Active switching multiple model method for tracking a noncooperative gliding flight vehicle

Zheng, Tianyu; Yao, Yu; He, Fenghua; Ji, Denggao; Zhang, Xinran

doi:10.1007/s11432-019-1515-2

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…Herein, a convolutional neural network (CNN) and a long short-term memory network (LSTM) are combined into a classification network to classify the trajectories of HGVs. Then prediction networks such as LSTM [18], gated recurrent units (GRU), and bidirectional recurrent neural networks (Bi-RNNs) [19] are applied to the trajectory prediction problem of HGVs in the absence of a large set of models.…”

Section: Hgv Maneuver Intention Predictionmentioning

confidence: 99%

“…Moreover, the stacking layer mechanism can enhance the power of the LSTM to cope with more complex recognition and prediction problems of temporal correlation. In addition, using multilayer LSTM networks to predict the target maneuver type is also a current maneuver intention prediction method with strong adaptability and high prediction accuracy [18]. The LSTM network can be used to approximate the dynamic relationships between aerodynamic parameters and displacements by aerodynamic analysis of HGVs at variable Mach numbers and mean angles of attack.…”

Section: Hgv Maneuver Intention Predictionmentioning

confidence: 99%

“…To compare the capabilities of the networks, the number of neurons in each LSTM layer and GRU layer is set to 128, and the training parameters of the network remain the same as in Section 4.1.3. For specific details of the M-LSTM and DCBNN algorithms, please refer to [18,19]. As shown in Figure 6b, the training loss variation curves of the joint prediction network with M-LSTM and the DCBNN.…”

Section: Comparison Of Training Loss Of Different Networkmentioning

confidence: 99%

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Intention Prediction of a Hypersonic Glide Vehicle Using a Satellite Constellation Based on Deep Learning

Cheng

Wei

et al. 2022

Mathematics

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Tracking of hypersonic glide vehicles (HGVs) by a constellation tracking and observation system is an important part of the space-based early warning system. The uncertainty in the maneuver intentions of HGVs has a non-negligible impact on the tracking and observation process. The cooperative scheduling of multiple satellites in an environment of uncertainty in the maneuver intentions of HGVs is the main problem researched in this paper. For this problem, a satellite constellation tracking decision method that considers the HGVs’ maneuver intentions is proposed. This method is based on building an HGV maneuver intention model, developing a maneuver intention recognition and prediction algorithm, and designing a sensor-switching strategy to improve the local consensus-based bundle algorithm (LCBBA). Firstly, a recognizable maneuver intention model that can describe the maneuver types and directions of the HGVs in both the longitudinal and lateral directions was designed. Secondly, a maneuver intention recognition and prediction algorithm based on parallel, stacked long short-term memory neural networks (PSLSTM) was developed to obtain maneuver directions of the HGV. On the basis of that, a satellite constellation tracking decision method (referred to as SS-LCBBA in the following) considering the HGVs’ maneuver intentions was designed. Finally, the maneuver intention prediction capability of the PSLSTM network and two currently popular network structures: the multilayer LSTM (M-LSTM) and the dual-channel and bidirectional neural network (DCBNN) were tested for comparison. The simulation results show that the PSLSTM can recognize and predict the maneuver directions of HGVs with high accuracy. In the simulation of a satellite constellation tracking HGVs, the SS-LCBBA improved the cumulative tracking score compared to the LCBBA, the blackboard algorithm (BM), and the variable-center contract network algorithm (ICNP). Thus, it is concluded that SS-LCBBA has better adaptability to environments with uncertain intentions in solving multi-satellite collaborative scheduling problems.

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Section: Hgv Maneuver Intention Predictionmentioning

confidence: 99%

Section: Hgv Maneuver Intention Predictionmentioning

confidence: 99%

Section: Comparison Of Training Loss Of Different Networkmentioning

confidence: 99%

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Intention Prediction of a Hypersonic Glide Vehicle Using a Satellite Constellation Based on Deep Learning

Cheng

Wei

et al. 2022

Mathematics

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“…Moreover, closed-form recursion with a smoothing process needs to be derived for different filters respectively and the computation is generally intractable, leading to limitations in its applications. Deep neural networks have strong capability of fitting if there are sufficient training data [22], which is conducive to solving the problems of model mismatch and estimation delays in existing MM filtering algorithms for targets with complex maneuvering motions. In [23], a deep learning maneuvering target tracking algorithm was proposed.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion

Tian

Fang

et al. 2022

Remote Sensing

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The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.

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“…Multiple model algorithm is an effective method to solve the problem of model uncertainty. It is often used in the target tracking field to solve the problem of complex and uncertain target maneuvering during target tracking [31]- [34]. Since the dynamic deformation angle is mainly produced by the influence of ocean waves, the main frequency in its model is often unknown even changed within a certain range while the model structure has not changed.…”

Section: Introductionmentioning

confidence: 99%

A Deformation Measurement Algorithm Based on Adaptive Variable Parameter Multiple Model for Large Ships

Wang

Zhang

et al. 2021

IEEE Trans. Instrum. Meas.

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Accurate measurement of ship deformation angle is of great significance for building a unified space attitude reference and improving the combat performance of the entire ship. However, many problems existing in the actual sailing process restrict its measurement accuracy severely, such as the time delay between measurement equipment, the unknown characteristics of measurement noise and the unknown model parameters of the dynamic deformation. This paper proposes an adaptive variable parameter multiple model (AVPMM) algorithm that can simultaneously solve these problems. Firstly, a time delay compensation algorithm is derived to suppress the influence of time delay on measurement accuracy; then an adaptive Cubature Kalman filter (ACKF) is designed based on the idea of adaptive filtering to suppress the influence of unknown characteristics of measurement noise; and finally an AVPMM algorithm is designed by combining the above ideas to reduce the impact of unknown model parameters of dynamic deformation. The results of simulation and turntable experiment show that this algorithm has good measurement accuracy and the error is less than 0.15 ′ .

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Active switching multiple model method for tracking a noncooperative gliding flight vehicle

Cited by 8 publications

References 22 publications

Intention Prediction of a Hypersonic Glide Vehicle Using a Satellite Constellation Based on Deep Learning

Intention Prediction of a Hypersonic Glide Vehicle Using a Satellite Constellation Based on Deep Learning

Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion

A Deformation Measurement Algorithm Based on Adaptive Variable Parameter Multiple Model for Large Ships

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