Multi-UAV Spatial Channel Map Construction via Data Fusion
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Solution Overview
Problem
Single UAVs are limited by load, endurance, and storage capacity, making it difficult to perform large-scale, fully three-dimensional, and high-density wide-area spatial channel measurements in a timely manner, which hinders the construction of a complete channel map and analysis of UAV channels.
Innovation Solution
A surveying and mapping instrument and method utilizing multi-UAV cooperation to extract effective multipath components from channel impulse responses, with a ground server fusing data to generate a complete wide-area spatial channel map, reducing onboard data storage needs and enhancing measurement efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a single UAV performs channel measurement, then the measurement can be completed with existing UAV capabilities, but the measurement coverage and density are limited due to load, endurance, and storage constraints
Solution Approach 1:
The patent divides the measurement task into multiple segments by deploying multiple UAVs (first UAV and second UAV) that operate independently but cooperatively. Each UAV collects channel data in its local area, and the ground station aggregates these segmented measurements to achieve comprehensive wide-area coverage that would be impossible for a single UAV to accomplish within its operational constraints.
Solution Approach 2:
The patent merges the measurement capabilities of multiple UAVs by having them simultaneously perform channel measurements in different spatial regions. The ground station receives and integrates data from both UAVs, combining their individual measurement results to construct a complete wide-area spatial channel map that exceeds the capabilities of any single UAV.
2Productivity
If a single UAV performs channel measurement, then the system complexity remains low, but the measurement time and storage requirements become excessive for large-scale applications
Solution Approach 1:
The patent segments the measurement task across multiple UAVs that can operate simultaneously and independently. This parallel segmentation allows the measurement system to cover large areas much faster than a single UAV could, as multiple measurements are conducted concurrently rather than sequentially.
Solution Approach 2:
The patent introduces a spatial dimension by deploying multiple UAVs at different locations and altitudes. This dimensional expansion from single-point to multi-point measurement enables parallel data collection across wide areas, dramatically reducing the total measurement time required while maintaining comprehensive coverage.
3Area of stationary object
If multi-UAV cooperation is implemented, then wide-area measurement coverage is achieved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a ground station as an intermediary that receives channel data from multiple UAVs and performs centralized processing. This intermediary handles the complexity of data aggregation, fusion, and map construction, allowing the UAVs to focus on their primary measurement function while the ground station manages the system-level complexity.
Solution Approach 2:
The patent uses identical measurement modules and processing algorithms across multiple UAVs, creating standardized copies of the same measurement system. This standardization simplifies the overall system architecture by allowing the same proven module to be replicated and deployed across multiple platforms, reducing the complexity burden that would otherwise arise from customizing each UAV's measurement system.
Data Source
AI summary
According to a surveying and mapping instrument and method for a wide-area spatial channel map through multi-unmanned aerial vehicle (UAV) cooperation, a measurement signal emission unit generates and emits a measurement signal, a measurement signal multi-UAV cooperative receiving unit receives the measurement signal transmitted through a wireless channel, extracts an effective multipath component from the measurement signal, and frames the effective multipath component and corresponding time and location information for storage, and transmits stored channel data to a channel data fusion processing unit offline, and the channel data fusion processing unit fuses the channel data in terms of space, time, and frequency dimensions, and completes missing channel data to construct a complete wide-area spatial channel map.