Aircraft Acoustic Machine Perception for Severe-Weather Detection
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Solution Overview
Problem
Existing perception systems in vehicles, such as aircraft, rely heavily on modalities like LIDAR, radar, and cameras, which can be expensive and less robust in severe weather conditions, leading to reduced confidence in object detection and information determination.
Innovation Solution
An acoustic-based machine perception system for aircraft that uses an array of microphones to acquire acoustic data, combined with LIDAR, radar, and camera data for training a machine learning model to predict object information like azimuth, range, and type, reducing reliance on traditional modalities and improving detection confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional perception systems use LIDAR, radar, and camera modalities, then object detection capability is achieved, but system cost increases and reliability decreases in severe weather conditions
Solution Approach 1:
The patent substitutes acoustic sensing (using microphones to detect sound waves) for traditional optical and electromagnetic sensing systems (LIDAR, radar, cameras). Acoustic waves are not blocked by severe weather conditions like rain, fog, or snow, allowing the system to maintain reliable object detection when traditional modalities fail. The acoustic sensor array detects objects by analyzing sound reflections and patterns, providing a complementary or alternative sensing mechanism that operates independently of weather conditions.
2Measurement precision
If multiple sensor modalities (LIDAR, radar, camera) are used, then detection accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent uses acoustic signal processing to create a virtual representation of the visual scene, effectively copying the object detection function achieved by complex multi-modal systems through a simpler acoustic approach. The acoustic sensor array captures sound reflections that contain spatial and object information, which is then processed to generate object detections similar to what LIDAR or camera systems would provide, but with fewer and simpler components.
Solution Approach 2:
The acoustic sensor array serves multiple functions: it detects object presence, determines object location, identifies object type through acoustic signature recognition, and operates across various weather conditions. A single acoustic sensing system replaces what would traditionally require multiple specialized sensors (LIDAR for depth, radar for velocity, cameras for visual identification), providing multi-functionality with reduced complexity.
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 system enhances object detection confidence by autonomously training a machine learning model, reducing human intervention, and can operate effectively in various weather conditions, supplementing or replacing traditional perception systems.
Implementation Method 1
an acoustic-based machine perception system for an aircraft... causes an array of microphones arranged on the aircraft to acquire acoustic data
Data Source
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AI summary
In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.