Magnetic Heading Computation with Dynamic Noise-Adaptive Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Magnetic sensors in computing devices face interference from time-varying magnetic noise, leading to inaccurate heading measurements due to platform-induced noise, which complicates navigation and accuracy determination.
Innovation Solution
The technique dynamically adjusts the number of sensor readings based on noise levels, desired accuracy, and confidence levels to mitigate time-varying noise, allowing for efficient computation of magnetic headings with reduced processing steps and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensor readings are used to improve heading accuracy, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent implements dynamic adjustment of the number of sensor readings based on real-time noise level detection. The system transitions from a static fixed-sample approach to a dynamic adaptive approach where the sample size changes according to environmental conditions, thereby optimizing the balance between accuracy and computational complexity
Solution Approach 2:
The patent changes the parameter of sample size from a fixed value to a variable that adapts to noise conditions. By adjusting this parameter dynamically based on detected noise levels, the system achieves variable computational complexity that matches the actual measurement requirements, reducing unnecessary computations in low-noise environments while maintaining accuracy in high-noise conditions
2Measurement precision
If a large number of sensor readings are averaged to reduce noise impact, then measurement precision improves, but processing time and power consumption increase
Solution Approach 1:
The system dynamically adjusts the number of readings based on detected noise levels, creating a responsive processing time that adapts to environmental conditions. In low-noise environments, processing time is reduced by using fewer samples, while in high-noise environments, more samples are processed to maintain accuracy
Solution Approach 2:
The sample size parameter is changed from a constant to a variable that responds to noise conditions. This parameter change enables the system to optimize processing time by using the minimum necessary number of readings to achieve the required accuracy level for current conditions
3Measurement precision
If more sensor readings are processed to achieve desired accuracy, then measurement precision improves, but energy consumption increases
Solution Approach 1:
The patent implements dynamic energy management by adjusting the number of sensor readings based on real-time noise detection. The system transitions from constant high-energy processing to adaptive energy consumption that matches actual measurement needs, reducing power usage in favorable conditions while maintaining accuracy when necessary
Solution Approach 2:
The sample size parameter is dynamically changed based on noise conditions, directly impacting energy consumption. By adjusting this parameter, the system optimizes the energy-accuracy tradeoff, using more energy only when environmental conditions require higher measurement precision
4Measurement precision
If complex algorithms with large numbers of sensor readings are used to determine heading and accuracy, then measurement precision improves, but computational efficiency decreases
Solution Approach 1:
The patent introduces dynamic adaptability to the computational process by adjusting the number of readings based on noise levels. This creates a flexible computational efficiency that can operate at high speed in low-noise conditions while maintaining accuracy through increased sampling in high-noise conditions, optimizing the overall productivity-accuracy balance
Solution Approach 2:
The sample size parameter is changed from a fixed high value to a dynamically adjusted value based on noise conditions. This parameter change enables the system to achieve high computational efficiency when possible while maintaining the capability to increase accuracy when environmental conditions require it
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
This approach improves computational efficiency and accuracy by dynamically adjusting sample sizes for magnetic heading calculations, enabling faster and more precise navigation while minimizing power usage.
Implementation Method 1
magnetic sensor circuit having at least two sensing elements for sensing perpendicular components of the Earth's magnetic field vector
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Techniques for computing a magnetic heading based on sensor data are described herein. An example of a device in accordance with the present techniques includes a magnetic sensor to collect sensor output data and a heading computation engine to compute a magnetic heading based on the sensor output data. The heading computation engine includes logic to measure a level of noise in the sensor output data. The heading computation engine also includes logic to determine whether to average the sensor output data based, at least in part, on the level of noise. The heading computation engine also includes logic to determine an applied sensor output based on the sensor output data. The heading computation engine also includes logic to compute a magnetic heading based, at least in part, on the applied sensor output.