Bioinspired Underwater Navigation Using Polarization Patterns Within Snell’s Window

Aiming at the requirement of autonomous navigation capability of the underwater unmanned vehicle(UUV),a novel bionic method for underwater navigation based on polarization pattern within Snell’s window is proposed.Inspired by creatures,polarization navigation is a satellite-free navigation scheme and has great potential to be used in the water.However,because of the complex underwater environment,whether UUV polarization navigation can be realized is doubtful.To illustrate the feasibility of underwater polarization navigation,we firstly establish the model of under-water polarization patterns to prove the stability and predictability of the underwater polarization pattern within Snell’s window.Then,we carry out static and dynamic experiments of underwater heading determination based on developed polarization information detection equipment.Finally,we obtain underwater polarization patterns and conduct the tracking experiment at different water depths.The experimental results of the underwater polarization patterns are consistent with the simulation,which proves the correctness of the proposed model.At the water depth of 5 m,the average angle and position error of the tracking experiment are 14.3508°and 4.0812 m,respectively.It is illustrated that underwater polarization navigation is realizable and the precision can meet the real-time navigation requirements of UUV.This study promotes the improvement of underwater navigation ability and the development of marine equipment.

Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.

Solar-tracking methodology based on refraction-polarization in Snell's window for underwater navigation

Underwater navigation system is an indispensable part for autonomous underwater vehicles.Due to the indiscernibility of satellite signal,however,the underwater navigation problem is quite challenging,and a satellite-free navigation scheme should be looked for.Polarization navigation,inspired by insects’capability of autonomous homing and foraging,is an alternative solution to satellite navigation with great application potential.Underwater polarization provides an indirect sun compass to animals for orientation determination.However,it is difficult to apply terrestrial solar-tracking methodologies in underwater situations due to the refraction of polarized skylight at the air–water interface.To resolve this issue,an underwater solar-tracking algorithm is developed based on the underwater refraction-polarization pattern inside the Snell’s window.By employing Snell’s law and Fresnel refraction formula to decouple the refractive ray bending and polarization deflection,the celestial polarization pattern is obtained based on underwater measurement.To further improve the accuracy,the degree of polarization is employed as a weight factor for E-vector.A long-lasting underwater experiment was conducted to validate the effectiveness of the proposed approach,and the results showed the root-mean-square errors of solar zenith and azimuth employing this algorithm were 0.3°and 1.3°,respectively.Our experimental results show that the refraction-polarization pattern inside the Snell’s window exhibits immense potential to improve the solar-tracking accuracy for underwater navigation.

Kalman filter applied in underwater integrated navigation system

For the underwater integrated navigation system, information fusion is an important technology. This paper introduces the Kalman filter as the most useful information fusion technology, and then gives a summary of the Kalman filter applied in underwater integrated navigation system at present, and points out the further research directions in this field.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Integrated navigation for autonomous underwater vehicles in aquaculture: A review

Aquaculture is the world’s fastest growing sector within the food industry,supplying humans with over half their aquatic products.Water quality monitoring or cage inspection is an indispensable part in aquaculture and is usually done manually.Autonomous underwater vehicles(AUVs)are increasingly being used in aquaculture as technology advances and the cost reduction.Autonomous navigation is considered as a basic function of AUVs but is a challenging issue primarily due to the attenuated nature of electromagnetic waves in water and unstructured underwater environments.An inertial navigation system(INS)is usually selected as the core navigation equipment for AUV navigation because it never fails to measure.This paper reviews and surveys the latest advances in integrated navigation technologies for AUVs and provides a comprehensive reference for researchers who intend to apply AUVs to autonomous monitoring of aquaculture.Pure INS has difficulty obtaining long-range precision navigation due to the inherent error accumulation of inertial sensors over time;aiding inertial navigation systems with auxiliary sensors are common means to improve the navigation accuracy of an INS for AUVs.The survey is conducted according to different assisted navigation technologies for inertial navigation.Finally,the future challenges of the AUV navigation are also presented.

Underwater map-matching aided inertial navigation system based on multi-geophysical information

In order to achieve long-term covert precise navigation for an underwater vehicle,the shortcomings of various underwater navigation methods used are analyzed.Given the low navigation precision of underwater mapmatching aided inertial navigation based on singlegeophysical information,a model of an underwater mapmatching aided inertial navigation system based on multigeophysical information(gravity,topography and geomagnetism)is put forward,and the key technologies of map-matching based on multi-geophysical information are analyzed.Iterative closest contour point(ICCP)mapmatching algorithm and data fusion based on Dempster-Shafer(D-S)evidence theory are applied to navigation simulation.Simulation results show that accumulation of errors with increasing of time and distance are restrained and fusion of multi-map-matching is superior to any single-map-matching,which can effectively determine the best match of underwater vehicle position and improve the accuracy of underwater vehicle navigation.

Robust SLAM localization method based on improved variational Bayesian filtering

Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise,outliers,or unknown and time-varying noise statistical characteristics,a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed.First,the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm.Then,the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average.Finally,the system updates are iterated multiple times to further gradually correct the state estimation error.Furthermore,to observe features at different depths,a feature measurement model containing depth parameters is constructed.Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information,compared with the variational Bayesian adaptive SLAM method,the positioning accuracy of the proposed method is improved by 17.23%,20.46%,and 17.76%,which has better applicability and robustness to environmental disturbance.

Water track laser Doppler velocimeter[Invited]

A water track laser Doppler velocimeter(LDV)is developed with advantages of high update rate,high real-time performance,high concealment,light weight,and small dimensions.The water track LDV measures the advance velocity of the underwater vehicle with respect to the surrounding water.The experimental results show that the water track LDV has an accuracy of 96.4%when the moving velocity of the vehicle with respect to the ground exceeds 0.25 m/s.Thus,the water track LDV is promising in the application of underwater navigation to aid the strapdown inertial navigation system.