Adaptive TPHD Tracking for Individuals Within a Bird Flock Using Doppler Features.
Saved in:
| Title: | Adaptive TPHD Tracking for Individuals Within a Bird Flock Using Doppler Features. |
|---|---|
| Authors: | Ni, Na1 (AUTHOR), Guo, Yuhang2 (AUTHOR), Wang, Zhiqin2,3 (AUTHOR) wangzhiqin@caict.ac.cn, Jiang, Qi1 (AUTHOR), Li, Weidong1,2 (AUTHOR), Wang, Rui1,3 (AUTHOR), Hu, Cheng1,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1538. 23p. |
| Subjects: | Doppler effect, Multiple target tracking, Tracking radar, Missing data (Statistics), Swarming (Zoology) |
| Abstract: | Highlights: What are the main findings? A Doppler temporal contrastive network is developed to learn micro-Doppler representations of bird targets, and is fused with kinematic parameters using XGBoost to improve the association accuracy in dense flock scenarios. The adaptive detection probability model and target birth mechanism are incorporated into the TPHD filter, reducing track fragmentation and false initialization under incomplete measurements and clutter interference. What are the implications of the main findings? The Doppler feature can provide complementary discriminative information beyond kinematic parameters, enhancing tracking performance within a bird flock. The proposed framework provides a solution for robust radar tracking under incomplete measurements, with applicability to real-world bird flock monitoring. Tracking multiple targets within a group is a challenging task in the radar field, especially for a bird flock. Targets in a group are usually closely spaced and exhibit similar characteristics. Additionally, the tracking radar typically employs a narrow beam to achieve a high range–angular resolution, resulting in incomplete measurements within the limited beamwidth. These factors lead to false association and track fragmentation in target tracking. However, in addition to kinematic characteristics, birds exhibit temporally correlated micro-Doppler signatures because of their wingbeat behavior, which can be utilized in target tracking. Therefore, this paper proposes an adaptive TPHD tracking method using Doppler features. First, a Doppler temporal contrastive network is designed to learn the micro-Doppler representation for the association of birds. Then, the learned feature is fused with kinematic parameters, using XGBoost to guide the weight update in the filter. Moreover, adaptive mechanisms are incorporated into the TPHD filter to achieve stable tracking under incomplete measurements. Simulation and experimental results verified the effectiveness of the proposed method and showed better tracking performance than the competing method. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A Doppler temporal contrastive network is developed to learn micro-Doppler representations of bird targets, and is fused with kinematic parameters using XGBoost to improve the association accuracy in dense flock scenarios. The adaptive detection probability model and target birth mechanism are incorporated into the TPHD filter, reducing track fragmentation and false initialization under incomplete measurements and clutter interference. What are the implications of the main findings? The Doppler feature can provide complementary discriminative information beyond kinematic parameters, enhancing tracking performance within a bird flock. The proposed framework provides a solution for robust radar tracking under incomplete measurements, with applicability to real-world bird flock monitoring. Tracking multiple targets within a group is a challenging task in the radar field, especially for a bird flock. Targets in a group are usually closely spaced and exhibit similar characteristics. Additionally, the tracking radar typically employs a narrow beam to achieve a high range–angular resolution, resulting in incomplete measurements within the limited beamwidth. These factors lead to false association and track fragmentation in target tracking. However, in addition to kinematic characteristics, birds exhibit temporally correlated micro-Doppler signatures because of their wingbeat behavior, which can be utilized in target tracking. Therefore, this paper proposes an adaptive TPHD tracking method using Doppler features. First, a Doppler temporal contrastive network is designed to learn the micro-Doppler representation for the association of birds. Then, the learned feature is fused with kinematic parameters, using XGBoost to guide the weight update in the filter. Moreover, adaptive mechanisms are incorporated into the TPHD filter to achieve stable tracking under incomplete measurements. Simulation and experimental results verified the effectiveness of the proposed method and showed better tracking performance than the competing method. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101538 |
