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Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth-Doppler Measurement.

Jianjun Huang1, Haoqiang Hu1, Li Kang1

  • 1School of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
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This study introduces a novel deep learning algorithm, the recursive downsample-convolve-interact neural network (RDCINN), for maneuvering target tracking. RDCINN effectively handles weak or missing observations, outperforming traditional methods in complex scenarios.

Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Control Systems

Background:

  • Traditional maneuvering target tracking algorithms struggle with weak/missing azimuth and Doppler observations.
  • Model mismatch and measurement noise in conventional methods cause significant prediction errors.
  • Neural network-based algorithms like RNNs, LSTMs, and Transformers show promise for improved target tracking.

Purpose of the Study:

  • To develop a deep learning algorithm capable of modeling complex nonlinear and contextual relationships in time series data for target tracking.
  • To overcome the limitations of traditional algorithms in efficiently utilizing observation information, especially during weak or non-existent observations.
  • To enhance the accuracy of maneuvering target state prediction.

Main Methods:

Keywords:
azimuth and Dopplerconvolutional neural networkdeep learning algorithmmaneuvering targets tracking

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  • Introduced the recursive downsample-convolve-interact neural network (RDCINN), a deep learning algorithm based on Convolutional Neural Networks (CNNs).
  • RDCINN downsamples time series data into subsequences and extracts multi-resolution features.
  • The architecture is designed to model nonlinear relationships between observation and target state time series and contextual relationships among time series points.

Main Results:

  • The proposed RDCINN algorithm demonstrated superior performance compared to existing algorithms in strong maneuvering target tracking scenarios.
  • The algorithm effectively utilizes combined azimuth and Doppler observations, even when they are weak or absent.
  • Experimental results validate the algorithm's capability in handling complex target motion states and improving prediction accuracy.

Conclusions:

  • RDCINN offers a significant advancement in maneuvering target tracking, particularly in challenging scenarios with limited or noisy observational data.
  • The deep learning approach effectively addresses the shortcomings of traditional algorithms, providing more robust and accurate target state predictions.
  • The algorithm's ability to model complex time series relationships makes it a promising solution for advanced tracking applications.