Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable.The acoustic signal is a marvelous choice fo...Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable.The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability.However,the traditional Acoustic Positioning Systems(APS)are expensive and incapable of positioning with limited acoustic observations.Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed.The first one is the single acoustic-beacon Range-only Matching Aided Navigation(RMAN)method,which is inspired by matching navigation without reference maps and presented for the first time.The second is the improved single acoustic-beacon Virtual Long Baseline(VLBL)method,which considers the impact of indicated relative position increments on virtual beacon reconstruction.Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available,named mAB-RMAN and mAB-VLBL.The comprehensive simulations and field investigations were conducted.The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline,specifically,the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90%and 98%when using single and double acoustic-beacons,respectively.These proposed techniques will provide new perspectives for underwater positioning,navigation,and timing.展开更多
In some tracking applications,due to the sensor characteristic,only range measurements are available.If this is the case,due to the lack of full position measurements,the observability of Cartesian states(e.g.,positio...In some tracking applications,due to the sensor characteristic,only range measurements are available.If this is the case,due to the lack of full position measurements,the observability of Cartesian states(e.g.,position and velocity)are limited to particular cases.For general cases,the range measurements can be utilized by developing a state estimation algorithm in range-Doppler(R-D)plane to obtain accurate range and Doppler estimates.In this paper,a state estimation method based on the proper dynamic model in the R-D plane is proposed.The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model.Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements.One is derived based on the well-known two-point differencing method.The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance,resulting in a model-based method,which capitalizes the model information to yield better performance.Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.展开更多
基金funding was provided by Natural Science Foundation of China(Grant numbers 42004067,62373367,42176195)。
文摘Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable.The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability.However,the traditional Acoustic Positioning Systems(APS)are expensive and incapable of positioning with limited acoustic observations.Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed.The first one is the single acoustic-beacon Range-only Matching Aided Navigation(RMAN)method,which is inspired by matching navigation without reference maps and presented for the first time.The second is the improved single acoustic-beacon Virtual Long Baseline(VLBL)method,which considers the impact of indicated relative position increments on virtual beacon reconstruction.Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available,named mAB-RMAN and mAB-VLBL.The comprehensive simulations and field investigations were conducted.The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline,specifically,the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90%and 98%when using single and double acoustic-beacons,respectively.These proposed techniques will provide new perspectives for underwater positioning,navigation,and timing.
基金This work was supported by the National Natural Science Foundation of China(61671181,62101162).
文摘In some tracking applications,due to the sensor characteristic,only range measurements are available.If this is the case,due to the lack of full position measurements,the observability of Cartesian states(e.g.,position and velocity)are limited to particular cases.For general cases,the range measurements can be utilized by developing a state estimation algorithm in range-Doppler(R-D)plane to obtain accurate range and Doppler estimates.In this paper,a state estimation method based on the proper dynamic model in the R-D plane is proposed.The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model.Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements.One is derived based on the well-known two-point differencing method.The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance,resulting in a model-based method,which capitalizes the model information to yield better performance.Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.
