Huang, J.; Hu, H.; Kang, L. Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth–Doppler Measurement. Sensors2024, 24, 263.
Huang, J.; Hu, H.; Kang, L. Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth–Doppler Measurement. Sensors 2024, 24, 263.
Huang, J.; Hu, H.; Kang, L. Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth–Doppler Measurement. Sensors2024, 24, 263.
Huang, J.; Hu, H.; Kang, L. Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth–Doppler Measurement. Sensors 2024, 24, 263.
Abstract
In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target tracking algorithms. Additionally, traditional target-tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target-tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and Transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample-convolve-interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into sub-sequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target-tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.
Keywords
azimuth and Doppler; convolutional neural network; deep learning algorithm; maneuvering targets tracking
Subject
Computer Science and Mathematics, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
