Öz
This paper presents a novel federated signal processing framework for multi-target tracking in distributed radar/sonar systems. Each sensor node independently tracks targets using an Extended Kalman Filter (EKF) that processes nonlinear range–bearing measurements, thereby generating refined state estimates and uncertainty covariances without transmitting raw sensor data. Instead, only these processed outputs—augmented by dynamically updated trust factors—are communicated to a central fusion center. The fusion center aggregates the local estimates via an inverse-covariance weighting scheme augmented by adaptive trust weights, yielding globally consistent target tracks with improved accuracy and robustness. Simulation results demonstrate that the proposed federated EKF approach significantly reduces communication overhead while maintaining high tracking performance under heterogeneous sensor conditions.
Anahtar Kelimeler
Multi-target Tracking Federated Learning Extended Kalman Filter Sensor Fusion Trust Weights
Kaynakça
- Boiteau, S., Vanegas, F., & Gonzalez, F. (2024). Framework for autonomous UAV navigation and target detection in global‐navigation‐satellite‐system‐denied and visually degraded environments. Remote Sensing, 16, 471. https://doi.org/10.3390/rs16030471
- Chen, B., Pei, X., & Chen, Z. (2019). Research on target detection based on distributed track fusion for intelligent vehicles. Sensors, 20, 56. https://doi.org/10.3390/s20010056
- Grigorescu, S., Trăsnea, B., Cocias, T., & Măceşanu, G. (2019). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37, 362–386. https://doi.org/10.1002/rob.21918
- Goodin, C., Doude, M., Hudson, C. R., & Carruth, D. W. (2018). Enabling off‐road autonomous navigation—simulation of lidar in dense vegetation. Electronics, 7, 154. https://doi.org/10.3390/electronics7090154
- Helgesen, K., Brekke, E., Helgesen, H. H., & Engelhardtsen. (2019). Sensor combinations in heterogeneous multi‐sensor fusion for maritime target tracking. In 2019 22nd International Conference on Information Fusion (FUSION) (pp. 1–9). https://doi.org/10.23919/fusion43075.2019.9011297
- Lee, S., Yuan, Z., Petrunin, I., & Shin, H. (2024). Impact analysis of time synchronization error in airborne target tracking using a heterogeneous sensor network. Drones, 8, 167. https://doi.org/10.3390/drones8050167
- Lee, W., Cho, H., Hyeong, S., & Chung, W. (2019). Practical modeling of GNSS for autonomous vehicles in urban environments. Sensors, 19, 4236. https://doi.org/10.3390/s19194236
- Liu, H. (2020). Autonomous navigation for Mars exploration. In Mars Exploration: A Step Forward. https://doi.org/10.5772/intechopen.92093
- Omori, M., Yoshitake, H., & Shino, M. (2024). Autonomous navigation for personal mobility vehicles considering passenger tolerance against approaching pedestrians. https://doi.org/10.21203/rs.3.rs-3825758/v1
- Reddy, B. N. B., Pardhasaradhi, B., Srinath, G., & Srihari, P. (2022). Distributed fusion of optimally quantized local tracker estimates for underwater wireless sensor networks. IEEE Access, 10, 38982–38998. https://doi.org/10.1109/access.2022.3164515
- Suleymanov, S., & Bayramov, A. A. (2023). Artificial intelligence application for unmanned aerial vehicle navigation. Modeling Control and Information Technologies, 21–24. https://doi.org/10.31713/mcit.2023.004
- Wang, C., & Yu, H. (2023). Multiple attitude estimation models based on a pressure sensor array. Journal of Physics: Conference Series, 2456, 012001. https://doi.org/10.1088/1742-6596/2456/1/012001
- Wei, W., Gao, Z., Gao, S., & Jia, K. (2018). A sins/srs/gns autonomous integrated navigation system based on spectral redshift velocity measurements. Sensors, 18, 1145. https://doi.org/10.3390/s18041145
- Wu, Q. (2024). Research on autonomous mobile robot maze navigation problem based on Dijkstra’s algorithm. Applied and Computational Engineering, 39, 10–17. https://doi.org/10.54254/2755-2721/39/20230570
- Yue, P., Xin, J., Zhang, Y., Lu, Y., & Shan, M. (2024). Semantic-driven autonomous visual navigation for unmanned aerial vehicles. IEEE Transactions on Industrial Electronics, 71, 14853–14863. https://doi.org/10.1109/tie.2024.3363761
- Yu, P., Wei, W., Li, J., Wang, F., Zhang, L., & Chen, Z. (2023). An improved autonomous inertial-based integrated navigation scheme based on vehicle motion recognition. IEEE Access, 11, 104806–104816. https://doi.org/10.1109/access.2023.3318548
- Zhang, K., Wang, Z., Guo, L., Peng, Y., & Zheng, Z. (2020). An asynchronous data fusion algorithm for target detection based on multi-sensor networks. IEEE Access, 8, 59511–59523. https://doi.org/10.1109/access.2020.2982682
Öz
Bu çalışma, dağıtık radar/sonar sistemlerinde çoklu hedef takibi için yeni bir federatif sinyal işleme çerçevesi sunmaktadır. Her bir sensör düğümü, ham sensör verilerini iletmeden, doğrusal olmayan menzil–yön ölçümlerini işleyen Genişletilmiş Kalman Filtresi (EKF) kullanarak hedefleri bağımsız olarak takip eder; böylece daha hassas durum tahminleri ve belirsizlik kovaryansları elde edilir. Ham sensör verileri yerine, yalnızca dinamik olarak güncellenen güven faktörleriyle zenginleştirilmiş işlenmiş çıktılar merkezi bir füzyon merkezine iletilir. Füzyon merkezi, yerel tahminleri adaptif güven ağırlıklarıyla desteklenen ters-kovaryans ağırlıklandırma şeması aracılığıyla birleştirerek, daha yüksek doğruluk ve dayanıklılığa sahip küresel olarak tutarlı hedef izleri elde eder. Simülasyon sonuçları, önerilen federatif EKF yaklaşımının heterojen sensör koşulları altında yüksek takip performansını sürdürürken iletişim yükünü önemli ölçüde azalttığını göstermektedir.
