Cooperative Sensor Grouping Using HMM Entropy Metrics
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Solution Overview
Problem
Existing detection systems lack an optimized method for selecting detection devices to cooperate effectively for surveillance missions, leading to inefficiencies in task assignment and operation.
Innovation Solution
A cooperative detection system utilizing Hidden Markov Chains (HMM) to determine transition rate matrices, calculate cooperation metrics, and group detection devices based on entropy values, enabling efficient task distribution and surveillance control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If detection devices operate independently and in isolation, then each device can function autonomously, but the overall surveillance efficiency and task assignment optimization deteriorate
Solution Approach 1:
The patent combines multiple independent detection devices into cooperative groups that share tasks and information. The system merges the capabilities of individual devices while maintaining their autonomous operation through a coordination layer that assigns tasks based on device characteristics and current surveillance needs.
2Ease of operation
If operator selection is used for detection device grouping, then device selection can be performed, but the optimization of cooperative groups deteriorates
Solution Approach 1:
The system implements feedback mechanisms where surveillance performance data is continuously collected and used to refine task assignment algorithms. The system learns from operational outcomes to optimize which devices are grouped together for specific surveillance tasks, improving cooperation effectiveness over time.
Solution Approach 2:
The patent dynamically changes grouping parameters based on surveillance conditions, device availability, and performance metrics. Instead of fixed operator-defined groups, the system adjusts device groupings and task assignments in response to changing operational parameters to maintain optimal surveillance efficiency.
3Extent of automation
If distributed execution logic is implemented across detection devices, then task assignment can be automated, but the complexity of coordination and control increases
Solution Approach 1:
The patent segments the surveillance mission into discrete tasks that can be independently assigned to different device groups. Each detection device maintains simplified local logic for executing assigned tasks, while a central coordination system handles the complexity of task distribution and inter-device communication.
Data Source
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AI summary
A cooperative detection system (100) is provided, configured to: - estimate (12) a transition rate matrix, for each candidate group of a set of detection devices (2), by performing learning based on hidden Markov chains, from data on activity states of the detection devices (2), the coefficients of the transition rate matrix representing the transition speeds from one activity state to another for each detection device (200) of the associated candidate group, - determine (13) a cooperation metric relating to the entropy of each candidate group from the associated transition rate matrix; - determine (14) at least one grouping (20) of detection devices (200) connected in a network and capable of cooperating with each other, from the cooperation metrics, the cooperative detection system being capable of using each grouping to monitor an area.