Drone Operator Location Mapping Using Multi-Sensor Probability Fusion
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Solution Overview
Problem
Existing methods for restricting drone operations in sensitive areas face challenges such as drones being hacked or operators intentionally flying in prohibited zones, making it difficult to locate and address unauthorized drone operations.
Innovation Solution
A system and method that utilize map information and detection of drone positions to form probability mappings of the drone operator's location, combining these mappings to identify prospective locations where the drone operator is likely to be, based on predetermined probability thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probabilistic location mapping is used to identify drone operators, then measurement precision of operator location is improved, but device complexity increases due to multiple detection means and computing units
Solution Approach 1:
The system divides the detection task into multiple independent detection means (radar, acoustic sensors, visual sensors) that each detect different aspects of drone operator location. Each sensor type operates independently to detect specific parameters, and their results are later integrated through probability mappings to achieve high-precision location identification.
Solution Approach 2:
The system combines multiple detection means and their respective probability mappings into a unified location determination system. The computing unit integrates data from radar, acoustic sensors, and visual sensors by merging their probability mappings to calculate the overall probability of operator presence at specific locations, thereby achieving accurate location identification through combination.
2Reliability
If multiple detection means are deployed to track drones, then reliability of drone detection is improved, but loss of energy increases due to operation of multiple sensors and computing units
Solution Approach 1:
The system dynamically activates and deactivates detection means based on operational conditions and probability thresholds. Detection means are activated only when necessary to maintain reliable drone tracking, and the system adjusts its operational state according to the calculated probability of operator presence, thereby reducing unnecessary energy consumption while maintaining detection reliability.
Solution Approach 2:
The system uses feedback from probability calculations to control the operation of detection means. When the probability of operator presence exceeds certain thresholds, the system activates additional detection means to improve reliability. When probabilities are low or conditions are stable, the system reduces activation to conserve energy, creating a feedback-controlled energy management mechanism.
Data Source
AI summary
Disclosed is a method for finding a location of a drone operator within an area by obtaining map information of the area; detecting a first set of positions of a drone; forming a first set of probability mappings based on the respective positions of the first set of positions and the obtained map information, wherein each of the first probability mappings provides, respectively a first probability of the location of the drone operator; combining the first set of probability mappings together to obtain a combined probability mapping of the location of the drone operator; and selecting as prospective location area of the drone operator, the location area in which values of the combined probability mappings are above predetermined value.