Robust underwater sensor network target tracking method

Abstract

The invention discloses a robust underwater sensor network target tracking method. The robust underwater sensor network target tracking method comprises the steps of (1) modeling the measurement biases of underwater sensor nodes and non-Gaussian random measurement noise by use of a normal inverse Gamma distribution model, (2) solving the updating process of the parameters of the normal inverse Gamma distribution model by use of variational Bayes approximation method, and (3) realizing the estimation on a target state and the measurement biases of the underwater sensor nodes by use of an extended kalman filter algorithm; the method has certain robustness to the non-Gaussian measurement noise. The robust underwater sensor network target tracking method takes the influence of the own measurement biases of the sensor nodes and the non-Gaussian random measurement noise caused by complex underwater environment on the target state estimation into account, and realizes robust and simultaneous estimation on the target state and the measurement biases by modeling the measurement biases and the non-Gaussian random measurement noise by use of the normal inverse Gamma distribution model.

Description

technical field [0001] The invention relates to underwater sensor network technology, in particular to a robust underwater sensor network target tracking method. Background technique [0002] Underwater Sensor Networks (UWSN) has broad application prospects in the fields of marine environment monitoring, submarine resource detection, disaster warning, auxiliary navigation, monitoring and tracking of intrusion targets, etc. In recent years, with the rise of the marine economy and the increasing emphasis on marine rights and interests in various countries, the underwater sensor network has become one of the ocean hot topics that scientific research institutions and scholars in various countries have paid close attention to. Target tracking is one of the underwater sensor networks. important application. [0003] There are mainly two types of errors in the measurement system of sensor nodes: random errors and systematic errors. In traditional wireless sensor network target tr...

AI Technical Summary

Problems solved by technology

However, there are few algorithms that can handle both random measurement noise and fixed measurement bias of the measurement system

Although Ozkan et al. proposed a filtering algorithm based on particle filter and normal-inverse Wishart distribution to simultaneously estimate the mean value of Gaussian measurement noise in the article "Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters" in 2013 and variance, but the rapid degradation of particles greatly limits the application scenarios of this algorithm

Images