Abstract: An intelligent course planning method and controller for unmanned surface vehicle are provided, an overall optimization from starting point to end point of course deflection angle is achieved based on an optimization principle of approximate dynamic programming; an optimization of minimization of the course deflection angle and a control signal is achieved based on a quadratic form cost function and a performance index by designing a virtual radial basis function neural network and a least square method, a target course expression is obtained, and a stability and a degree of convergence are ensured through a positive-definite constraint of the Hessian matrix of the performance index. Compared with the related art, an overshoot of course deflection and the control signal is reduced, an optimization of flight and steering energy consumption is achieved, and completely data-driven for intelligent course planning for unmanned surface vehicle and a high-accuracy feedback adjustment are achieved.

Abstract: An intelligent course planning method and controller for unmanned surface vehicle are provided, an overall optimization from starting point to end point of course deflection angle is achieved based on an optimization principle of approximate dynamic programming; an optimization of minimization of the course deflection angle and a control signal is achieved based on a quadratic form cost function and a performance index by designing a virtual radial basis function neural network and a least square method, a target course expression is obtained, and a stability and a degree of convergence are ensured through a positive-definite constraint of the Hessian matrix of the performance index. Compared with the related art, an overshoot of course deflection and the control signal is reduced, an optimization of flight and steering energy consumption is achieved, and completely data-driven for intelligent course planning for unmanned surface vehicle and a high- accuracy feedback adjustment are achieved.

Abstract: Methods and devices for correcting underwater photon displacement and for depth sounding with a single-photon Lidar are provided. The method includes: acquiring a pointing angle of a photon emitted by the single-photon Lidar, and coordinates of a water-surface photon signal and a water-bottom photon signal returned by the photon emitted by the single-photon Lidar; performing a sea wave fitting according to the water-surface photon signal to determine a sea wave model; determining an intersection of the photon and an air-water interface according to coordinates of any water-bottom photon, the pointing angle and the sea wave model; determining an underwater displacement error of the photon according to the intersection, the sea wave model and the pointing angle; and correcting the coordinates of the water-bottom photon according to the underwater displacement error. The invention performs sea wave modeling through water surface photon signal and determines intersection of the photon and water-air interface.

Abstract: An adaptive filtering method of photon counting Lidar for bathymetry is provided in this invention, which includes steps: step S1: adaptively acquiring parameters of elliptic filtering for water surface photon signals; step S2: determining a relationship between filter parameters and elevation of underwater photon signals, and obtaining parameters of the elliptic filtering for photon signal in water column; and step S3: filtering and fitting the water surface photon signals and the underwater photon signals to acquire continuous bathymetry results.

Abstract: A training method for multi-output land cover classification model and a classification method are provided. The training method includes: obtaining a training data; inputting the training data into an initial model based on deep belief nets for training to obtain a multi-output land cover classification model, wherein the initial model includes N level outputs, and the N level outputs include an output set at last network layer and (N?1) level output set at any (N?1) network layers from a first network layer to a penultimate network layer of the initial model; determining a total loss according to losses of the N level outputs; performing a backpropagation based on the total loss to adjust a parameter of the initial model, N being an integer greater than or equal to 2. The gradient is not easy to disappear during backpropagation of the model, which is beneficial to improve classification accuracy.

Abstract: An adaptive filtering method of photon counting Lidar for bathymetry is provided in this invention, which includes steps: step S1: adaptively acquiring parameters of elliptic filtering for water surface photon signals; step S2: determining a relationship between filter parameters and elevation of underwater photon signals, and obtaining parameters of the elliptic filtering for photon signal in water column; and step S3: filtering and fitting the water surface photon signals and the underwater photon signals to acquire continuous bathymetry results.

Abstract: Methods and devices for correcting underwater photon displacement and for depth sounding with a single-photon Lidar are provided. The method includes: acquiring a pointing angle of a photon emitted by the single-photon Lidar, and coordinates of a water-surface photon signal and a water-bottom photon signal returned by the photon emitted by the single-photon Lidar; performing a sea wave fitting according to the water-surface photon signal to determine a sea wave model; determining an intersection of the photon and an air-water interface according to coordinates of any water-bottom photon, the pointing angle and the sea wave model; determining an underwater displacement error of the photon according to the intersection, the sea wave model and the pointing angle; and correcting the coordinates of the water-bottom photon according to the underwater displacement error. The invention performs sea wave modeling through water surface photon signal and determines intersection of the photon and water-air interface.