Swarm Agent Radar for Buried Object Discrimination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current ground-penetrating radar systems struggle to accurately distinguish between different types of buried objects, such as landmines and utility structures, due to their fixed sensor array configurations and limited flexibility, which hampers efficient identification and resource allocation.
Innovation Solution
An object discrimination system employing a swarm of agents with sensors, such as UAVs, UUVs, and UGVs, that can assume various configurations and use design-of-experiments techniques like Latin hypercubes to select optimal positions and sensor settings for acquiring data, allowing for the analysis of return signals to differentiate between object types based on feature extraction and singular value decomposition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed sensor array configuration is used, then the system structure is simple and stable, but the object type discrimination accuracy is insufficient
Solution Approach 1:
The patent implements dynamic sensor array configurations where agents (drones, robots, or sensors) can adjust their positions and orientations in real-time based on object characteristics. The system transitions from static fixed arrays to dynamic reconfigurable arrays, allowing optimal positioning for different object types while maintaining manageable complexity through automated control algorithms.
Solution Approach 2:
The system changes multiple parameters simultaneously including agent positions, sensor orientations, aperture sizes, and frequency selections based on object characteristics. This multi-parameter optimization enables accurate discrimination of different object types by adapting the measurement configuration to match the specific features of each detected object.
2Productivity
If manual determination of object types is performed, then resource allocation can be adjusted, but significant time and resources are consumed
Solution Approach 1:
The patent replaces manual visual inspection and physical examination with automated radar-based detection systems. The system uses electromagnetic wave interactions, signal processing algorithms, and machine learning models to automatically classify objects, eliminating the need for manual determination while significantly reducing time and resource consumption.
Solution Approach 2:
The system performs self-classification of detected objects using automated analysis of radar return signals. The algorithm independently determines object types based on measured characteristics without requiring human intervention, enabling continuous operation and rapid processing of multiple objects.
3Measurement precision
If various sensor configurations are tested to improve discrimination accuracy, then object type identification improves, but the complexity of system operation increases
Solution Approach 1:
The system incorporates feedback loops where measurement results from initial configurations inform subsequent configuration adjustments. The algorithm analyzes returned signals, determines discrimination quality, and automatically adjusts sensor positions and parameters to optimize measurement accuracy, reducing the need for manual configuration management.
Solution Approach 2:
The system performs preliminary testing and optimization of sensor configurations during system setup or before actual detection tasks. Training data is collected and analyzed in advance to establish optimal configuration patterns for different object types, allowing rapid deployment without extensive real-time configuration adjustments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly enhances the accuracy of object type identification by enabling flexible sensor array configurations and variable apertures, overcoming the limitations of fixed arrays, and allows for efficient discrimination of buried objects.
Implementation Method 1
Some technologies may use ultra-wideband ground-penetrating radar ('GPR') antennas that are mounted as a linear array of sensors on the front of a vehicle
Implementation Method 2
When a radar signal strikes a subsurface object, it is reflected back as a return signal to a receiver
Implementation Method 3
GPR systems can be used to detect not only metallic objects but also nonmetallic objects whose dielectric properties are sufficiently different from those of the soil
Data Source
AI summary
A configuration system for identifying a target configuration of agents for acquiring data is provided. In some embodiments, the configuration system generates sample configurations, each of which has a location within the space for each agent. The configuration system selects as the target configuration a sample configuration that is suitable to acquire data. The configuration system selects the target configuration by, for at least one or more sample configurations, directing that the agents assume the sample configuration and then acquire data when in the sample configuration. The configuration system then stores, for each sample configuration, an indication of suitability for acquiring data based on the sample configuration. The configuration system selects a sample configuration that is deemed to be suitable as the target configuration.