Angle Sensor Noise Reduction via Iterative Nonlinear Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing angle sensor systems face challenges in generating highly accurate angle detection values with reduced error caused by noise magnetic fields, especially when the strengths and directions of composite magnetic fields at multiple detection positions cannot be represented by linear model functions.
Innovation Solution
An angle sensor system comprising multiple magnetic sensors and a processor that detect composite magnetic fields at various positions, using nonlinear model functions and iterative processes to estimate unknowns, minimizing discrepancy values and reducing angle errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple detection circuits are used to detect composite magnetic fields at multiple positions, then measurement precision can be improved, but device complexity increases
Solution Approach 1:
The patent combines multiple detection circuits into a single detection circuit that sequentially measures magnetic fields at multiple positions. The detection circuit is controlled to move or switch between detection positions, integrating the functions of multiple circuits into one, thereby reducing device complexity while maintaining the ability to detect composite magnetic fields at multiple locations for accurate angle measurement
Solution Approach 2:
The patent employs dynamic measurement by sequentially detecting magnetic fields at multiple positions rather than using static simultaneous detection. The detection circuit dynamically switches between positions, and the control unit processes these sequential measurements to calculate angle information, achieving multi-point detection capability without requiring multiple simultaneously operating circuits
2Ease of manufacture
If linear model functions are used to represent composite magnetic fields, then calculation simplicity is improved, but measurement precision deteriorates when fields are nonlinear
Solution Approach 1:
The patent changes the mathematical model from linear to nonlinear functions to represent the composite magnetic fields. The control unit uses nonlinear model functions that better capture the actual magnetic field characteristics, and adjusts calculation parameters accordingly to solve for angle information, thereby improving measurement precision while accepting increased calculation complexity
Solution Approach 2:
The patent replaces simple linear mathematical modeling with more sophisticated nonlinear mathematical modeling and iterative calculation methods. The control unit employs numerical algorithms to solve nonlinear equations, substituting simple analytical calculations with more powerful computational approaches that can handle complex magnetic field relationships
3Reliability
If noise magnetic fields are present in detection, then reliability of detection is worsened, but angle detection can still be performed
Solution Approach 1:
The patent extracts and separates the noise magnetic field components from the useful detection signals through mathematical processing. The control unit uses the measured composite magnetic fields at multiple positions to calculate and subtract noise components, isolating the actual angle-related magnetic field information and eliminating noise interference from the final angle measurement
Solution Approach 2:
The patent introduces intermediate calculation steps that use the spatial distribution of magnetic fields at multiple positions as a mediator to identify and remove noise. By analyzing how magnetic fields vary across positions and comparing with expected patterns, the system uses these intermediate measurements as mediators to distinguish noise from genuine signal variations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively generates highly accurate angle detection values with reduced noise magnetic field errors, even when composite magnetic field representations are nonlinear, by iteratively refining estimates of unknowns and approximating functions.
Implementation Method 1
a plurality of magnetic sensors detect a composite magnetic field at a plurality of detection positions different from each other and generate a plurality of detection values that represent directions of the composite magnetic field
Data Source
AI summary
An angle sensor includes a plurality of magnetic sensors and a processor. The plurality of magnetic sensors generate a plurality of detection values representing directions of a composite magnetic field, which is a composite of a magnetic field to be detected and a noise magnetic field. The processor assumes a group of estimated unknowns. The group of estimated unknowns is a set of estimated values of a first, a second, and a third unknown. The first unknown corresponds to an angle detection value. The second unknown corresponds to the direction of the noise magnetic field. The third unknown corresponds to the strength of the noise magnetic field. The processor executes a process for determining the group of estimated unknowns a plurality of times, and assumes an estimated value of the first unknown in the last determined group of estimated unknowns as the angle detection value.