Radar Data Fusion for Target Elevation Estimation
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Solution Overview
Problem
Conventional collision avoidance and target identification systems using radar technology generate a high number of false alerts due to their inability to accurately determine the elevation of objects, leading to nuisance warnings from hyper-elevated objects like overpasses and hypo-elevated features like railroad tracks.
Innovation Solution
A collision avoidance system utilizing single-dimensional scanning radar technology with data fusion from two radar sensors of different ranges and beam angles to estimate target elevation, reducing false alerts by determining the relative signal value and comparing it to predetermined categories for accurate object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional single-dimensional radar sensors are used for collision avoidance detection, then the system is cost-effective and easy to implement, but the system generates a high number of false alerts due to inability to determine target elevation
Solution Approach 1:
The patent combines data from multiple single-dimensional radar sensors (SRR and LRR) with different beam angles and ranges to create a fused output that estimates target elevation. This merging approach allows the system to maintain cost-effectiveness while improving reliability by reducing false alerts through elevation discrimination.
Solution Approach 2:
The patent introduces an intermediary processing layer that fuses radar data from multiple sensors and compares the fused output against a database of known hyper- and hypo-elevated objects. This intermediary process enables elevation estimation without requiring expensive 3D sensors, thereby reducing false alerts while maintaining implementation simplicity.
2Measurement precision
If stereo vision or 2D scanning Lidar is used to obtain three-dimensional target information, then target elevation can be accurately determined, but the cost of implementation and operation becomes prohibitively high
Solution Approach 1:
The patent creates a computational model (copy) of three-dimensional space by fusing data from multiple single-dimensional radar sensors. Instead of using expensive 3D sensors, the system reconstructs elevation information through data fusion algorithms, achieving comparable measurement precision at a fraction of the cost.
Solution Approach 2:
The patent changes the parameters of existing radar sensors by utilizing multiple sensors with different beam angles and ranges. By varying these parameters across multiple sensors, the system achieves three-dimensional measurement capability without requiring expensive specialized hardware, thereby reducing implementation cost while maintaining measurement precision.
3Measurement precision
If monopulse or phased array radar technology is used to achieve azimuth and elevation resolution, then target elevation can be determined, but the device complexity and cost increase significantly
Solution Approach 1:
The patent segments the elevation measurement function across multiple independent single-dimensional radar sensors rather than requiring a single complex phased array system. Each sensor performs simple one-dimensional scanning, but the collective data from multiple sensors provides elevation resolution, thereby reducing device complexity while maintaining measurement precision.
Data Source
AI summary
A collision avoidance system for reducing false alerts by estimating the elevation of a target, includes short and long range single-dimensional scanning radar sensors having differing ranges and beam angles of inclination, and a digital fusion processor, and preferably includes a locator device, an inclinometer, and a memory storage device cooperatively configured to further perform trend analysis, and target tracking.