The long baseline （LBL） system is widely used to locate and track autonomous underwater vehicles （AUV） through acoustic communication. Three important issues are presented here in LBL system application with AUV. ...The long baseline （LBL） system is widely used to locate and track autonomous underwater vehicles （AUV） through acoustic communication. Three important issues are presented here in LBL system application with AUV. Those issues which regard the normal acoustic communication between LBL system and AUV are the depth of towed army, the length of beacon cable, and the effective area of the AUV. The first issue is the key of the LBL system, which ensures the normal communication between towed array and beacons. The second issue which impacts the normal communication from the AUV to beacons in available range should be considered after the first one has been settled. Then the last issue determines the safe work area of the AUV. The ordinary differential equations （ODE） algorithm of ray is deduced from Snell＇s law. The ODE algorithm is applied to obtain sound rays from sound source to receiver. These problems are solved by the judgment that whether rays pinging from a sound source arrives at a receiver. The sea trial shows that these methods have much validity and practicality.展开更多
An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equ...An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.展开更多
文摘The long baseline （LBL） system is widely used to locate and track autonomous underwater vehicles （AUV） through acoustic communication. Three important issues are presented here in LBL system application with AUV. Those issues which regard the normal acoustic communication between LBL system and AUV are the depth of towed army, the length of beacon cable, and the effective area of the AUV. The first issue is the key of the LBL system, which ensures the normal communication between towed array and beacons. The second issue which impacts the normal communication from the AUV to beacons in available range should be considered after the first one has been settled. Then the last issue determines the safe work area of the AUV. The ordinary differential equations （ODE） algorithm of ray is deduced from Snell＇s law. The ODE algorithm is applied to obtain sound rays from sound source to receiver. These problems are solved by the judgment that whether rays pinging from a sound source arrives at a receiver. The sea trial shows that these methods have much validity and practicality.
基金supported by the National Natural Science Foundation of China(41576103)
文摘An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.
