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| Target State Estimation Algorithms Under Partially Known State Space Models |
| ZHOU Tianlong1， YAO Fangjing2， RAO Weixiong1 |
| 1.School of Computer Science and Technology， Tongji University， Shanghai 201804， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China |
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Abstract Target state estimation is a key problem of collaborative detection. Traditional model-based state estimation algorithms perform poorly under partially known state-space models. In contrast, existing neural network-based state estimation algorithms have low interpretability, making it difficult for them to effectively apply in real-world scenarios (e.g., collaborative detection). This paper proposed a highly interpretable neural network-based state estimation and fusion framework to address the above problems. First, a Kalman filter neural network model was employed to obtain high interpretability by approximating the model-based state estimation algorithm. Second, a learnable weighted robust fusion framework was introduced to improve the fusion accuracy under partially known state space models. Experimental results show that the proposed method performs high target state estimation accuracy and robustness in simulation environments and real datasets, significantly outperforming traditional methods.
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Received: 12 March 2025
Published: 15 July 2025
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| [2] |
LI Keyong, LI Chuchen, CAI Yunze. Key Technologies for Collaborative Situational Awareness Based on Swarm-to-Swarm Interception[J]. Air & Space Defense, 2025, 8(1): 1-9. |
| [3] |
ZANG Hongyan, XIE Xiaolong, XU Yazhou, TAO Ye, GAO Changsheng. Hypersonic Maneuvering Target State Estimation Algorithm Based on Recurrent Neural Network[J]. Air & Space Defense, 2024, 7(4): 88-98. |
| [4] |
CHEN Wanchun, YUAN Wenjie, YU Qi, LIU Xiaoming, XU Zeng. Research on Target Classification and Trajectory Prediction Method of Airborne Interceptor Weapons Under Low Information Condition[J]. Air & Space Defense, 2024, 7(4): 81-87. |
| [5] |
HUANG Quanyin, CAI Yichao, LI Hao, TANG Xiao, WANG Chenyang. Adaptive Trajectory Prediction Method Based on
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| [6] |
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| [7] |
NIE Rui, WANG Hongru. Fault Diagnosis of UAV Formation Actuator Based on Neural Network Observer[J]. Air & Space Defense, 2022, 5(2): 32-41. |
| [8] |
DAI Shaohuai, YANG Gewen, YU Wen, WU Xiangshang. Active Suppression Jamming Recognition Based on RBF Neural Network[J]. Air & Space Defense, 2022, 5(1): 102-107. |
| [9] |
WEI Zhifei, SONG Quanhong, LI Fang, YANG Qingyu, WANG Aihua. Research on Object Detection Algorithm Based on Neural Network Model Compression Technique[J]. Air & Space Defense, 2021, 4(4): 107-112. |
| [10] |
QI Shengyong, ZANG Yuejin, LYU Guoyun, DU Ming. Research on Air Target Image Generation Algorithm Based on Generative Adversarial Networks[J]. Air & Space Defense, 2021, 4(2): 67-73. |
| [11] |
LONG Teng, LIU Zhenyu, SHI Renhe, WANG Shengyin. Neural Network Based Air Defense Weapon Target Intelligent Assignment Method[J]. Air & Space Defense, 2021, 4(1): 1-7. |
| [12] |
WU Pengfei, SHI Ran, YI Zhikun, WU Zhijie, QIU Cunkai, FU Weiping. Research on α Drive Control for Solar Array Based on Improved Neural Network PID[J]. Air & Space Defense, 2018, 1(4): 8-17. |
| [13] |
Wei Liming, Li Xiaolong, Zhao Zheng, Du Sha, Wang Jiuzhou. Application of Neural Network in Penetration of Air-breathing Hypersonic Missile[J]. Air & Space Defense, 2018, 1(2): 14-17. |
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2025, Vol. 8 
