Adaptive robust estimation method and system for parameters of unmanned surface vessel

An adaptive robust estimation method for parameters of an unmanned surface vessel, comprising the following steps:

• step 1:
• constructing an augmented state estimation problem considering an external disturbance for the unmanned surface vessel;
• augmented state vectors comprising a state vector of the unmanned surface vessel, a parameter vector of an unmanned surface vessel model, and an unknown input vector representing a modeling error of an unmanned surface vessel system and the external disturbance;
• step 2:
• depending on a real-time input vector and a measurement vector of the unmanned surface vessel system, employing an adaptive robust unscented Kalman filtering method having process noise covariance matrix constraints and based on the combination of a maximum cross entropy criterion and a minimum mean square error criterion to obtain model parameters of the unmanned surface vessel;
• the input vector comprising a control signal from a shipborne controller, and the measurement vector comprising data obtained by GPS and IMU measurement;
• a method for estimating the process noise covariance matrix comprising:
• firstly, employing an innovation-based adaptive method to estimate a noise covariance matrix k0;
• then, introducing covariance matrix constraints according to a known structure and dividing a process noise covariance matrix k to be estimated into a diagonal block without structure constraints and a diagonal block with structure constraints;
• and finally, employing KL divergence to construct and solve the following optimization problem:
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• wherein k represents the moment;
• k1,j1 represents the diagonal block without structure constraints;
• λkj2 represents a coefficient of the diagonal block k2,j2 with structure constraints;
• k2,j2=λkj2kc,j2, kc,j2 is a known structure;
• j1=1, . . . , n1, j2=1, . . . , n2, n1 and n2 represent the number of the diagonal block without structure constraints and the number of the diagonal block with structure constraints, respectively;
• p(x|k0) and p(x|k) represent probability density functions of a random variable X with the covariance matrix being k0 and k respectively and obeying zero mean Gaussian distribution.