Deep Learning Aided State Estimation for Guarded Semi-Markov Switching Systems With Soft Constraints

Fu, Qien; Lu, Kelin; Sun, Changyin

doi:10.1109/tsp.2023.3274937

Cited by 3 publications

(3 citation statements)

References 52 publications

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“…Drawing inspiration from the concepts of the MM algorithm and the utilization of deep learning as demonstrated in [18,41], this paper proposes the utilization of a maneuvering model estimation network to enhance the multi-model centroid tracking of UAV swarms. A noteworthy distinction from [18] lies in the derivation of maneuvering filtering, which exhibits significant differences based on the dynamics model of UAVs.…”

Section: Framework Of the Proposed Tracking Methodsmentioning

confidence: 99%

“…For switching systems, RNNs are combined with IMM methods to learn mode probabilities, which are used to estimate and predict trajectories and estimate human posture [40]. In combination with multi-model tracking, the long short-term memory (LSTM) network is used to replace the transition probability matrix to realize the function of model judgment [41]. Currently, such methods have not been applied to tracking a UAV swarm's maneuvering centroid.…”

Section: Introductionmentioning

confidence: 99%

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UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

Chen,

Liu,

et al. 2024

Electronics

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When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.

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Section: Framework Of the Proposed Tracking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

Chen,

Liu,

et al. 2024

Electronics

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“…The selection of inappropriate models can result in poor results, filter divergence, and overfitting. Moreover, the inclusion of a large number of models may lead to the formation of a system that is overly complicated without necessarily enhancing the accuracy of state features estimation [23]. When dealing with instances where the undersea target exhibits extreme, sudden, or erratic fluctuations in behavior, IMM Kalman filtering may encounter challenges in fast transitioning between models or precisely adjusting to these changes [24].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

Ali,

Bilal,

Alharbi

et al. 2024

Front. Phys.

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In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model’s robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise’s standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data.

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A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

Wang,

Wang

2024

Aerospace Science and Technology

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Deep Learning Aided State Estimation for Guarded Semi-Markov Switching Systems With Soft Constraints

Cited by 3 publications

References 52 publications

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

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