AirCombat-WEZ
This dataset supports the development and evaluation of Weapon Engagement Zone (WEZ) prediction models, generated through high‑fidelity air‑combat simulations. The dataset captures various scenarios and conditions representing engagements between shooter aircraft and targets.
Description
Dataset Description
Overview
This dataset supports the development and evaluation of Weapon Engagement Zone (WEZ) prediction models, generated from high‑fidelity air‑combat simulations. It captures a variety of scenarios and conditions representing engagements between shooter aircraft and targets.
Features
The dataset includes key input features influencing missile performance, derived from raw simulation parameters and feature‑engineering processes. Detailed information:
| Feature | Variable | Min | Max | Unit | Description |
|---|---|---|---|---|---|
| Shooter aircraft speed | v_s | 450 | 750 | NM/hour | Ground speed of the shooter aircraft. |
| Shooter aircraft altitude | h_s | 1,000 | 45,000 | ft | Altitude of the shooter relative to sea level. |
| Target speed | v_t | 450 | 750 | NM/hour | Ground speed of the target. |
| Target bearing | φ_t | -60 | 60 | degree | Angular position of the target relative to the shooter. |
| Target altitude difference | Δh_t | -5,000 | 5,000 | ft | Altitude difference (h_s - h_t). |
| Relative heading to bearing | Δφ_t | -180 | 180 | degree | Relative angle combining target bearing and heading (θ_t - θ_s - φ_t). |
Target Outputs
Two primary outputs per engagement scenario:
- Maximum Weapon Engagement Zone (
R_max): Farthest distance at which the missile can successfully strike the target. - No‑Escape Zone (
R_nez): Range within which the target cannot evade the missile regardless of maneuvering.
Data Generation Process
- Simulation Environment:
- Simulations performed using the high‑fidelity Aerospace Simulation Environment (ASA) built on MIXR.
- Scenarios designed to reflect realistic air‑combat conditions, ensuring diversity and representativeness.
- Bisection Search for Outputs:
R_maxandR_nezobtained via iterative bisection search, starting from 45 NM with a precision threshold of 0.2 NM.
- Experimental Design:
- Two datasets created:
- A factorial design dataset with fixed input levels for initial feature analysis (864 cases).
- A random design dataset containing 1,000 cases generated with uniformly random inputs for model training and evaluation.
- Two datasets created:
AI studio
Generate PPTs instantly with Nano Banana Pro.
Generate PPT NowAccess Dataset
Please login to view download links and access full dataset details.
Topics
Source
Organization: github
Created: 11/21/2024
Power Your Data Analysis with Premium AI Models
Supporting GPT-5, Claude-4, DeepSeek v3, Gemini and more.
Enjoy a free trial and save 20%+ compared to official pricing.