracy without the expense of duplicating calibration equipment for each sensor.
Data Source
AI summary
A method for determining rotation bias in a sensor using a cooperative target that includes receiving, via a processor, sensor data acquired in a field of view of a sensor at a plurality of points in time. The method also includes receiving, via the processor, position data for a cooperative target travelling along a path through the field of view of the sensor at the plurality of points in time. The method also includes determining, via the processor, a rotation bias in the sensor based on a maximum likelihood estimate performed over the path based on the sensor data and the cooperative target position data.