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🔥 Content Introduction
1. Preparations Before Running the Code: Environment and File Configuration
Before executing the code, three key preparations must be completed to avoid failures due to missing configurations:
1. Folder Structure and File Placement
The code relies on specific path model files and ACMI data files, which must be organized in the following structure:
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Your working directory/
├─ trajectory_vers2/ # Model folder (must be created manually)
│ └─ 80jet.mat # Aircraft3D model file (must be placed here)
└─ control.txt.acmi # TacvieexportedACMI data file (at the same level as the working directory)
- Model file acquisition:80jet.mat is a 3D model file of a jet aircraft, containing vertex (V), face (F), and color (C) data. If it cannot be obtained, it can be generated in the following ways:
- Create the aircraft model using 3D modeling software like SolidWorks and export it in STL format;
- Use MATLAB’s stlread function to read the STL file, extract vertex and face data, and manually construct V(n×3), F (m×3) matrices, and the color matrix C can be set to a uniform RGB value (e.g., C = ones(n,3)*[0.8,0.2,0.2] represents red).
- ACMI file format verification: Ensure control.txt.acmi contains aircraft position and attitude data, with a typical valid line format as follows:
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1,Time=116.4074|39.9042|500|5|0|30,Name=F-16,Color=red
Where “1” is the aircraft ID, and after “Time=” are “Longitude | Latitude | Altitude | Pitch Angle (degrees) | Roll Angle (degrees) | Yaw Angle (degrees)”, followed by name and color.
2. MATLAB Version and Toolbox Dependencies
- Version Requirements: The code uses startsWith, split and other functions, requiring MATLAB 2016b or later (recommended 2017b+, better compatibility);
- Toolbox Dependencies: No additional toolboxes are required, only basic MATLAB functions ( fopen, patch, time r etc.), the default environment can run.
3. Key Parameter Pre-Adjustment
Based on the actual scale of the ACMI data, adjust the “coordinate scaling factor” in the code in advance to avoid overly dense or sparse aircraft trajectories during visualization:
- Latitude and Longitude Scaling:If the latitude and longitude range of the ACMI data is small (e.g., within 1km), reduce x_m and y_m scaling factors (e.g., change 150 to 50, 50 to 20);
- Height Scaling:If the height data unit is “meters” and the range is 0-1000m, z_m = z * 290 may cause excessive height in the vertical direction, it can be adjusted to z_m = z * 0.5;
- Model Scaling:If the 80jet.mat model is too large or too small, modify scale_factor (e.g., change 9000 to 5000 or 12000), ensuring the aircraft model is clearly visible in the 3D view.
⛳️ Running Results
📣 Some Code
🔗 References
🎈 Some theories reference online literature; if there is any infringement, please contact the blogger for deletion.
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🏆 The team specializes in guiding customized MATLAB simulations in various research fields, helping to realize research dreams:
🌟 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, 3D packing, logistics site selection, cargo location optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, container ship loading optimization, pump combination optimization, medical resource allocation optimization, facility layout optimization, visual field base station and drone site selection optimization, knapsack problem, wind farm layout, time slot allocation optimization, optimal distribution of distributed generation units, multi-stage pipeline maintenance, factory-center-demand point three-level site selection problem, emergency life material distribution center site selection, base station site selection, road lamp post arrangement, hub node deployment, transmission line typhoon monitoring devices, container scheduling, unit optimization, investment optimization portfolio, cloud server combination optimization, antenna linear array distribution optimization, CVRP problem, VRPPD problem, multi-center VRP problem, multi-layer network VRP problem, multi-center multi-vehicle VRP problem, dynamic VRP problem, two-layer vehicle routing planning (2E-VRP), electric vehicle routing planning (EVRP), oil-electric hybrid vehicle routing planning, hybrid flow shop problem, order splitting scheduling problem, bus scheduling optimization problem, flight shuttle vehicle scheduling problem, site selection path planning problem, port scheduling, port bridge scheduling, parking space allocation, airport flight scheduling, leak source location, cold chain, time window, multi-parking lot, etc., site selection optimization, port bridge scheduling optimization, traffic impedance, redistribution, parking space allocation, airport flight scheduling, communication upload and download allocation optimization.
🌟 Time series, regression, classification, clustering, and dimensionality reduction in machine learning and deep learning
2.1 BP time series, regression prediction, and classification
2.2 ENS voice neural network time series, regression prediction, and classification
2.3 SVM/CNN-SVM/LSSVM/RVM support vector machine series time series, regression prediction, and classification
2.4 CNN|TCN|GCN convolutional neural network series time series, regression prediction, and classification
2.5 ELM/KELM/RELM/DELM extreme learning machine series time series, regression prediction, and classification
2.6 GRU/Bi-GRU/CNN-GRU/CNN-BiGRU gated neural network time series, regression prediction, and classification
2.7 Elman recurrent neural network time series, regression prediction, and classification
2.8 LSTM/BiLSTM/CNN-LSTM/CNN-BiLSTM long short-term memory neural network series time series, regression prediction, and classification
2.9 RBF radial basis function neural network time series, regression prediction, and classification
2.10 DBN deep belief network time series, regression prediction, and classification
2.11 FNN fuzzy neural network time series, regression prediction
2.12 RF random forest time series, regression prediction, and classification
2.13 BLS broad learning system time series, regression prediction, and classification
2.14 PNN pulse neural network classification
2.15 Fuzzy wavelet neural network prediction and classification
2.16 Time series, regression prediction, and classification
2.17 Time series, regression prediction, and classification
2.18 XGBOOST ensemble learning time series, regression prediction, and classification
2.19 Transform various combinations time series, regression prediction, and classification
Covering wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal identification, subway parking accurate prediction, transformer fault diagnosis.
🌟 In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing.
🌟 In path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), drone 3D path planning, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problem, electric vehicle routing planning (EVRP), two-layer vehicle routing planning (2E-VRP), oil-electric hybrid vehicle routing planning, ship trajectory planning, full path planning, warehouse patrol, bus time scheduling, reservoir scheduling optimization, multimodal optimization.
🌟 In drone applications
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, online optimization of drone safe communication trajectories, vehicle collaborative drone path planning.
🌟 In communication
Sensor deployment optimization, communication protocol optimization, routing optimization, target location optimization, Dv-Hop location optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI location optimization, underwater communication, communication upload and download allocation.
🌟 In signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, electromyography signals, electroencephalography signals, signal timing optimization, electrocardiogram signals, DOA estimation, encoding and decoding, variational mode decomposition, pipeline leakage, filters, digital signal processing + transmission + analysis + denoising, digital signal modulation, bit error rate, signal estimation, DTMF, signal detection.
🌟 In power systems
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity, electric/cold/heat load forecasting, power equipment fault diagnosis, battery management system (BMS) SOC/SOH estimation (particle filter/Kalman filter), multi-objective optimization in power system scheduling, photovoltaic MPPT control algorithm improvement (perturbation observation method/incremental conductance method), electric vehicle charging and discharging optimization, microgrid day-ahead optimization, energy storage optimization, household electricity optimization, supply chain optimization.
🌟 In cellular automata
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion.
🌟 In radar
Kalman filter tracking, trajectory association, trajectory fusion, SOC estimation, array optimization, NLOS identification.
🌟 In workshop scheduling
Zero-wait flow shop scheduling problem NWFSP , permutation flow shop scheduling problem PFSP , hybrid flow shop scheduling problem HFSP , zero idle flow shop scheduling problem NIFSP, distributed permutation flow shop scheduling problem DPFSP, blocking flow shop scheduling problem BFSP.
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