Inertial Navigation Static State Detection Using IMU Standard Deviation
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Solution Overview
Problem
Current inertial navigation systems (INS) face increasing errors over time due to sensor errors, and existing zero velocity update (ZUPT) methods have low accuracy in determining static states.
Innovation Solution
A method that calculates standard deviations from inertial measurement unit (IMU) data, matches them with a database to determine corresponding probabilities and weights, and uses these to determine whether the system is in a static or moving state by comparing a calculated probability to a static probability threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a threshold method is used to determine static state, then the detection process is simple, but the accuracy of determining static state is low
Solution Approach 1:
The patent changes the parameter used for static state detection from simple threshold comparison of raw IMU data to comparison of standard deviation values against a database. By transforming the detection parameter from direct acceleration/velocity values to statistical standard deviation metrics, the system achieves higher accuracy in distinguishing static from dynamic states while maintaining computational feasibility through pre-built databases.
Solution Approach 2:
The patent applies preliminary action by pre-building databases containing standard deviation characteristics for both static and dynamic states before actual detection. These pre-computed databases store statistical profiles that enable rapid comparison during runtime, eliminating the need for complex real-time analysis while improving detection accuracy through预先 prepared reference data.
2Reliability
If the inspection time is extended to reduce INS error, then the INS error becomes larger as time increases, but extending inspection time can improve measurement accuracy
Solution Approach 1:
The patent substitutes the traditional time-based error correction mechanism with a statistical detection mechanism. Instead of relying on extended inspection periods to average out sensor errors, the system uses standard deviation analysis to quickly identify static states and apply corrections, replacing the time-intensive mechanical waiting process with a more efficient statistical approach.
Data Source
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AI summary
A static state determining method and apparatus are disclosed to resolve a problem in the prior art that accuracy of determining a static state is low. An inertial navigation system obtains first running data that is measured by an IMU in first specified duration, determines N first standard deviations of the first running data; matches the N first standard deviations with a prestored database, to determine N second standard deviations the same as the N first standard deviations; determines, in the prestored database, first information corresponding to each of the N second standard deviations; multiplies a first probability in each of the N pieces of first information by a corresponding weight, and adds N values obtained through the multiplication, where a value obtained through the addition is determined as a second probability that the N first standard deviations are static; and if the second probability is greater than or equal to a static probability threshold, the inertial navigation system determines that a device in which the inertial navigation system is located is in a static state in the first specified duration.