Underwater target tracking method based on multivariable deflection Laplace distribution

Abstract

The invention discloses an underwater target tracking method based on multivariable deflection Laplace distribution modeling, and the method comprises the steps: collecting the motion state data of an underwater target through a sonar sensor, and obtaining the target position measurement information through a coordinate system conversion mode; the method comprises the following steps: establishing a motion model of an underwater target, determining a state-space equation of the underwater target, analyzing mathematical characteristics of underwater noise, and establishing a measurement model of the target under non-Gaussian noise; the method comprises the following steps: modeling non-Gaussian noise on the basis of multivariable skew lapace distribution, solving a mixed parameter, a shape parameter and a scale matrix of the noise under a variational Bayesian framework, iteratively updating a target state and a noise covariance matrix, iteratively updating an estimated target motion state by using Kalman filtering, and after the number of iterations is reached, calculating the target motion state according to the estimated target motion state. And outputting the estimated values of the underwater target position and speed and the estimated value of the covariance matrix. The method has better robustness and estimation precision, does not need to select a degree-of-freedom parameter, and can be better applied to target tracking.

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