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A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

Defence Technology（防务技术） 2024年第3期33卷 463-474页

作者： Yili Wang Changsheng Li Xiaofeng Wang Nanjing University of Science and Technology Nanjing 210094JiangsuChina State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of *** address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent *** leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration *** analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,*** model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target *** maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed ***,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

关键词： Penetration fuze Temporal convolutional network(TCN) Long short-term memory(LSTM) Layer counting multi-source fusion

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multi-source Adaptive Selection and fusion for Pedestrian Dead Reckoning

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2174-2185页

作者： Yuanxun Zheng Qinghua Li Changhong Wang Xiaoguang Wang Lifeng Hu NORINCO GROUP Air Ammunition Research Institute Harbin 150030China the Research Center of Space Control and Inertial Technology Harbin Institute of TechnologyHarbin 150001China Research Center of Space Control and Inertial Technology Harbin Institute of TechnologyHarbin 150001China

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without *** proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight *** proposed algorithm exhibits good robustness, adaptability,and value on applications.

关键词： Adaptive reliability evaluation adaptive weight evaluation Kalman filter(KF) multi-source fusion optimal set selection

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维普期刊数据库

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Robust Magnetic Field Loop Closure Detection for Low-Cost Robot’s Localization and Mapping

Robust Magnetic Field Loop Closure Detection for Low-Cost Ro...

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第十三届中国卫星导航年会

作者： Bin Yao Wen Li Dongyan Wei Xinchun Ji Wenchao Zhang Aerospace Information Research Institute Chinese Academy of Sciences University of Chinese Academy of Sciences

The robot realizes the perception of its own location through the fusion of multiple navigation sources. However, there are limits in application scenarios and cost for various navigation sources such as vision, laser, and inertial measurement unit. Therefore, a robust magnetic field loop closure detection for low-cost robot’s localization and mapping method is proposed in this paper. In front-end, The MEMS inertial measurement unit and wheel odometer are fused to estimate the pose state of the robot, and magnetic field sequence loop closure is detected in DTW algorithm, and a general graph optimization method is applied to optimize the pose in back-end. Experiments show that this method can effectively detect the magnetic sequence loop closures and eliminate the cumulative error in inertial recurrence, realize the long-time continuous indoor positioning of the robot.

关键词： Magnetic Sequence Loop Closure Graph Optimization Robot SLAM multi-source fusion

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