Multi-Sensor Fusion Using Conditional Belief Functions
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Solution Overview
Problem
Existing information fusion schemes for multi-sensor systems are limited in their ability to account for various types of knowledge regarding the dependency, competence, and sincerity of sensors, particularly when sensors are in different operating states, such as being competent and independent or non-independent.
Innovation Solution
A method and system for calculating a merged belief function that takes into account the operating states of sensors by using conditional belief functions and a knowledge base to represent the propensity of sensors in specific states, employing operators like the unnormalized Dempster's rule and cautious rule based on predefined associations between sensor states and merge operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing information fusion schemes are used, then the fusion process is simple, but the ability to account for various types of knowledge regarding sensor dependency, competence, and operating states is limited
Solution Approach 1:
The patent segments the fusion process by introducing conditional belief functions that separate different operating states of sensors. Each sensor state (competent, non-competent, independent, non-independent) is handled by a specific merge operator, dividing the complex fusion problem into manageable state-specific sub-problems while maintaining overall reliability
Solution Approach 2:
The patent applies dynamics by making the merge operator selection dynamic rather than static. The system automatically selects appropriate merge operators (Dempster's rule, cautious rule, etc.) based on the current operating states of sensors, allowing the fusion scheme to adapt to changing sensor conditions and incorporate various knowledge types about sensor behavior
2Measurement precision
If conditional belief functions and multiple merge operators are used to account for sensor operating states, then the accuracy of information fusion improves, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining a library of merge operators corresponding to different sensor operating states (Dempster's rule for independent competent sensors, cautious rule for non-independent sensors, etc.). This preparation allows the system to quickly select and apply the appropriate operator without performing complex analysis during the fusion process itself, maintaining precision while reducing computational burden
Data Source
AI summary
The invention relates to a method, device and system for fusion of information originating from several sensors. The invention includes a mechanism for fusion of belief functions. To apply this mechanism, various information, knowledge and operations are modelled within the framework of the theory of belief functions: information provided by the sensors, knowledge regarding the propensity of the sensors to be in a given operating state, and merge operators for each operating state considered.