UAV Height Detection Using Kalman-Filtered Millimeter-Wave Radar
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Solution Overview
Problem
Conventional height detection methods for unmanned aerial vehicles, such as GPS altimetry, barometric altimetry, and ultrasonic radar, suffer from inaccuracies in uneven terrain, climate susceptibility, and limited measurement ranges, posing safety risks due to unreliable altitude information.
Innovation Solution
An automatic obstacle avoidance method using millimeter-wave radar combined with Kalman filtering to exclude invalid data from detected data, incorporating theoretical state information from the carrier's motion, and calculating height information based on corrected data to ensure accurate altitude detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional height detection methods (GPS, barometer, ultrasonic radar) are used, then the system structure is simple, but the measurement precision and reliability are insufficient due to terrain, climate, and range limitations
Solution Approach 1:
The patent combines multiple data sources (radar detected data and carrier motion information) into a unified height detection system. The Kalman filter integrates these diverse inputs to produce accurate height measurements, resolving the contradiction by merging simple components into a coherent system that achieves high precision without excessive complexity
Solution Approach 2:
The Kalman filter acts as an intermediary that processes and reconciles data from radar and motion sensors. It mediates between the raw detected data and the theoretical motion information to produce reliable height estimates, enabling accurate measurement while managing system complexity through intelligent data fusion
2Reliability
If radar detected data is used directly for height calculation, then the response speed is fast, but the reliability deteriorates due to invalid data from non-ground targets and external noise
Solution Approach 1:
The Kalman filter performs preliminary processing on radar detected data before height calculation. By pre-filtering and validating data points against theoretical motion models, the system eliminates invalid data from non-ground targets and noise in advance, ensuring reliability without significant time loss
Solution Approach 2:
The system uses feedback from carrier motion information to continuously validate and correct radar height measurements. The Kalman filter compares detected data with expected motion patterns and adjusts accordingly, maintaining reliability while minimizing processing delays through iterative refinement
3Measurement precision
If GPS altimetry is used, then the device cost is low, but the measurement precision deteriorates in uneven terrain as it only provides height difference relative to takeoff point
Solution Approach 1:
The Kalman filter serves as an intermediary that transforms GPS relative height data into accurate absolute height measurements. By integrating GPS data with carrier motion information and radar measurements, the system achieves precise absolute height determination without requiring complex alternative systems
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
The method effectively filters out non-ground targets and external noise, providing reliable height information for safe flight by combining radar data with motion information, reducing unnecessary obstacle avoidance warnings and improving flight safety.
Implementation Method 1
acquiring detected data of a radar; the radar being carried on the carrier
Implementation Method 2
With the all-weather detection capability of the millimeter-wave radar
Data Source
Figure 1~2A
Figure 2B~2C
Figure 3~4
AI summary
An automatic obstacle avoidance method is applied to an unmanned aerial vehicle (UAV). The UAV includes at least one radar. The method includes: acquiring detected data of the radar; calculating theoretical state information of the detection target based on motion information of the UAV; excluding invalid data from the detected data using a Kalman filtering algorithm to obtain corrected data combined with the theoretical state information; determining the height information of the UAV by correcting the detected data; and triggering an obstacle avoidance warning when the height information of the UAV is less than a pre-set height threshold value.