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| Citation: | LI Wenna, ZHANG Shunsheng, and WANG Wenqin. Multitarget-tracking method for airborne radar based on a transformer network[J]. Journal of Radars, 2022, 11(3): 469–478. doi: 10.12000/JR22009
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Multitarget-tracking Method for Airborne Radar Based on a Transformer Network
doi: 10.12000/JR22009
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Abstract
Conventional multitarget-tracking data association algorithms must have prior information, such as the target motion model and clutter density. However, such prior information cannot be obtained timely and accurately before tracking. To address this issue, a data association algorithm for multitarget tracking based on a transformer network is proposed. First, considering that the radar may not perform accurate detected the target, virtual measurements are performed to re-establish the data association model. Thus, a data association method based on the transformer network is proposed to solve the matching problem of multitargets and multimeasurements. Moreover, a loss function combining Masked Cross entropy loss and Dice (MCD) loss is designed to optimize the network parameters. Simulation data and real measurement data results show that the proposed algorithm outperforms classic data association algorithms and algorithms based on bidirectional long short-term memory network under varying detection probability conditions. -
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References
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- Figure 1. A diagram of the matching relationship between each target and all measurements
- Figure 2. Attention mechanism and Transformer-DA network structure
- Figure 3. Multitarget-tracking framework based on Transformer-DA
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Figure 4. Simulation trajectory and measurement when
${E_\lambda } = 80$ ${p_d} = 0.99$ -
Figure 5. Tracking results of different algorithms when
${E_\lambda } = 80$ ${p_d} = 0.99$ -
Figure 6. OSPA distance of different algorithms when
${E_\lambda } = 80$ ${p_d} = 0.99$ -
Figure 7. Real trajectory and simulation measurements when
${E_\lambda } = 80$ ${p_d} = 0.99$ -
Figure 8. Tracking results of different algorithms when
${E_\lambda } = 80$ ${p_d} = 0.99$ -
Figure 9. OSPA distance of different algorithms when
${E_\lambda } = 80$ ${p_d} = 0.99$