Multi-sensor information fusion estimation method for state-dependent observation loss

By constructing a linear discrete dynamic system model and a support vector machine discriminator, combined with a recursive fusion estimator of EM algorithm, the problem of state-dependent observation loss in multi-sensor fusion is solved, and the accuracy and robustness of state estimation is improved, and it is suitable for fields such as unmanned driving and environmental monitoring.

CN120449089APending Publication Date: 2025-08-08TONGJI UNIV
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Patent Information

Application Number
CN202510534466.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-27
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The existing multi-sensor fusion estimation methods lack accuracy and stability in the case of state-dependent observation loss, especially in practical scenarios such as unmanned driving and environmental monitoring, the estimation performance of traditional methods has significantly decreased.

Method used

A linear discrete dynamic system model is constructed, and an observation loss discriminator is trained in combination with support vector machines, and a recursive fusion estimator is constructed based on the EM algorithm. The system state is iteratively updated through the maximum posterior probability optimization framework to deal with state-dependent observation loss problem.

Benefits of technology

It significantly improves the accuracy and robustness of system state estimation, can effectively deal with state-dependent observation loss in various practical scenarios, and provides more accurate state perception and environmental understanding.

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Abstract

The invention discloses a multi-sensor information fusion estimation method for state-dependent observation loss, and the method comprises the steps: constructing a system model which is a linear discrete dynamic system model and is used for representing a dynamically changing system state; training an observation loss discriminator, processing a complex observation rejection domain by the observation loss discriminator through a support vector machine, and converting a nonlinear classification problem into a linear convex optimization problem through a kernel function so as to discriminate whether a target state is in the observation rejection domain; and constructing a recursive fusion estimator based on an EM algorithm. According to the invention, the recursive estimator has high robustness and adaptability, can effectively deal with the complex situation of state dependence observation loss in various actual scenes, and significantly improves the precision of system state estimation.
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