Study on Maneuvering Target On-axis Tracking Algorithm of Modified Current Statistical Model

Zhao, Xiong; Fanying, Li; Zhang, Lei

doi:10.1051/matecconf/201816002008

Cited by 2 publications

(1 citation statement)

References 9 publications

(5 reference statements)

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“…The noise was generated utilizing a MATLAB signal processing toolbox function known as "ColoredNoise". In order to evaluate the improvement in filter performance using parameter self-learning modules, the selected were compared with various traditional Kalman filter models, including the Kalman filter along with the process models CA [32], CV [33], Singer [34], current statistical [35], and EM Kalman filter [16]. At the same time, we also compared the combination methods of neural networks and filtering models, including the experimental results of transformer-EF and LSTM-EF, which will be of great benefit to our research, as shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

Jin,

Chen,

et al. 2023

Sensors

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GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

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Section: Resultsmentioning

confidence: 99%

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

Jin,

Chen,

et al. 2023

Sensors

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GPS Tracking Based on Stacked-Serial LSTM Network

Jin,

Liu,

Kong

et al. 2024

Chin. j. inf. fusion

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Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed here does not need to model the motion characteristics and sensor characteristics. It can achieve high-performance tracking by learning historical data dynamics and sensor characteristics. Experimental results show that this method can effectively improve the trajectory estimation performance when the target motion is unknown and uncertain.

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Study on Maneuvering Target On-axis Tracking Algorithm of Modified Current Statistical Model

Cited by 2 publications

References 9 publications

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

GPS Tracking Based on Stacked-Serial LSTM Network

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