Abstract
At present, the tracking accuracy of underwater passive target tracking is often limited due to models that are overly simple, with low complexity, poor universality, and an inability to learn. In this paper, a cubature Kalman filter (CKF) algorithm based on a gated recurrent unit (GRU) network is proposed. The filter innovation, prediction error, and filter gain obtained from the CKF are used as the input to the GRU network, and the filter error value is used as the output to train the network. End-to-end online learning is carried out using the designed fully connected network, and the current state of the target is predicted. In this paper, a deep neural network based on the GRU architecture is used to convert the tracking prediction problem into a time series prediction problem in the field of artificial intelligence, and its strong fitting ability is used to resolve the uncertainty of the target motion. Simulation results show that an unmanned underwater vehicle (UUV) state estimation method based on the GRU filter proposed in this paper offers better accuracy and stability than the traditional state estimation method.
Highlights
Passive sonar is one of the main target detection methods for modern warships, submarines, unmanned underwater vehicles (UUVs), and so on [1]
Error, and filter gain obtained from the cubature Kalman filter (CKF) are used as the input to the gated recurrent unit (GRU) neural neural network, and the filter error value is used as the output to train the network
The GRU–CKF algorithm is proposed to solve the problem of low accuracy of target state estimation caused by the strong nonlinear relationship between the pure angle measurement and the state in underwater passive target tracking and highly maneuverable target motion
Summary
Passive sonar is one of the main target detection methods for modern warships, submarines, unmanned underwater vehicles (UUVs), and so on [1]. Unpredictable approximation errors are inevitable due to the integration, discretization, and linearization of continuous models To solve such problems, an adaptive covariance feedback framework has been proposed [21]. Simulation results showed that this algorithm can effectively reduce the amount of calculation and make the model set better match the motion state of the target, thereby improving the tracking accuracy. Many filtering algorithms and improved methods have been proposed, it is still challenging to obtain accurate state estimation results due to the complexity and strong coupling of UUV dynamics and the inaccuracy of passive sonar measurements. The authors of [50] proposed a recurrent neural network (RNN) algorithm that can accomplish multitarget tracking tasks such as prediction, data association, state updating, target initiation, and termination under a unified network structure. X-axis, andand thethe distance between thethe twotwo sensors is Lismeters
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
