Dynamic Target Impact Point Sweetener for Aerial Vehicles
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Solution Overview
Problem
Guided missiles face challenges in accurately hitting the most vulnerable points on targets, especially in environments where communication with a central node is denied due to hostile conditions, leading to reduced effectiveness in strategic impact.
Innovation Solution
A system and method that enable guided missiles to generate a composite 3D image of a target using radar data from multiple aerial vehicles, allowing for dynamic determination of the most vulnerable impact point without relying on a central node, by acting as either a master or slave vehicle to share and process radar data for tomographic image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If guided missiles use sophisticated guidance systems with central node communication, then they can receive guidance updates and improve targeting accuracy, but they become vulnerable to communication denial in hostile environments
Solution Approach 1:
The missile performs self-service by autonomously generating its own 3D target model and identifying impact points using onboard processing of radar data from multiple vehicles, eliminating dependence on external central node communication while maintaining high targeting accuracy
Solution Approach 2:
Radar data from multiple aerial vehicles serves as an intermediary, providing the necessary target information to the missile without requiring direct communication with a central node, thus bridging the information gap in denied environments
2Measurement precision
If multiple aerial vehicles collect and share radar data for composite 3D image generation, then the accuracy of target representation improves, but the system complexity increases
Solution Approach 1:
The system segments the complex task of 3D target modeling by assigning different roles to multiple aerial vehicles (master and slave), where each vehicle contributes specific radar data and the master vehicle performs the composite image generation, distributing the computational burden and simplifying individual vehicle design
Solution Approach 2:
The master vehicle performs multiple functions including receiving radar data from slave vehicles, generating composite 3D images, identifying target characteristics, and determining impact points, reducing the need for separate specialized systems and overall system complexity
3Reliability
If missiles are designed with advanced guidance capabilities to hit vulnerable points, then the effectiveness of each missile increases, but the cost and complexity of the missile system increases
Solution Approach 1:
The system performs preliminary action by generating the composite 3D target model and identifying vulnerable impact points before the missile reaches the target, using radar data collected during the approach phase, allowing the missile to be simpler while still achieving high effectiveness through pre-computed guidance information
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
Enables improved accuracy in targeting the most vulnerable areas of a target, even in communication-denied environments, by allowing aerial vehicles to autonomously generate and share composite 3D images, enhancing the likelihood of successful strategic impact.
Implementation Method 1
radar data received at the apparatus from other aerial vehicles collecting projections over an area in which the target is located and based on radar data collected by an aerial vehicle in which the apparatus is located
Implementation Method 2
generate a composite multi-dimensional representation of a target based on radar data received at the apparatus from other aerial vehicles collecting projections over an area in which the target is located
Data Source
AI summary
Methods and apparatus for providing a dynamic target impact point sweetener is disclosed. An example method includes identifying a target based on a composite three-dimensional image generated based on data received from a first aerial vehicle acting as a master vehicle and a second aerial vehicle acting as a slave vehicle; changing a role of the first aerial vehicle to the slave vehicle; changing the role of the second aerial vehicle to the master vehicle; and causing, using the second aerial vehicle acting as the master vehicle, a third vehicle to attack the target based on the identity of the target.