🚀 MCST: Maneuver Compensation Strong Tracker

MCST is an adaptive deep learning-based radar target tracking algorithm designed to handle high-speed and highly maneuverable targets. It leverages a Bi-LSTM architecture with a dual-level attention mechanism and a custom Maneuver Compensation Unit (MCU) to achieve robust and accurate tracking performance, even under extreme target dynamics and observation uncertainties.

📄 Accepted at IEEE Transactions on Aerospace and Electronic Systems
📚 Read the paper

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✨ Features

• 🚀 Supports tracking of hypersonic, highly maneuverable targets
• 🧠 Built upon a Bi-LSTM architecture with:
  • Predictor & Updater modules inspired by Kalman filtering
  • Dual-level attention module for temporal and sample weighting
  • Maneuver Compensation Unit (MCU) using FFT-based residual analysis
• 🔄 Outputs both state estimates and uncertainty covariance matrices
• 📈 Trained and tested on a custom 10,000-trajectory dataset
• 📦 Modular and extensible codebase

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🖼️ Model Architecture

A hybrid Bi-LSTM + attention framework with maneuver compensation and uncertainty modeling.

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📁 Project Structure

MCST/
├── config.py            # Model and training configs
├── data/                # Dataset and preprocessing scripts
├── models/              # Model definitions (Predictor, MCU, Updater)
├── utils/               # Utilities (normalization, trajectory initialization)
├── train/               # Training pipeline
├── evaluation/          # Evaluation and metrics
├── log/                 # log
├── main.py

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🧪 Quick Start

1. Setup Environment

conda create -n mcst python=3.9
conda activate mcst

2. Download Dataset

You can download from https://github.com/Shen-kl/OneManeuveringTarget3D.

3. Train the Model

python main.py 

4. Evaluate

python evaluate.py --checkpoint checkpoints/mcst_best.pth

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📊 Dataset Overview

MCST is trained on a synthetic dataset of 10,000 trajectories, simulating targets with:

• Velocities up to Mach 5
• Acceleration range: 3g–7g
• Motion models: CV, CA, HCT, FCT
• Sampling interval: 0.4 seconds
• Noise added in spherical coordinates, then converted to Cartesian

Each trajectory contains 100 frames, with 3 model transitions per trajectory.

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📈 Performance

MCST outperforms several SOTA model-based tracking algorithms, including:

• Single model: FSTCKF ,and RNSTF
• Multiple models: HGMM, and RIMM

Especially in miss detection and rapid maneuver scenarios.

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📜 Citation

If you find this work helpful, please cite our paper:

@article{shen2024mcst,
  title={MCST: An adaptive tracking algorithm for high-speed and highly maneuverable targets based on bidirectional LSTM network},
  author={Shen, Kailun and Yuan, Weiming and Yan, Junkun and Ma, Keke},
  journal={IEEE Transactions on Aerospace and Electronic Systems},
  year={2024},
  publisher={IEEE}
}

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