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Adaptive Kalman Filtering Localization Calibration Method Based on Dynamic Mutation Perception and Collaborative

Zijia Huang¹, Qiushi Xu², Menghao Sun²

¹National Key Laboratory of Multi-Domain Data Collaborative Processing and Control, Xi'an 710068, China.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces an adaptive Kalman filtering method to improve unmanned swarm navigation accuracy in complex electromagnetic environments. The approach enhances positioning by monitoring system mutations and collaboratively correcting trajectories.

Keywords:

adaptive Kalman filtering collaborative correction dynamic mutation perception

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Area of Science:

Robotics and Control Systems
Navigation and Positioning Technologies
Electromagnetic Interference Mitigation

Background:

Unmanned swarm navigation systems face reduced accuracy due to dynamic noise in complex electromagnetic environments.
Existing Kalman filtering methods struggle with abrupt noise, impacting collaborative positioning performance.

Purpose of the Study:

To propose an adaptive Kalman filtering method for enhanced positioning and calibration in unmanned swarm navigation.
To address the challenge of dynamic abrupt noise in complex electromagnetic environments.

Main Methods:

Developed an adaptive Kalman filtering approach incorporating dynamic mutation perception and collaborative correction.
Implemented real-time monitoring of acceleration and velocity mutations with a dynamic threshold detection mechanism.
Utilized multidimensional scaling analysis for trajectory similarity calculation and collaborative state correction, adaptively adjusting the covariance matrix.

Main Results:

The proposed method demonstrated improved positioning accuracy compared to traditional extended Kalman filter algorithms.
Experimental validation using simulation and real-world data confirmed the effectiveness of the adaptive filtering technique.
Successfully mitigated the impact of complex electromagnetic interference on unmanned swarm collaborative positioning.

Conclusions:

The adaptive Kalman filtering method offers an effective solution for robust unmanned swarm navigation under electromagnetic interference.
Real-time adaptation and collaborative correction are crucial for maintaining high positioning accuracy in dynamic environments.
This research provides a significant advancement for reliable collaborative positioning in unmanned systems.