Abstract
The dynamic positioning system of unmanned underwater vehicles (UUVs) is a complex and large-scale system mainly due to the nonlinear dynamics, uncertainty in model parameters, and external disturbances. With the aid of the bio-inspired computing (BIC) method, the designed three-dimensional (3D) spatial positioning system is used for enlarging communication constraints and increasing signal coordination processing. With the growing of measurement scales, the issue of the networked high-precision positioning has been developed rapidly. Then, an information fusion estimation approach is presented for the distributed networked systems with data random transmission time delays and lost and disordered packets. To reduce the communication burden, an adaptive signal selection scheme is employed to reorganize the measurement sequence, and the parameter uncertainties as well as cross-correlated noise are used to describe the uncertain disturbances. Moreover, a reoptimal weighted fusion state estimation is designed to alleviate the information redundancy and maintain higher measurement accuracy. An illustrative example obtained from the 3D spatial positioning system is given to validate the effectiveness of the proposed method.
Highlights
For the complex and large-scale systems in the field of energy, transportation, logistics, and so on, due to the emerging problems such as strongly nonlinear and highly coupling, they significantly challenge the current computational tools
Motivated by the above analysis, based on the principle of spatial positioning of linear charge-coupled devices (CCD), the information measured by the three-dimensional (3D) photoelectric sensor is considered to be influenced by the uncertain factors, such as transmission time delays and cross-correlation noise, and the accuracy of state estimation is improved
In order to improve the robustness for the state estimation, the robust finite-horizon filtering is transformed into Kalman prediction to reduce the computational complexity using the method of reorganizing and transforming the measurement sequence as well as the innovation sequence. e proposed weighted fusion approach of filtering error covariance is used for coordinating and exchanging the measurement information between two subsystems
Summary
For the complex and large-scale systems in the field of energy, transportation, logistics, and so on, due to the emerging problems such as strongly nonlinear and highly coupling, they significantly challenge the current computational tools. In the engineering of the ship navigation applications, due to the complex issue of transmission over networks and external disturbances, the dynamic spatial positioning is difficult with the requirements of high-precision positioning and growing measurement errors. Motivated by the above analysis, based on the principle of spatial positioning of linear charge-coupled devices (CCD), the information measured by the three-dimensional (3D) photoelectric sensor is considered to be influenced by the uncertain factors, such as transmission time delays and cross-correlation noise, and the accuracy of state estimation is improved. In order to improve the robustness for the state estimation, the robust finite-horizon filtering is transformed into Kalman prediction to reduce the computational complexity using the method of reorganizing and transforming the measurement sequence as well as the innovation sequence. A spatial point light source is projected into each CCD plane by a cylindrical optical mirror, which is straight line light intersecting with the CCD and is able to perceive its
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