access icon free 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.

Inspec keywords: nonlinear systems; target tracking; recursive estimation; particle filtering (numerical methods); covariance analysis

Other keywords: system biases estimation; target state estimation; multiplatform target tracking system; global surveillance system performance; multiple-particle statistics; sensor measurements; nonGaussian property; multiplatform surveillance system; common coordinate system; dynamic system models; stochastic system bias; multiple model-particle filter framework; state covariance; state bias; parameter estimation; recursive joint estimation algorithm; manoeuvring target motion; nonlinear system

Subjects: Filtering methods in signal processing; Radar equipment, systems and applications; Interpolation and function approximation (numerical analysis); Other topics in statistics; Signal processing theory; Signal detection; Other topics in statistics; Interpolation and function approximation (numerical analysis)

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