Joint estimation of state and system biases in non-linear system

In multi-platform surveillance system, a prerequisite for successful fusion is the transformation of data from different platforms to a common coordinate system. However, some stochastic system biases arise during this transformation, and they seriously downgrade the global surveillance performance. Considering that the target state and the system biases are coupled and interactive, the authors present a new recursive joint estimation (RJE) algorithm for registering stochastic system biases and estimating target state. First, the relationship between system biases estimation and target state estimation is derived. Second, the RJE framework is introduced on the basis of the proposed relationship. Representing the different behavioural aspects of the motion of a maneuvering target is difficult to achieve with a single model in a multi-platform target tracking system. By accounting for the non-linear and/or non-Gaussian property of the dynamic system, they modify the interacting multiple model-particle filter framework to estimate parameters. This approach considers not only the influence of the system biases, but also the covariance of state on the basis of multiple-particle statistics. Simulation results reveal the superior performance of the proposed approach with respect to the traditional algorithm under the same conditions.