Transformer-based tracking Network for Maneuvering Targets

Zhang, Yushu; Li, Gang; Zhang, Xiaoping; He, Yong

doi:10.1109/icassp49357.2023.10094765

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Furthermore, the articles [16][17][18] focus on addressing the challenges that arise from the inherent uncertainty in both maneuvering target states and the measurement information faced by traditional target tracking algorithms and presenting methodologies that better model the long-term dependence among sequence data through the gating mechanism. Subsequently, the articles [19,20] address the limitation of the long short-term memory (LSTM) model in capturing the global nature of the target maneuvering state by proposing the use of the transformer architecture which captures both long-term and short-term dependence of the target state, further enhance the accuracy of target tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Jianjun,

Hu,

2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Jianjun,

Hu,

2023

Preprint

View full text Add to dashboard Cite

show abstract

A Novel Transformer-Aided Multiple Model Algorithm for Maneuvering Target Tracking

Zhao,

Peng,

Cheng

et al. 2024

IEEE Sensors J.

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

Huang,

Hu,

Kang

2024

Sensors

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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 subsequences 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.

show abstract

Transformer-based tracking Network for Maneuvering Targets

Cited by 3 publications

References 17 publications

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

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

A Novel Transformer-Aided Multiple Model Algorithm for Maneuvering Target Tracking

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

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