Anahtar Kelimeler
Çoklu Hedef Takibi Federatif Öğrenme Genişletilmiş Kalman Filtresi Sensör Füzyonu Güven Ağırlıkları
Kaynakça
- Boiteau, S., Vanegas, F., & Gonzalez, F. (2024). Framework for autonomous UAV navigation and target detection in global‐navigation‐satellite‐system‐denied and visually degraded environments. Remote Sensing, 16, 471. https://doi.org/10.3390/rs16030471
- Chen, B., Pei, X., & Chen, Z. (2019). Research on target detection based on distributed track fusion for intelligent vehicles. Sensors, 20, 56. https://doi.org/10.3390/s20010056
- Grigorescu, S., Trăsnea, B., Cocias, T., & Măceşanu, G. (2019). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37, 362–386. https://doi.org/10.1002/rob.21918
- Goodin, C., Doude, M., Hudson, C. R., & Carruth, D. W. (2018). Enabling off‐road autonomous navigation—simulation of lidar in dense vegetation. Electronics, 7, 154. https://doi.org/10.3390/electronics7090154
- Helgesen, K., Brekke, E., Helgesen, H. H., & Engelhardtsen. (2019). Sensor combinations in heterogeneous multi‐sensor fusion for maritime target tracking. In 2019 22nd International Conference on Information Fusion (FUSION) (pp. 1–9). https://doi.org/10.23919/fusion43075.2019.9011297
- Lee, S., Yuan, Z., Petrunin, I., & Shin, H. (2024). Impact analysis of time synchronization error in airborne target tracking using a heterogeneous sensor network. Drones, 8, 167. https://doi.org/10.3390/drones8050167
- Lee, W., Cho, H., Hyeong, S., & Chung, W. (2019). Practical modeling of GNSS for autonomous vehicles in urban environments. Sensors, 19, 4236. https://doi.org/10.3390/s19194236
- Liu, H. (2020). Autonomous navigation for Mars exploration. In Mars Exploration: A Step Forward. https://doi.org/10.5772/intechopen.92093
- Omori, M., Yoshitake, H., & Shino, M. (2024). Autonomous navigation for personal mobility vehicles considering passenger tolerance against approaching pedestrians. https://doi.org/10.21203/rs.3.rs-3825758/v1
- Reddy, B. N. B., Pardhasaradhi, B., Srinath, G., & Srihari, P. (2022). Distributed fusion of optimally quantized local tracker estimates for underwater wireless sensor networks. IEEE Access, 10, 38982–38998. https://doi.org/10.1109/access.2022.3164515
- Suleymanov, S., & Bayramov, A. A. (2023). Artificial intelligence application for unmanned aerial vehicle navigation. Modeling Control and Information Technologies, 21–24. https://doi.org/10.31713/mcit.2023.004
- Wang, C., & Yu, H. (2023). Multiple attitude estimation models based on a pressure sensor array. Journal of Physics: Conference Series, 2456, 012001. https://doi.org/10.1088/1742-6596/2456/1/012001
- Wei, W., Gao, Z., Gao, S., & Jia, K. (2018). A sins/srs/gns autonomous integrated navigation system based on spectral redshift velocity measurements. Sensors, 18, 1145. https://doi.org/10.3390/s18041145
- Wu, Q. (2024). Research on autonomous mobile robot maze navigation problem based on Dijkstra’s algorithm. Applied and Computational Engineering, 39, 10–17. https://doi.org/10.54254/2755-2721/39/20230570
- Yue, P., Xin, J., Zhang, Y., Lu, Y., & Shan, M. (2024). Semantic-driven autonomous visual navigation for unmanned aerial vehicles. IEEE Transactions on Industrial Electronics, 71, 14853–14863. https://doi.org/10.1109/tie.2024.3363761
- Yu, P., Wei, W., Li, J., Wang, F., Zhang, L., & Chen, Z. (2023). An improved autonomous inertial-based integrated navigation scheme based on vehicle motion recognition. IEEE Access, 11, 104806–104816. https://doi.org/10.1109/access.2023.3318548
- Zhang, K., Wang, Z., Guo, L., Peng, Y., & Zheng, Z. (2020). An asynchronous data fusion algorithm for target detection based on multi-sensor networks. IEEE Access, 8, 59511–59523. https://doi.org/10.1109/access.2020.2982682
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Mühendisliği (Diğer) |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Gönderilme Tarihi | 20 Mart 2025 |
| Kabul Tarihi | 6 Ağustos 2025 |
| Yayımlanma Tarihi | 3 Aralık 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 28 Sayı: 4 |