MEMS IMU Sensor Array Blending for Precision
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Solution Overview
Problem
Conventional Inertial Measurement Units (IMUs) used in navigation systems are large, expensive, and require high power, while MEMS-based IMUs have limited performance due to high measurement noise and unstable parameters, restricting their use in applications requiring low size, weight, and power (SWAP) with high accuracy.
Innovation Solution
An IMU comprising a plurality of micro-electromechanical system (MEMS) sensors with a processor that applies calibration coefficients and blending weights to fuse the outputs of multiple sensors, calculated using harmonic or geometric means of test parameters, to enhance precision and reduce measurement errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional IMUs are used, then high measurement precision and stable performance parameters are achieved, but size, weight and power consumption increase
Solution Approach 1:
The patent combines multiple low-grade MEMS sensors into an array configuration, merging their outputs through weighted blending algorithms. This approach achieves high measurement precision comparable to conventional IMUs while maintaining the low weight and power characteristics of MEMS technology, resolving the contradiction between precision and weight.
Solution Approach 2:
The system dynamically adjusts blending weights based on real-time performance parameters of each MEMS sensor. By changing the weighting parameters adaptively, the system optimizes measurement precision while maintaining low SWAP, effectively resolving the trade-off between precision and weight.
2Use of energy by moving object
If MEMS sensors are used, then low size, weight and power are achieved, but measurement noise and unstable performance parameters increase
Solution Approach 1:
The system continuously monitors performance parameters of each MEMS sensor and uses this feedback to dynamically adjust blending weights. This feedback mechanism compensates for unstable performance parameters and measurement noise, achieving reliable navigation-grade precision while maintaining low power consumption of MEMS sensors.
Solution Approach 2:
The patent creates a composite sensing system by combining multiple MEMS sensors with different performance characteristics. The weighted blending of outputs from these diverse sensors produces a composite measurement signal that is more stable and less noisy than individual MEMS sensors, while maintaining low power consumption.
3Weight of moving object
If MEMS sensors are used, then low size, weight and power are achieved, but measurement precision decreases
Solution Approach 1:
By merging outputs from multiple MEMS sensors through weighted blending, the system achieves measurement precision comparable to conventional IMUs while maintaining the low weight advantage of MEMS technology. The combination of multiple sensors compensates for individual sensor limitations.
Solution Approach 2:
The system segments the measurement function across multiple independent MEMS sensors rather than relying on a single sensor. Each sensor contributes to the overall measurement through weighted blending, achieving high precision while keeping individual sensor requirements (and thus weight) low.
Data Source
AI summary
An inertial measurement unit (IMU). The IMU includes: a plurality of micro-electromechanical system (MEMS) sensors, each having an output; a memory for storing calibration coefficients separately for each of the plurality of MEMS sensors, blending weights for each of the plurality of MEMS sensors, and data blending instructions for blending the outputs of the plurality of MEMS sensors; and a processor, coupled to the memory and the plurality of MEMS sensors, configured to execute the data blending instructions to apply the calibration coefficients separately to each of the plurality of MEMS sensors and the blending weights to the outputs of the plurality of MEMS sensors to create a blended output for the IMU; wherein the blending weights are calculated based on a plurality of test parameters for the plurality of MEMS sensors using at least one of a harmonic and a geometric mean of the plurality of test parameters.